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Modelling and control of hybrid electric vehicles (A comprehensive review)
Abstract
The gradual decline in global oil reserves and presence of ever so stringent emissions rules around the world, have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing an HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability, without over-depleting the battery state of charge at the end of the defined driving cycle.
As a result, a number of power management strategies have been proposed in literature. This paper presents a comprehensive review of these literatures, focusing primarily on contributions in the aspect of parallel hybrid electric vehicle modelling and control. As part of this treatise, exploitable research gaps are also identified. This paper prides itself as a comprehensive reference for researchers in the field of hybrid electric vehicle development, control and optimisation.
... Z wykle silnik elektryczny zastępuje alternator, połączony z silnikiem spalinowym paskiem wielorowkowym. Technologia micro hybrid korzysta z akumulatorów kwasowo-ołowiowych, które są tanie i dobrze rozwinięte, co czyni tę technologię ekonomiczną i łatwą do wdrożenia [3][4][5][6][7][8]. ...
... Silnik reluktancyjny jest często wybierany ze względu na efektywność i możliwości sterowania. Nowoczesne technologie, jak dwusprzęgłowe przekładnie, umożliwiają implementację różnych strategii sterowania, co zapewnia optymalną równowagę pomiędzy momentem obrotowym a stanem naładowania baterii [3][4][5][6][7][8]. ...
... Napęd elektryczny pozwala na zeroemisyjną jazdę, szczególnie w warunkach miejskich. Systemy full hybrid monitorują stan naładowania akumulatorów, co zapewnia niezawodność, optymalną wydajność oraz większą oszczędność paliwa i jednocześnie niższą emisję CO2 [3][4][5][6][7][8]. ...
... Rising fuel prices and concerns about global warming have been the main drivers in the automotive industry towards hybrid electric vehicles, a development process towards advanced powertrains [4]. HEVs have the potential to reduce fuel consumption and emissions significantly, while meeting vehicle power demands and maintaining satisfactory vehicle performance [5][6][7]. HEVs combine the use of an engine and electric motors, resulting in better fuel efficiency and lower emissions, further reducing dependence on fossil fuels and environmental impact [8,9]. The power required by the vehicle at any time can be provided by one or a combination of these energy sources [10]. ...
... The control performance of HEVs is determined by the power distribution between multiple power sources via energy management systems [15,16]. Torque distribution and optimal hybrid driving mode selection are critical to achieving improved fuel economy and reduced emissions [6,[15][16][17]. Energy management system optimization for powersplit hybrid electric vehicle configurations has been increasingly attracting researchers' attentions because of its torque split ability based on different drive conditions to improve fuel economy and energy consumption by optimizing the usage of different power sources in the vehicle. ...
... Different driving cycles can be simulated using predefined map-based speed and slope profiles shown in Figure 9. UDDS is a standard driving cycle used in the EU to analyze fuel consumption and emissions in urban driving conditions and reflects typical urban driving conditions such as stop-and-go traffic, low speeds, and frequent turns [6,[51][52][53][54]. The JC08 driving cycle is a standardized test procedure used in Japan to evaluate the fuel consumption and exhaust emissions of gasoline passenger cars under urban driving conditions, which was developed to better suit Japanese driving conditions [55,56]. ...
Optimization studies for the energy management systems of hybrid electric powertrains have critical importance as an effective measure for vehicle manufacturers to reduce greenhouse gas emissions and fuel consumption due to increasingly stringent emission regulations in the automotive industry, strict fuel economy legislation, continuously rising oil prices, and increasing consumer awareness of global warming and environmental pollution. In this study, firstly, the mathematical model of the powertrain and the rule-based energy management system of the vehicle with a power-split hybrid electric vehicle configuration are developed in the Matlab/Simulink environment and verified with real test data from the vehicle dynamometer for the UDDS drive cycle. In this way, a realistic virtual test platform has been developed where the simulation results of the energy management systems based on discrete dynamic programming and Pontryagin’s minimum principle optimization can be used to train the artificial neural network-based energy management algorithms for hybrid electric vehicles. The average fuel consumption in relation to the break specific fuel consumption of the internal combustion engine and the total electrical energy consumption of the battery in relation to the operating efficiency of the electrical machines, obtained by comparing the simulation results at the initial battery charging conditions of the vehicle using different driving cycles, will be analyzed and the advantages of the different energy management techniques used will be evaluated.
... The optimization-based control strategy achieves energy distribution management by solving complex optimization problems, and the commonly applied optimization strategies are the dynamic programming strategy (DP), Pontryagin's minimum principle (PMP) and equivalent fuel consumption minimization strategies (ECMSs) [12][13][14][15]. DP is a typical offline optimization method that can obtain theoretically globally optimal control results [16]. However, DP is computationally intensive and requires predictive information about operating conditions, which makes it difficult to meet the needs of online applications for vehicles [17]. ...
... . m H2 = N M H2 nF I (16) where . m H2 is the hydrogen consumption rate; N is the number of fuel cell monomers; M H2 is the molar mass of hydrogen; LHV is the low heating value of hydrogen; and n is the number of electrons lost in the electrochemical reaction. ...
... ( 16) where ̇2 is the hydrogen consumption rate; is the number of fuel cell monomers 2 is the molar mass of hydrogen; is the low heating value of hydrogen; and is the number of electrons lost in the electrochemical reaction. The fuel cell output power is related to efficiency and hydrogen consumption as shown in Figure 3. ...
Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.
... When there is little need for energy, it operates as a generator; when more power is demanded, it acts as an electric motor. [23]. Figure3 shows the power train connection for Parallel-HEV. ...
... Dynamic programming involves working forward from the initial formulation of the problem to its ultimate resolution. This approach is often employed when the methodology can be divided into a series of sub-issues, where the solution to each sub-problem depends solely on the solutions to the subproblems that come earlier in the sequence [23]. ...
... However, it also encounters two limitations: the requirement to have complete information about the entire driving cycle beforehand, the challenge of dealing with a large number of variables, and also a significant workload caused by computation. Hence, the control solutions obtained from dynamic programming are primarily utilized as reference points for assessing other controllers or as building blocks for creating and enhancing alternative optimizationbased approaches [23]. ...
As the demand for electric vehicles (EVs) continues to surge, improvements to energy management systems (EMS) prove essential for improving their efficiency, performance, and sustainability. This paper covers the distinctive challenges in designing EMS for a range of electric vehicles, such as electrically powered automobiles, split drive cars, and P-HEVs. It also covers significant achievements and proposed solutions to these issues. The powertrain concept for series, parallel, series-parallel, and complex hybrid electric cars was also disclosed in this study. Much of this analysis is dedicated to investigating the various control strategies used in EMS for various electric vehicle types, which include global-optimization approaches, fuzzy rule based, and real-time optimization-oriented strategies. The study thoroughly evaluates the strengths and shortcomings of various electric vehicle strategies, offering valuable insights into their practical implementation and effectiveness across different EV models, such as BEVs, HEVs, and PHEVs.
... There's growing excitement about Optimization-Based Supervisory (OBS) in FCHEVs because it can achieve the best possible outcomes. These optimal results are computed within OBS by minimizing the objective function across the entire driving cycle (global optimization) or at each instantaneous sampling time (local optimization) [100]. The intended outcomes of these approaches can be measured using a objective function that encompasses considerations such as fuel economy, power source durability, economic costs, as well as additional goals [101]. ...
... Dynamic programming involves working forward from the initial formulation of the problem to its ultimate resolution. This method is frequently used when the methodology can be broken down into a number of individual issues, each of which has a solution that is exclusively dependent upon finding solutions to the sub-problems that came before it in the sequence [100]. One advantage of dynamic programming is its broad applicability to a range of systems, example include systems that are linear and those that are non-linear, as well as problems with and without constraints [73]. ...
