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A Power Consumption and Total Cost of Ownership Analysis of Extended Range System for a Logistics Van
Abstract
Urban logistics holds the lifeline of global business and consumption demand. The amount of transport vehicles rises gradually but also leads to graver emission problem. Therefore, electrified vehicles are considered as better