Rising concerns about fuel costs, emissions, oil depletion, and energy security have propelled the search for alternative energy sources in transportation. Electric vehicles are a crucial development in this direction, and fuel cell technology is gaining traction for its versatility and potential benefits. Fuel cells have become increasingly attractive for automobile owing to their ease of use, quiet operation, superior efficiency, modular design, and reliance on clean hydrogen. However, challenges remain, including hydrogen storage, high costs, integration with power electronics, and cold-start capabilities, which continue to impede the widespread adoption of Fuel Cell Electric Vehicles (FCEVs). This study critically examines these key issues, explores various fuel cell technologies and drivetrain architectures, and provides a comparative analysis of different energy storage systems. Furthermore, the study delves into the classification and evaluation of energy management strategies (EMS) for FCEVs. The review ultimately aims to stimulate further research focused on reducing costs, extending fuel cell lifespan, enhancing hydrogen infrastructure, optimizing electronic interfaces, and refining EMS to pave the way for the future of FCEVs.
... The bidirectional power converter, a basic component of EVs, is where energy is transferred to or from the battery, as shown in Fig. 1. Alternatively, hybrid vehicles can be categorized as series HEV, parallel HEV, series-parallel HEV, complex HEV, or plug-in HEV (Kumar and Jain, 2014) based on their internal design, with the distinctions based on how energy flows from the energy storage sources (Alagarsamy and Moulik, 2018;Enang and Bannister, 2017). The power converter plays a crucial role in controlling the flow of electricity to the electric machine, ensuring efficient operation and optimal performance. ...
... Increasing the use of renewable energy, decreasing CO 2 emissions, (UNFCCC, 2014) and defining the smart grid technology concept (Fang et al., 2012) are driving the need for energy storage systems (ESSs) in power markets. When transmission lines are overloaded or interrupted due to high demand, ESS provides a reliable and adaptable backup power source that keeps the lights on and even improves the quality of the power being delivered to consumers. ...
... BOSCH Company developed similar concepts [15,[17][18][19][20][21]. The new generation of Audi Q6 e-tron (type F4) which will be produced starting in 2025 includes similar complex equipment with some peculiarities regarding the pedal force simulator unit, and the progressive control of the brake release by four very small two-way servovalves (Figure 30). ...
The paper contains a review of the main world achievements in the field of digital electrohydraulic automotive braking systems and a short presentation of some theoretical and experimental studies performed by the authors to assess the static and dynamic behavior of recent and new braking systems. Design, modeling, digital simulation and experimental identification are used to point out the degree of mechanical, electrical and hydraulic complexity of some new achievements in the field.
... Series HEVs can keep the ICE operating at the highest efficiency with the best fuel consumption (3.6 litres per kilometre for the Nissan E-Power HEV 47 ), but the electric motor needs to provide the maximum vehicle power 41 . This disadvantage can be addressed by using parallel HEVs, which have two power providers, providing higher flexibility by eliminating the need for a large traction motor 48 . Series-parallel HEVs incorporate the serial configuration into the design of parallel HEVs, thereby achieving the advantages of both serial and parallel HEVs. ...
Energy storage and management technologies are key in the deployment and operation of electric vehicles (EVs). To keep up with continuous innovations in energy storage technologies, it is necessary to develop corresponding management strategies. In this Review, we discuss technological advances in energy storage management. Energy storage management strategies, such as lifetime prognostics and fault detection, can reduce EV charging times while enhancing battery safety. Combining advanced sensor data with prediction algorithms can improve the efficiency of EVs, increasing their driving range, and encouraging uptake of the technology. Energy storage management also facilitates clean energy technologies like vehicle-to-grid energy storage, and EV battery recycling for grid storage of renewable electricity. We offer an overview of the technical challenges to solve and trends for better energy storage management of EVs.
(This is a read-only link to the full-text PDF: https://rdcu.be/d8xK1)
... Ketika pengemudi melepaskan pedal gas atau menginjak rem, motor listrik dapat berfungsi sebagai generator, mengubah gerakan kendaraan kembali menjadi energi listrik yang kemudian disimpan kembali dalam baterai. Hal ini dapat meningkatkan efisiensi dan menambah jarak tempuh (Enang dan Bannister, 2017). Controller juga memiliki peran dalam mengamankan operasi kendaraan. ...
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... According to high interaction between different technology packages system-level simulation should be implemented to overcome the complexity of powertrain design (Delgado et al. 2017). Developing a hybrid powertrain, system-level simulations enable the possibility to calculate vehicle fuel consumption and battery state of the charge for which are the main control strategy objectives (Enang and Bannister 2017). The aim of this research is to develop a tool which could give a good platform to scheme suitable structures for electric axle. ...
The electrification of heavy vehicles and work machinery is developing rapidly. The main motivators are green transition and requirements from the customers. In Finland, there are many hightech market-leading companies in this segment. Mass-produced equipment and machines are suitable for general applications and thus tailoring design for specific conditions and/or needs results in better productivity and efficiency. In heavy electric vehicle applications, the challenge is to make new products economically viable and configure them to meet customer needs. In these applications, the number of solutions is an order of magnitude higher than in traditional mechanical solutions. However, electronic solutions enable new features and energy efficiency improvements to have measurable benefits in the application. The research investigates the effects of electric axle solutions for hybrid heavy duty vehicles. Modelling and simulations consider both the effects of engine and usage of battery charge and surroundings of vehicle, for example road profile, traffic, outdoor temperature, and friction. A system level model of a vehicle has been utilized to simulate its longitudinal dynamics interacting with estimated surroundings followed by model-based control. The planned route can be made further favorable by utilizing real-time model predictive control (MPC) receiving online data from changing conditions. MPC gives new suggestions for optimal battery usage based on deviations from the best matching model from a database. Control strategy is important when considering economic benefits for a hybrid heavy duty vehicle with a high degree of freedom in system design.
... FLC employs "if-then" rules to establish relationships between inputs and outputs, enabling real-time control actions during system operation. 88,89 However, its ability to guarantee optimal solutions is constrained by the reliance on fuzzy rules derived from engineering expertise and practical experience. Additionally, manually adjusted parameters can lead to suboptimal or partial solutions. ...
Given the increasing demand for sustainable agricultural practices and energy conservation, advanced technologies for electric agricultural machinery (EAM) are critically needed. This paper provides a comprehensive review and analysis of powertrain systems and energy management strategies (EMSs) for electric tractors (ETs), a key representative of EAM. Specifically, this paper: (1) outlines the current development status and research significance of ET powertrains, including single-energy powertrains (SEPs), diesel-electric hybrid powertrains (DEHPs), and hybrid energy storage systems (HESSs); (2) offers an in-depth analysis of EMS approaches—covering rule-based, optimization-based, and learning-based strategies—and evaluates their performance in terms of energy efficiency, adaptability, and cost reduction; (3) identifies future research hotspots, such as intelligent data-driven EMSs, multi-source energy integration, and advanced energy optimization algorithms to improve the adaptability, efficiency, and reliability of ET power systems. The findings of this paper highlight the critical role of hybrid powertrains and advanced EMSs in enhancing the operational range, energy efficiency, and economic viability of ETs, offering insights and guidance for the further development of sustainable agricultural technologies.
... This not only speeds up the development process but also reduces costs and allows for a more extensive testing range than would be feasible with physical models alone (Adegbohun et al. 2021, Montaleza et al. 2022, Genikomasakis and Mitrentsis 2017. Furthermore, vehicle modeling facilitates the refinement of control systems, ensuring that they operate efficiently and reliably in real-world scenarios, thereby enhancing vehicle safety and performance (Adegbohun et al. 2021, Enang and Bannister 2017, Krasopoulus et al. 2017. One effective method for modeling a vehicle and extracting its parameters is through a coast-down test. ...
In the realm of electric vehicle design and development, critical vehicle parameters are estimated by coast down test. However, other drag factors can introduce errors to the estimation, including inertial and friction of rotating parts. Additionally, certain parameters are challenging to measure directly, such as drag coefficient, driveline losses and the frontal area of vehicles like go-karts. To address this challenge, a method of model-based parameter estimation is proposed in this research. An electric vehicle and powertrain model of a go-kart are developed, incorporating uncertain parameters like rolling resistance coefficient and air density. The target vehicle undergoes testing, with vehicle speed and motor torque recorded concurrently. Subsequently, the MATLAB/Simulink parameter estimation tool is applied to fine-tune the uncertain parameters, aligning the simulated vehicle speed and torque with the recorded test data. The optimization reduces speed and torque error between simulation values and test data by 24.76% and 15.58% respectively comparing to coast down model. Once optimized, the model is validated through simulation using another set of recorded drive cycle data reduces speed and torque error by 48.46% and 30.63% respectively comparing to coast down model. With a validated model boasting high confidence, design optimization tools are utilized to enhance the traction motor's pulley ratio. The optimized pulley ratio leads to a 33.80% improvement in energy efficiency, thereby enhancing the overall performance of the electric go-kart.
... [2], [15], [16], [30], [31], [32], [33], [34] Differential Evolution (DE) Simplicity and ease of implementation. Global search capability. ...
As air pollution, greenhouse gases, and global warming worsen, finding clean energy sources is critical. Renewable energy is a promising solution, especially in the transportation sector, which consumes significant energy. Hybrid electric vehicles (HEVs), combining an internal combustion engine and an electric battery, are key to reducing fossil fuel use and mitigating environmental harm. Effectively managing power distribution between these sources to enhance efficiency and minimize fuel consumption is crucial, known as an Energy Management Strategy (EMS). This article provides an overview of various EMS approaches for HEVs, analyzing their advantages and disadvantages. Rule-based strategies offer simplicity, optimization-based strategies provide superior performance, and advanced techniques like machine learning promise significant improvements. Current trends include integrating sophisticated sensors, data analytics, and artificial intelligence for real-time decision-making. Future directions aim at robust EMS frameworks integrating smart grid technologies and vehicle-to-everything (V2X) communication. The article reviews EMS methodologies, comparing their strengths and weaknesses, and discusses the main challenges and future trends in energy management for hybrid electric vehicles.
... Furthermore, engine downsizing leads to reduction in friction, heat transfer across cylinder walls, and pumping losses, which are all beneficial for improving fuel economy [4]. Additionally, an energy storage system engaged in the powertrain can compensate for the loss of engine power due to reduced engine capacity, assist the engine to be operated at higher efficiency region, and store regenerative energy when available [5]. Successful applications of hybrid powertrain have been realized in various machines, such as passenger cars [6], gantry cranes [7], and ships [8]. ...
The paper addresses the critical need for a robust testing methodology to assess control performances of off-road vehicles with electrified powertrains due to the challenges posed by stringent emission regulations. The main contribution of this paper lies in the development of an innovative real-time hardware-in-the-loop (HIL) simulation platform tailored for a next-generation battery hybrid electric wheel loader proposed for John Deere. This platform integrates a unique vehicle power management (VPM) strategy, component-level controllers, and physics-based powertrain component models to simulate vehicle operations. Through this new HIL simulation platform, the paper demonstrates the potential for achieving an over 10% reduction in fuel consumption with the proposed next-generation battery hybrid electric wheel loader. The effectiveness of the platform is further validated through in-field testing, aligning the fuel efficiency capabilities and VPM strategy performance with the simulated results in the HIL environment.
... A review study about the EMS in hybrid vehicles has been done by some researchers such as [28] and [29]. However, both of these do not focus on specific EMS methods but discuss a lot of EMS used in hybrid vehicles. ...
The transportation sector, a significant contributor to carbon dioxide emissions as of 2020, confronts a pressing challenge in mitigating pollution. Electric Vehicles (EVs) present a promising solution, offering a cleaner alternative; however, their limited travel range poses a constraint. Hybrid Electric Vehicles (HEVs) and Hybrid Energy Storage System Electric Vehicles (HESS EVs) emerge as economically feasible compromises. Nonetheless, the effective management of energy and the optimization of power source size remain crucial challenges for both HEVs and HESS EVs. Among various Energy Management Strategies (EMS), the Fuzzy Logic Controller (FLC) stands out for its performance, simplicity, and real-time applicability. This article comprehensively explores the diverse applications of FLC as an EMS in both HEVs and HESS EVs, providing a comparative analysis with other EMS methods and delving into the advantages and challenges associated with each approach. A detailed examination of various FLC types employed as EMS has been conducted, drawing insights from a multitude of references. Each class of FLC EMS is scrutinized, presenting a broad overview of proposed methodologies within each category. By providing this comprehensive information, the article equips readers with foundational knowledge and insights for the continued development of FLC EMS in hybrid electric and hybrid energy storage system electric vehicles.
... The power source of the electric tractor driven by two motors is the main motor and the auxiliary motor. The two motors are connected through the coupling device to realize the dynamic coupling of the two motors, and then the power is transmitted to the driving wheel through the components such as the gearbox and the differential (Hu 2018;Enang and Bannister, 2017), as shown in Fig. 3, and the main parameters are shown in Table 1. ...
At present, electric tractors experience significant battery energy loss during operation, resulting in a short continuous running time. Therefore, in order to reduce the power consumption of the tractor drive system, minimize battery energy loss, and extend the operating time under various conditions, this paper presents a method for driving an electric tractor based on dual-motor coupling. Based on the characteristics of the transmission structure, an online torque distribution strategy for dual-motor coupling-driven electric tractors using a fuzzy control approach is proposed. First, an enhanced genetic algorithm is utilized to optimize the fuzzy rule table. Simultaneously, it is compared with the offline optimization strategy of dynamic programming. Subsequently, a method that integrates test data models and theoretical models is employed to establish an efficiency model of key components of the electric tractor drive system and a longitudinal dynamics model of the entire machine. The performance of the entire vehicle was simulated and analyzed under plowing conditions. Finally, on the experimental bench, conduct steady-state load tests and dynamic performance tests on the dual-motor coupled drive system. The results show that the State of Charge (SOC) change trends of the fuzzy control strategy based on the improved genetic algorithm and the dynamic programming strategy are similar. The SOC change values are close, which enhances the adaptability of the electric tractor in various operating conditions. Compared with the fuzzy control strategy, the improved strategy reduced average power consumption by 8.8%, demonstrating that the fuzzy control energy management strategy based on the enhanced genetic algorithm is both economical and superior. The bench experiment demonstrated that the dual-motor drive system can adapt to load changes to achieve power distribution between the two motors, meeting the required workload while reducing power consumption.
... Instead, they rely on a predefined set of rules to determine the appropriate value of the control to be applied at each time step. Rules are typically formulated using heuristics [30], intuition, or derived from the knowledge of the best global solutions obtained using mathematical models and optimization techniques [31][32][33]. ...
The developing environmental effects caused by automobiles are increasingly becoming a pressing social concern. In order to address these challenges and prevent the adoption of less desirable alternative technologies, the automobile industry must implement and introduce Hybrid Electrical Vehicles (HEVs) and Electrical Vehicles (EVs). EVs enable us to achieve a completely clean service with a 100% cleanliness rating. However, it is constrained by infrastructure limits and faces challenges related to its limited driving range. In order to surmount this challenge, we require the implementation of a hybrid system. A HEV is an aesthetically pleasing alternative to the conventional Internal Combustion (IC) engine-powered vehicle system, effectively mitigating the issues arising from emissions. It offers an effective option for addressing infrastructure limitations and reducing operating expenses. HEV, short for hybrid electrical car, is a fusion of an internal combustion engine car and an electrical vehicle. While internal combustion engine vehicles are powered by fuel, electrical vehicles are propelled by an electric motor. In a HEV, the Electrical Motor (EM) is linked to a rechargeable battery pack, enabling electrical motor propulsion. Simultaneously, a HEV utilizes both engines to enhance power and torque, or alternatively, relies on either one depending on the driving conditions. This paper gives a review on hybrid electrical vehicles and explains architectures, classification and energy management.
... The primary control goals of most HEV control strategies are optimizing fuel consumption and tailpipe emission without compromising the vehicle performance attributes and the auxiliary source as a supercapacitor SoC. 80 Energy management strategies (EMS) have a significant impact on HEVs' fuel efficiency. It is mainly utilized for splitting power between two sources. ...
Continuous efforts to preserve the environment and to reduce gaseous emissions due to the massive growth of urban economic development and heightened concerns over crude oil depletion have accelerated researchers to find long‐term solutions, particularly in the transportation sector with the focus on powertrain electrification. This article delivers a comprehensive overview of electric vehicle architectures, energy storage systems, and motor traction power. Subsequently, it emphasizes different charge equalization methodologies of the energy storage system. This work's contribution can be identified in two points: first, providing an overview of different energy management methods to researchers and scholars. Second, to highlight the state‐of‐the‐art leanings in major components and to highlight promising approaches to hybrid electric vehicle future development.
With the accelerating depletion of fossil fuels and growing severity of air pollution, hybrid electric powertrain systems have become a research hotspot in transportation, owing to their ability to improve fuel economy and reduce emissions. However, optimizing the control of these systems is challenging, as it involves multi-power source coordination, dynamic operating condition adaptation, and real-time energy distribution. Traditional control methods, whether rule-based or optimization-based, often lack global optimality and adaptability. In recent years, artificial intelligence algorithms have provided new solutions for the intelligent control of hybrid electric powertrain systems with their powerful nonlinear modeling capabilities, data-driven optimization, and adaptive learning capabilities. This paper systematically reviews the research progress of artificial intelligence algorithms in hybrid electric powertrain systems. First, the architecture classification of hybrid electric powertrain systems is introduced. Secondly, the advantages and disadvantages of rule-based and optimization-based energy management strategies are summarized. Then, the existing research on the application of artificial intelligence algorithms in hybrid electric powertrain systems is systematically reviewed, and the advantages, disadvantages, and specific applications of various algorithms are analyzed in detail. Finally, the future application direction of artificial intelligence algorithms in hybrid electric powertrain systems is prospected.
div class="section abstract"> The hybrid electric drive system has the potential to make a significant contribution to the energy sustainability of the automotive industry. This paper investigates the improved adaptive equivalent consumption minimization strategy (A-ECMS) for a multi-mode series-parallel hybrid electric vehicle. First, a basic ECMS algorithm for the series-parallel vehicle is established, which considers the instantaneous optimal torque matching in the electric, serial hybrid, and engine driving modes. Under the condition that the future traffic information scenario is known, it is desired to realize the global optimal planning based on the combination of dynamic programming (DP) and ECMS. The SOC, engine speed, and torque results calculated by the DP strategy are used as benchmarks to develop the improved SOC-AECMS and S-AECMS strategies, which better incorporate the advantages of the global optimization results. Finally, a hardware-in-the-loop simulation platform is set up to validate the real-time application of the proposed algorithms. A comparative analysis of the engine operating point distribution and fuel consumption levels is performed. The results show that the basic A-ECMS is adaptable to different driving cycles, but has limited fuel saving effects. Both the optimized SOC-AECMS and S-AECMS can effectively improve fuel economy while avoiding the computational burden of DP algorithms. The S-AECMS approaches 91.6% of the total fuel consumption level of the globally optimized DP, which provides a promising method for integrating the driving scenario information with the advanced energy management techniques.
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div class="section abstract"> In modern vehicles, effective thermal management is crucial for regulating temperatures across various components and sub-systems, ensuring optimal performance, efficiency, safety, and passenger comfort. As the industry shifts towards reducing carbon emissions, powertrain electrification - encompassing electric and hybrid vehicles - has emerged as a prominent trend. This transition introduces greater complexities, as the powertrain system must now precisely control the temperatures of not only traditional components but also batteries, power electronics, and motors.
Typically, the performance of vehicle-level thermal management systems is fully evaluated only after physical prototypes are developed and tested, particularly during summer and winter road trials. Conducting development and validation at such a late stage in the development process significantly increases both development risks and costs.
To address these challenges, a comprehensive vehicle-level thermal management simulation platform has been developed for a plug-in hybrid vehicle (PHEV). This platform integrates all components and subsystems of the thermal management system, including full control of the thermal system. Using this platform, vehicle-level simulations under various driving conditions in real-world scenarios can be performed, enabling the evaluation of vehicle technical specifications during the early development phase. Furthermore, individual components, subsystems, and control strategies can be designed and optimized through virtual assessments.
This paper outlines the development of a vehicle-level comprehensive simulation platform, detailing various component-level and system-level models, as well as control and calibration methodologies. Validations were conducted under the summer road test condition and the WLTC test at an ambient temperature of 38°C. The platform's capabilities are demonstrated through application examples in PHEV thermal management development, including optimization of compressor operations for minimizing energy consumption, evaluation of traction motor temperature rise during consecutive uphill starts, and analysis of direct cooling designs for battery packs using refrigerants.
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div class="section abstract"> The impact and vibration problem during gear shifting and mode switching of the P2 hybrid 8AT system of new energy vehicles seriously affects driving comfort. This paper proposed a collaborative clutch slip and friction control strategy for a P2 hybrid power system with power downshifting and engine starting to reduce transient shock vibration during the power system operation. A dynamic model of the P2 hybrid system was established, including a physical model of the engine, motor, clutch, 8AT transmission mechanism, and driving resistance. The transient dynamic behavior of the P2 hybrid system with power downshifting and engine starting was systematically studied. On this basis, with the goal of consistent power response and smooth gear shifting, a multi-stage collaborative control strategy including the motor, engine, and clutch under the power downshifting condition was formulated. Model-in-loop simulation verification was carried out based on MATLAB/Simulink platform. The simulation results show that compared with traditional methods, the proposed control method can effectively improve the power performance and comfort of the P2 hybrid power system.
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This comprehensive review explores advanced control strategies for hybrid electric vehicles (HEVs), focusing on the application of artificial intelligence (AI) and machine learning (ML) techniques. The chapter traces the evolution of HEV control strategies from conventional methods to more sophisticated approaches, such as fuzzy logic, neural networks, model predictive control, and adaptive control. The integration of AI and ML, including reinforcement learning, deep learning, and hybrid AI approaches, is discussed in detail. A comparative analysis of these strategies evaluates their performance in terms of fuel efficiency, emissions reduction, computational complexity, and adaptability. Case studies highlight real-world applications and benefits. Future directions and research opportunities, including edge computing, IoT integration, and advanced sensors, are explored. This review underscores the importance of adopting advanced control strategies to optimize HEV performance, efficiency, and reliability, contributing to a sustainable and energy-efficient future.
The work examines the state of urban electric transport in Bulgaria, the available vehicles, volume of transport work and the European policy in the field of bus electromobility. The planning process for public urban transport operators transitioning to an electric bus transport fleet is examined, with an overview of vehicle types and charging devices. External costs should be taken into account when operating a mixed bus fleet. Through detailed simulations that calculate different scenarios, the transport operator can make informed decisions about the interworking of the mixed fleet. A multi-criteria linear optimization model with integer variables has been developed for optimal allocation of vehicles at minimum values of costs and harmful emissions along transport routes. This model has been approved for the city of Ruse with a developed Matlab program. The possibilities for the application of intelligent mobility to improve the processes of planning and operation of vehicles have been assessed and a real case study of an integrated intelligent system built in the city of Ruse is indicated to monitor and control vehicles and passengers. The system will serve 392,098 trips per year, 25 routes and 268 stops of urban passenger transport in Ruse.
Electric vehicle Charging stations are essential for the growing number of EVs, requiring efficient integration of renewable sources, battery storage, and grid connections to enhance sustainability. However, connecting these components to a DC bus can cause DC bus voltage instability due to fluctuating power sources and varying load demands. Starting with the design of a second-order disturbance observer (DOB) aimed at estimating and counteract matched and mismatched disturbances. An adaptive sliding mode control (SMC) is then introduced. This enables the creation of a DOB-based adaptive SMC scheme, which manages the overall disturbances of the system. To address the chattering issue, a barrier function based SMC (BFSMC) approach is proposed. The distinctive aspects of this BFSM control approach is its capability to stabilize the designed sliding variable to a predefined neighbourhood around the origin within a limited duration, eliminating the need for prior understanding of the maximum limit of the lumped disturbance. The stability of the proposed control system is determined using the Lyapunov candidate function. The robustness of the controller is validated by comparing it to traditional control strategies like PID, Lyapunov and sliding mode controllers. The proposed method demonstrates superior performance in regulating DC bus voltage.
Electrified propulsion systems are a promising way of reducing traffic-related pollution. Because of the characteristics of the exhaust systems of engine-assisted vehicles, it is possible that pedestrians in close proximity to vehicles may encounter situations with high enough concentrations of emissions to cause specific health effects. To decrease the impact of vehicle emissions and pollutants on surrounding pedestrians, this chapter presents a cyber-physical optimisation technique for the pedestrian-aware supervisory control strategy of hybrid propulsion systems. The technique is a combination of the Bees Algorithm and a fuzzy adaptive cost map to optimise the rule-based power-split parameters. It is capable of self-adjusting the intertarget weights of exhaust emissions and fuel with real-time pedestrian density information during the optimisations. To examine the robustness of the hybrid propulsion system optimised by the introduced technique, the effects of communication quality and bootstrap sampling techniques are studied. The results show that applying the developed fuzzy adaptive cost map can reduce emissions to nearby pedestrians by 14.42%.
Fuel consumption is a serious environmental issue. Hybrid electric vehicles “HEVs” are highly energy efficient and reduce emissions. Parallel Hybrid Electric Vehicles “PHEVs” are currently the most popular architecture type in the HEV market. A PHEV manages energy between its components based on a proposed fuzzy logic control strategy. This strategy has been well researched and proven to be effective. PHEV control strategy has several simultaneous objectives. Reducing fuel consumption is the main goal of a PHEV while maintaining driving performance. Due to the complex nature of PHEVs, optimization algorithms must be used as the applied control strategies do not yield satisfactory global system efficiency. Several constraints were added as boundary conditions in the optimization process to allow optimized sizing of components not only to achieve goals, but also to meet specific vehicle performance requirements. In an acceleration test, the PHEV must reach a certain speed within a given time. In addition, in terms of reducing fuel consumption and emissions, the PHEV must have a zero energy balance, between the start and end of the cycle, at the battery level. PHEV optimization involves several decision variables, including key powertrain components and energy management strategy “EMS” system parameters. These variables greatly affect vehicle performance. A comparative study of optimization algorithms was conducted to determine the characteristics of PHEV to improve fuel economy. The particle swarm optimization “PSO” algorithm and the divided rectangular “DIRECT” algorithm are applied to the studied PHEV to solve optimization problems specific to fuel consumption. Conducted studies prove the effectiveness of the PSO algorithm compared to the DIRECT algorithm in terms of fuel economy improvement. This research has made it possible to determine the optimal size parameters for components that will make our PHEV more efficient and less polluting.
Over the past decade, the world has experienced a remarkable shift in the automotive landscape, as electric vehicles (EVs) have appeared as a viable and increasingly popular alternative to the long-standing dominance of internal combustion engine (ICE) vehicles and their ability to absorb the surplus of electricity generated from renewable sources. This paper presents a detailed examination of the different categories of EVs, charging methods and explores energy generation systems tailored for EVs. As vehicle complexity and road congestion increase with the growth of EVs, the need for intelligent transport systems to improve road safety and efficiency becomes imperative. Machine learning (ML), recognized as a powerful approach for adaptive and predictive system development, has gained importance in the vehicle domain. By employing a variety of algorithms, ML effectively addresses pressing issues related to electric vehicles, including battery management, range optimization, and energy consumption. This paper conducts a brief review of ML methods, including both traditional and applied approaches, to address energy consumption issues in EVs, such as range estimation and prediction, as well as range optimization.
Energy management is a key factor affecting the efficient distribution and utilization of energy for on-board composite energy storage system. For the composite energy storage system consisting of lithium battery and flywheel, in order to fully utilize the high-power response advantage of flywheel battery, first of all, the decoupling design of the high- and low-frequency components of the power required by vehicle is carried out based on Haar wavelet algorithm. Then, to solve the problem that the Haar wavelet is unable to adapt to the random and complex vehicle operation, caused by the design using fixed decomposition layer, support vector machine (SVM) is applied to construct the identification model for the time-varying vehicle operation. Moreover, to maintain the state of energy (SOE) of flywheel within the efficient range for adjusting the lithium battery operation, a fuzzy controller is designed to redistribute the power from Haar wavelet. Finally, the economic performance of the composite energy storage system under WLTC is tested and analyzed. Results show that, under WLTC condition, compared with the energy management strategy using Haar wavelet with fixed decomposition layer, the proposed adaptive wavelet–fuzzy energy management is able to reduce the power fluctuation of lithium battery by 26.6% and increase the average efficiency by 1.2%. Furthermore, in comparison with the original scheme without using flywheel battery, the power fluctuation of lithium battery is reduced by 37.3% and the average efficiency is increased by 4.3%. The designed energy management is able to make full use of the high-power response advantage of flywheel battery.
Electric vehicles (EVs) are crucial for long-term resource management, combating climate change, and enhancing energy efficiency. They significantly reduce harmful emissions, addressing challenges like greenhouse gas escalation and fossil fuel depletion. Exploring EVs involves analyzing their ecological footprint, energy efficiency, and addressing challenges like range anxiety and charging infrastructure. Beyond environmental benefits, EVs offer reduced emissions and potential integration with renewables, promoting overall sustainability. Advances in battery technology and EV design address concerns, ensuring continuous evolution. The chapter expands to social and economic implications, including job creation, lowered healthcare costs, and improved air quality. Global policy initiatives and case studies highlight growing global acceptance, emphasizing EVs' transformative potential in reshaping transportation for environmental sustainability and global energy conservation.
Power distribution networks are swiftly transforming into active distribution networks (ADNs) due to increasing penetration of renewables and electric vehicles. This raises concerns among distribution system operators regarding the power quality issues. Frequent over/under voltages in ADNs are some of the major issues. The advanced devices and control techniques are to be used to prevent system voltage violations to enable efficient and reliable operation. To enhance the insight and comprehension, a precise description regarding the state-of-the-art devices used for voltage control in ADNs, their models and characteristics are provided in this chapter. Various advanced control strategies used for voltage control under different operation scenarios to highly uncertain future power networks are systematically presented, along with their merits and limitations. Finally, a voltage control approach based on dual-stage model predictive control framework is presented and tested on modified 33-bus distribution network to study the voltage control capabilities of PVs and EV charging stations.
div class="section abstract"> This paper presents the characteristics of more than 260 trim levels for over 50 production electric vehicle (EV) models on the market since 2014. Data analysis shows a clear trend of all-wheel-drive (AWD) powertrains being increasingly offered on the market from original equipment manufacturers (OEMs). The latest data from the U.S. Environmental Protection Agency (EPA) shows that AWD EVs have seen a nearly 4 times increase in production from 21 models in 2020 to 79 models in 2023. Meanwhile single axle front-wheel-drive (FWD) and rear-wheel-drive (RWD) drivetrains have seen small to moderate increases over the same period, going from 9 to 11 models and from 5 to 12 models, respectively. Further looking into AWD architectures demonstrates dual electric machine (EM) powertrains using different EM types on each axle remain a small portion of the dual-motor AWD category. However, these architectures have been shown to have energy savings of 1 % to 5 % over that of identical dual-motor permanent magnet (PM) machine or dual-motor induction machine (IM) architectures. Further work shows dual motor architectures with an IM powering the front axle and a PM machine powering the rear axle under mathematical optimization-based controls to be less energy consuming than the same architecture subjected to a rule-based energy management strategy (EMS). This leads to a review of electrified vehicle EMSs, with the two main methods of rule-based and optimization-based controls being presented. The pros and cons of each control method are stated with optimization-based methods showing the most benefit. The optimal control method of model predictive control (MPC) is then presented by covering its’ background, structure, variations, and mechanics. Finally, the use of MPC as a viable EMS for multi-motor EVs is reviewed with motor thermal regulation as part of the control objective.
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This research paper presents a novel approach to sustainable transportation by proposing a hybrid vehicle that integrates a compressed natural gas (CNG) engine with a battery-powered plug-in electric vehicle (BEV). The hybrid system incorporates in-wheel motors for enhanced efficiency and maneuverability, coupled with a rooftop solar panel system to supplement energy supply. The integration of CNG and electric power addresses range anxiety associated with electric vehicles, reduces emissions, and promotes cleaner air quality. In-wheel motors optimize torque control and regenerative braking, while the rooftop solar panels extend the vehicle's driving range, reducing dependence on the grid. This comprehensive approach offers a promising solution for sustainable transportation, aligning with environmental goals and advancing clean energy technologies in the automotive sector
The demand for gasoline in Canada, especially by light-duty vehicles, continues to increase with economic growth and development. Fossil fuel driven vehicles are not only creating financial strain due to fluctuating gas prices but are also polluting the environment and posing health risks to the community. In an effort to promote public awareness, this paper reviews hybrid vehicle technology as a logical step towards sustainable, efficient and environment friendly transportation and discusses the measures taken by the Canadian government to encourage hybrid vehicle sales and to minimize fossil fuel dependency of the transportation sector.
In this study, Pontryagin's minimum principle (PMP) is applied to obtain the control law of plug-in hybrid vehicles. The results show that the minimization of the equivalent fuel consumption with a pre-defined weighting coefficient, which is a costate of PMP, is an effective way to obtain a control strategy that minimizes the overall energy. Dynamic programming yields results that are close to being global optimal. To realize the control algorithm solved by PMP, we introduce an adaptive concept that is based on driving patterns, and we conclude that an instantaneous optimal strategy with a properly selected costate is sufficiently simple for application to a real-time controller and is a desirable strategy that yields satisfactory results.
The pursuit of high fuel efficiency and low emissions has inspired a lot of research efforts on automotive powertrain hybridization. Targeted at developing a real-time hybrid energy management strategy, a stochastic dynamic programming - extremum seeking (SDP-ES) optimization algorithm with both the system states and output feedback is investigated in this paper. This SDP-ES algorithm utilizes a state-feedback control, which is offline generated by the stochastic dynamic programming (SDP), as a reference term to ensure the approximate global energy optimality and battery state-of-charge (SOC) sustainability. And in real-time, this algorithm injects a “local” feedback term via extremum seeking (ES), which is a non-model-based nonlinear optimization method, to compensate the control commands from the SDP and generate more fuel-efficient operation points along the specific SOC sustaining line, by leveraging the real-time measurement of system outputs (fuel consumption and emissions). The simulation results show the SDP-ES algorithm can provide desirable improvement of fuel economy based on the original SDP.
Living in the era of rising environmental sensibility and increasing gasoline prices, the development of a new environmentally friendly generation of vehicles becomes a necessity. Hybrid electric vehicles are one means of increasing propulsion system efficiency and decreasing pollutant emissions. In this paper, the series-parallel power-split configuration for Michigan Technological University's FutureTruck is analyzed. Mathematical equations that describe the hybrid power-split transmission are derived. The vehicle's differential equations of motion are developed and the system's need for a controller is shown. The engine's brake power and brake specific fuel consumption, as a function of its speed and throttle position, are experimentally determined. A control strategy is proposed to achieve fuel efficient engine operation. The developed control strategy has been implemented in a vehicle simulation and in the test vehicle. Simulation and experimental results are presented and discussed. The control strategy leads to a series hybrid vehicle behavior at low speeds and parallel hybrid vehicle behavior at highway speeds. Furthermore, the strategy ensures charge sustaining vehicle operation.
Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons. The reduction of the fuel consumption of these vehicles has become a top priority. Many ideas to reach that objective have been presented. In most cases these systems are more complex than the traditional approaches. For such complex systems a heuristic design approach fails. The only way to deal with this situation is to employ model-based methods. This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms.
The paper presents a novel approach for hybrid powertrain control, based on a real-time minimization of the equivalent fuel consumption. This approach is non-predictive, thus the control strategy developed requires only information on the current status of the powertrain. The control parameters are continuously estimated on-board using information deriving from static route mapping and telemetry. Simulations for a prototypical hybrid car under development show the very promising benefits in terms of fuel consumption reduction and charge sustaining that can be obtained with the controller presented.
This paper presents the development and the implementation of energy management algorithms for the control of a parallel hybrid-electric sport-utility vehicle. Two strategies based on two different control techniques have been prototyped, simulated and analyzed. In order to compare their performance in terms of fuel consumption improvement and emissions control, the algorithms are evaluated using a simulation model of the hybrid powertrain in the Matlab/Simulink environment. The algorithms have also been implemented in the ASCET -SD environment on-board a prototype SUV based on the Ford Explorer platform. This paper summarizes the evaluation phase of the program, and describes the energy management algorithms and their effectiveness in the simulation environment.
The hybridization of fuel cell vehicles not only leads to an improved efficiency, but also results in a significant lessening of the dynamic control requirements for automotive fuel cell systems. However, a proper power flow management plays a fundamental role in order to guarantee the optimal performances. In this paper, a two level control architecture is proposed for vehicles equipped with a fuel cell system and a battery. The lower level scheme controls the fuel cell acting on the compressor command and on the back pressure valves of anode and cathode. On the other hand the higher control level is devoted to manage the power flows, that is the power absorbed by the motor and the one provided by the fuel cell. In view of its capability to manage constraints, such as those inherently related to the level of the battery charge, the Model Predictive Control (MPC) approach is used to design the controller acting at the higher level.
Typically the energy management problem of a hybrid vehicle is formulated as an optimization problem, where the optimal power split between the prime mover and the secondary power converter is calculated off line based on a given driving cycle and solved numerically with dynamic programming techniques. An important constraint is that the energy level of the secondary power source at the end is the same as in the beginning. In real live the future driving cycle is not known a priori, making it difficult to calculate the exact optimal power split beforehand. To arrive at a practical real time control algorithm, a sub-optimal control law can be applied, where the end-point constraint is replaced by a term in the cost function that accounts for the change in energy; in case of a hybrid electric vehicle it represents the fuel equivalence of the stored reversible energy. In this paper it is reasoned that the reversible energy contains also kinetic and potential energy of the vehicle as well as energy stored in the secondary power source. By feedback control of the state of energy of the secondary power source, the amount of stored energy can be kept on a trajectory, such that the total amount of reversible energy remains constant. Kinetic and potential energy is proportional with vehicle mass, therefore this trajectory is adaptive to vehicle loading. In this paper simulations of an on-line strategy are included that show fuel consumption improvements of a distribution truck, close to those obtained with dynamic programming, validating the reasoning.
For a kind of parallel hybrid electric vehicle, a fuzzy controller of energy management is constructed by using the torque request of the hybrid system and the battery state of charge (SOC) as inputs, and the engine torque as the output. Then, membership functions and rules of fuzzy controller are optimized simultaneously by using particle swarm optimization (PSO) based on the optimization object of fuel economy. This energy management strategy is implemented on a PHEV prototype in ADVISOR, and simulation results show that this approach provides excellent fuel efficiency along with charge sustaining operation.
A fuzzy neural network (FNN) control scheme for a class of complicated nonlinear systems was presented. In this scheme it has the structure that combines a FNN controller with neural network identification controller, a new improved learning algorithm was derived theoretically. Based on the error-compensation method and using the modified genetic algorithm for optimizing the membership functions, the accuracy of the algorithm was improved. Then chaotic mechanism was introduced to normal BP algorithm, and the problem of local limit value for network was solved by using global moving characteristic of chaotic mechanism. The simulation results show that this design has a better performance than normal fuzzy controller.
Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function. Two control algorithms - ECMS (Equivalent Consumption Minimization Strategy) and DP (dynamic programming) - are considered in this paper to optimize the power split between electrical and mechanical energy sources. The performance obtained using dynamic programming as global optimal energy management strategy for a PHEV is used as benchmark for evaluating on-board implementable control strategy - ECMS. The ECMS is used to design two control modes - EV and Blended. The model of a PHEV version of a Chevrolet Equinox fueled by bio-diesel B20 has been developed in the Matlab/Simulink environment. A Chevrolet Equinox was hybridized at The Center of Automotive Research (CAR), at The Ohio State University as part of Challenge-X competition; the vehicle was used to validate the components of the Simulink model.
This chapter focuses on the relative fuel economy potential of intelligent, hybrid, and intelligent–hybrid passenger vehicles. Apart from passenger safety and large-scale traffic management, telematics is increasingly used for fuel saving. Hybrid powertrains, which represent a viable interim solution to overcome the range and battery life issues, come with the disadvantage of increased cost, as two propulsion systems have to be incorporated into one vehicle. The alternative, or complementary, solution to hybrid powertrains is the use of telematic technology whose potential in the road freight industry has already been proved. With the use of road grade prediction algorithms and global positioning system (GPS) data in the road freight industry, improvements in fuel economy of up to 3.5% and a 40% reduction in gear changes have been observed. The major advantages of hybrid powertrains include engine shutoff, when the vehicle is stationary, and regenerative braking. Although during highway driving, the hybrid vehicle does not offer fuel saving when compared to an equivalent conventional powertrain vehicle, as there is additional mass that must be carried, and the ability to recharge the batteries during regenerative braking events is minimal, the next generation of hybrid vehicles would include plug-in variants and an on-board GPS system fed into the vehicle's power management system that could be used to inform a controller about the vehicle's proximity to a likely recharge station, etc.
Deciding what mix of engine and battery power to use is critical to hybrid vehicles' fuel efficiency. Current solutions consider several factors such as the charge of the battery and how efficient the engine operates at a given speed. Previous research has shown that by taking into account the future power requirements of the vehicle, a more efficient balance of engine vs. battery power can be attained. In this paper, we utilize a probabilistic driving route prediction system, trained using Inverse Reinforcement Learning, to optimize the hybrid control policy. Our approach considers routes that the driver is likely to be taking, computing an optimal mix of engine and battery power. This approach has the potential to increase vehicle power efficiency while not requiring any hardware modification or change in driver behavior. Our method outperforms a standard hybrid control policy, yielding an average of 1.22% fuel savings. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.
In this paper, predictive energy management strategies that utilize the previewed traffic pattern and terrain information are developed. A generalized predictive optimal control framework is used to find the conditions under which the predictive strategies, will give superior fuel economy to that of the instantaneous strategies. Mixed integer linear programming methodology, with no assumptions on the control structure, is used to find the predictive energy management strategies. It is shown, by using theoretical work and simulation, that certain conditions are needed to make the predictive strategies, that utilize the previewed driving pattern and terrain information, give superior fuel economy to the instantaneous ones.
An energy management controller based on shortest path stochastic dynamic programming (SP-SDP) is implemented and tested in a prototype vehicle. The controller simultaneously optimizes fuel economy and powertrain activity, namely gear shifts and engine on-off events. Previous work reported on the controller's design and its extensive simulation based evaluation. This paper focuses on implementation of the controller algorithm in hardware. Practical issues concerning real-time computability, driver perception, and command timing are highlighted and addressed. The SP-SDP controllers are shown to run in real-time, gracefully handle variations in engine start and gear-shift-completion times, and operate in a manner that is transparent to the driver. A hardware problem with the test vehicle restricted its maximum engine torque, which prevented a reliable fuel economy assessment of the SP-SDP controller. The data that were collected indicated that SP-SDP controllers could be straightforwardly designed to operate at different points of the fuel economy tradeoff curve and that their fuel economy may equal or exceed that of a baseline industrial controller designed for the vehicle.
We use a survey to compare consumers’ stated interest in conventional gasoline (CV), hybrid (HEV), plug-in hybrid (PHEV) and pure electric vehicles (EV) of varying designs and prices. Data are from 508 households representing new vehicle buyers in San Diego County, California in 2011. The mixed-mode survey collected information about access to residential recharge infrastructure, three days of driving patterns, and desired vehicle designs and motivations via design games. Across the higher and lower price scenarios, a majority of consumers designed and selected some form of PHEV for their next new vehicle, smaller numbers designed an HEV or a conventional vehicle, and only a few percent designed an EV. Of those who did not design an EV, the most frequent concerns with EVs were limited range, charger availability, and higher vehicle purchase prices. Positive interest in HEVs, PHEVs and EVs was associated with vehicle images of intelligence, responsibility, and support of the environment and nation (United States). The distribution of vehicle designs suggests that cheaper, smaller battery PHEVs may achieve more short-term market success than larger battery PHEVs or EV. New car buyers’ present interests align with less expensive first steps in a transition to electric-drive vehicles.
As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.
Telematics allow a prediction of the future driving conditions of a car along some time horizon. This prediction offers a knowledge of the torque request caused by the route ahead and can be used for implementing sophisticated operating strategies for Hybrid Electric Vehicles (HEVs). The intention of the present, paper is to describe a method which minimizes the fuel consumption of the system beyond the prediction horizon. Therefore the strategy determines the best operating conditions of the combustion engine and the electric motor with respect to the predicted torque request, and the SOC of the battery.
To fully utilize the fuel reduction potential of a hybrid powertrain requires a careful design of the energy management control algorithms. Here a controller is created using map-based equivalent consumption minimization strategy and implemented to function without any knowledge of the future driving mission. The optimal torque distribution is calculated offline and stored in tables. Despite only considering stationary operating conditions and average battery parameters, the result is close to that of deterministic dynamic programming. Effects of making the discretization of the tables sparser are also studied and found to have only minor effects on the fuel consumption. The controller optimizes the torque distribution for the current gear as well as assists the driver by recommending the gear that would give the lowest consumption. Two ways of adapting the control according to the battery state of charge are proposed and investigated. One of the adaptive strategies is experimentally evaluated and found to ensure charge sustenance despite poor initial values.
This paper describes a strategy for fuel economy improvement of light duty truck with parallel hybrid system. The main objective of this paper is to develop a new hybrid controller which optimizes the torque distribution among various running situations and drivers characteristics with on-line simulation, computing fuel and electric current consumption by using neural network models of the hybrid ECU. Then, fuel and battery current consumption computational models with respect to battery state of charge (SOC), engine and motor torque and engine speed are synthesized by using neural network, and the models are based on experimental data. Finally, the new hybrid controller including the above mentioned models is developed, and its effectiveness on fuel economy improvement is verified by using computer simulation.
Recent studies from several authors show that it is possible to lower the fuel consumption for heavy trucks by utilizing information about the road topography ahead of the vehicle. The approach in these studies is receding horizon control where horizon length and residual cost are main topics. To approach these topics, fuel equivalents previously introduced based on physical intuition are given a mathematical interpretation in terms of Lagrange multipliers. Measures for the suboptimality, caused by the truncated horizon and the residual cost approximation, are defined and evaluated for different routes and parameters.
To demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV), an energy optimization strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented. In addition, the paper reports an analysis and evaluation of issues related to real time control implementation for the modeled PHEV system, which include the optimization window sizes and the impact of prediction errors on the energy optimization strategy performance. The optimization time window sizes were identified and validated for different driving cycles under different operating modes and total length of travel. With the identified optimization windows size, improvements in fuel consumption were realized; the highest improvement was for Urban Dynamometer Driving Schedule (UDDS), with a range of improvement of 14–31%, followed by a 1–15% range of improvement for Highway Fuel Economy Driving Schedule (known as HWFET) and a 1–8% range of improvement for US06 (also known as Supplemental Federal Test Procedure). While no correlation was observed between the error rate and the rate of increased fuel consumption, this PHEV system still yielded energy savings with errors in the speed prediction, which is an indication of robustness of this PHEV model.
In this paper we present a novel adaptation method for the Adaptive Equivalent fuel Consumption Minimization Strategy (A-ECMS). The approach is based on Driving Pattern Recognition (DPR). The Equivalent (fuel) Consumption Minimization Strategy (ECMS) method provides real-time suboptimal energy management decisions by minimizing the "equivalent" fuel consumption of a hybrid-electric vehicle. The equivalent fuel consumption is a combination of the actual fuel consumption and electrical energy use, and an equivalence factor is used to convert electrical power used into an equivalent chemical fuel quantity. In this research, a driving pattern recognition method is used to obtain better estimation of the equivalence factor under different driving conditions. A time window of past driving conditions is analyzed periodically and recognized as one of the Representative Driving Patterns (RDPs). Periodically updating the control parameter according to the driving conditions yields more precise estimation of the equivalent fuel consumption cost, thus providing better fuel economy. Besides minimizing the instantaneous equivalent fuel consumption, the battery State of Charge (SOC) management is also maintained by using a PI controller to keep the SOC around a nominal value. The primary improvement of the proposed A-ECMS over other algorithms with similar objectives is that it does not require the knowledge of future driving cycles and has a low computational burden. Results obtained in this research show that the driving conditions can be successfully recognized and good performance can be achieved in various driving conditions while sustaining battery SOC within desired limits.
This paper describes the optimization of the parallel hybrid electric vehicle (HEV) component sizing using a genetic algorithm approach. The optimization process is performed over three different driving cycles including the European ECE-EUDC, American FTP and TEH-CAR cycles in order to investigate the influence of the driving pattern on the optimal HEV component sizes. Hybrid Electric Vehicles are considered as a solution to the world’s need for cleaner and more fuel-efficient vehicles. HEVs use a combination of an internal combustion engine and an electric motor to propel the vehicle. Proper execution of a successful HEV design requires optimal sizing of its key mechanical and electrical components. In this paper, genetic algorithm is used as the optimization approach to find the best size of internal combustion engine, electric motor and energy storage system. The objective is minimization of fuel consumption and emissions while vehicle performances, like acceleration and gradeability are defined as constraints. These constraints are handled using penalty functions. Simulation results reveal that the HEV optimal component sizing is independent from the driving pattern. However, the amount of fuel use and emissions are extremely dependent on the driving cycles. In addition, the results show, while the performance constraints are within the standard criteria, the reduction in fuel consumption and emissions are achieved.
This paper proposes a new method for solving the energy management problem for hybrid electric vehicles (HEVs) based on the equivalent consumption minimization strategy (ECMS). After discussing the main features of ECMS, an adaptation law of the equivalence factor used by ECMS is presented, which, using feedback of state of charge, ensures optimality of the strategy proposed. The performance of the A-ECMS is shown in simulation and compared to the optimal solution obtained with dynamic programming.
The energy management strategy in a hybrid electric vehicle is viewed as an optimal control problem and is solved using Model Predictve Control (MPC). The method is applied to a series hybrid electric vehicle, using a linearized model in state space formulation and a linear MPC algorithm, based on quadratic programming, to find a feasible suboptimal solution. The significance of the results lies in obtaining a real-time implementable control law. The MPC algorithm is applied using a quasi-static simulator developed in the MATLAB environment. The MPC solution is compared with the dynamic programming solution (offline optimization). The dynamic programming algorithm, which requires the entire driving cycle to be known a-priori, guarantees the optimality and is used here as the benchmark solution. The effect of the parameters of the MPC (length of prediction horizon, type of prediction) is also investigated.
This paper deals with optimization algorithms for energy management of Plug-in Hybrid Electric Vehicles (PHEVs). In order to maximize fuel economy of a PHEV, the battery should attain its lowest admissible state of charge at the end of the driving cycle by following an optimal State of Charge (SOC) profile. Finding this optimal profile is a challenging optimization problem and requires prior knowledge of the entire driving cycle. There are many different optimization methods that can be applied to the energy management of PHEVs and they are usually classified into two main categories according to the optimality of their solutions. In general, in order to obtain the global optimum, the complete knowledge of future driving conditions is needed. This requirement renders unfeasible the on-line implementation of such strategies. On the other hand, simpler algorithms which are on-board implementable, do not provide the optimal solution. In this paper, a global optimal strategy — Dynamic Programming, is considered as a benchmark for evaluating the performance of an onboard implementable strategy — Equivalent Consumption Minimization Strategy with linearly decreasing reference SOC’. The study is conducted on an energy-based model of a parallel hybrid powertrain developed in Matlab/Simulink environment. The model and each powertrain components are validated based on road tests and laboratory data for a Chevrolet Equinox (hybridized at The Ohio State University Center for Automotive Research). The optimality assessment considers two main metrics, namely fuel economy and deviations from the optimal SOC profile. Simulations are carried out by considering different driving scenarios and battery sizes. Results show that for longer distances and bigger batteries, Equivalent Consumption Minimization Strategy and Dynamic Programming provide similar fuel economy and SOC profiles.
Toyota Hybrid System is the innovative powertrain used in the current best-selling hybrid vehicle on the market—the Prius. It uses a split-type hybrid configuration which contains both a parallel and a serial power path to achieve the benefits of both. The main purpose of this paper is to develop a dynamic model to investigate the unique design of THS, which will be used to analyze the control strategy, and explore the potential of further improvement. A Simulink model is developed and a control algorithm is derived. Simulations confirm our model captures the fundamental behavior of THS reasonably well.
Online receding horizon controller based on the principle of predictive control for parallel Hybrid Electric Vehicle is proposed in this paper. First of all, the structure of Hybrid Electric Vehicles is introduced, and the model of batteries and engine, etc is established. Secondly the energy management strategies based on predictive control algorithm is presented. At last the results of simulation experiments are analyzed compared with the rule-based control strategy in different conditions.
This paper studies the minimization of the fuel consumption of hybrid electric vehicles (HEV) using model predictive control (MPC) method. The presented MPC-based controller calculates an optimal sequence of control inputs to the vehicle plant over the receding prediction horizon based on the torque demand estimated from the desired vehicle speed and the desired state of charge (SOC) of the battery. The controller is implemented in Simulink using Matlab model predictive control toolbox. The effectiveness of the MPC-based controller is validated by the 2004 Toyota Prius, a power-split HEV. The simulation result presents sound transient response performance and the ability to remain the SOC within a reasonable range.
In this paper, the author proposed an energy management strategy of the Parallel Hybrid Electric Vehicle (PHEV) was that based on the fuzzy logic control for torque distribution between motor and engine. The value D which is the difference value between the torque of the vehicle requirements Tr and the engine goal requires torque Te, and the battery state of charge (SOC) as two of the fuzzy controller input variables and the torque control coefficient C is the output variable of the fuzzy controller. With three different road conditions, simulated respectively between the rule-based of electric assist control strategy and the fuzzy logic control strategies by using the electric vehicle software ADVISOR. The result shows that the design of fuzzy logic energy management strategy can easily control the engine and motor, significantly increasing fuel economy performance and reduced exhaust emissions.
This paper describes a new methodology for sizing energy sources in hybrid electric vehicles, that enables obtaining the minimal sizing required for a given driving cycle, independently of the chosen energy management strategy. The methodology is based on two combined optimization loops: one for sizing the energy sources, using a genetic algorithm, and another one for computing the optimal energy management strategy for a specific driving cycle, using dynamic programming. Results show that the algorithm can find the best sizing of sources for the best fuel consumption, with a 6.5kW fuel cell and a 75Wh battery for the ECE driving cycle and a 9.0kW fuel cell and a 72Wh battery for the LA92 cycle. Compared to results obtained through the mean sizing power method, the algorithm shows that the hydrogen consumption can be reduced by up to 70% and the size of the battery by up to 67 %. The proposed methodology can thus help optimize the sizing of hybrid vehicles used for given driving cycles.
In order to reduce the fuel consumption and emissions of automobiles, the powertrain structure of Parallel Hybrid Electric Vehicle (PHEV) and the models of main components were firstly established. Then, on the basis of the models, the fuzzy control energy management system (EMS) was designed for PHEV. The fuzzy control energy management strategy was embedded in soft of Advisor for simulation. Finally, the parameters of the powertrain were optimized. Simulation results show that the fuzzy control strategies with parameters optimization not only have improved the fuel economy, but also reduced the emissions of HC, CO and so on, compared with the before parameters optimization.
Because the theoretical part of the book is based on the calculus of variations, the exposition is very transparent and requires mostly a trivial mathematical background. In the case of open-loop optimal control, this leads to Pontryagin's Minimum Principle and, in the case of closed-loop optimal control, to the Hamilton-Jacobi-Bellman theory which exploits the principle of optimality. Many optimal control problems are solved completely in the body of the text. Furthermore, all of the exercise problems which appear at the ends of the chapters are sketched in the appendix. The book also covers some material that is not usually found in optimal control text books, namely, optimal control problems with non-scalar-valued performance criteria (with applications to optimal filtering) and Lukes' method of approximatively-optimal control design. Furthermore, a short introduction to differential game theory is given. This leads to the Nash-Pontryagin Minimax Principle and to the Hamilton-Jacobi-Nash theory. The reason for including this topic lies in the important connection between the differential game theory and the H-control theory for the design of robust controllers. © Springer-Verlag Berlin Heidelberg 2007. All rights are reserved.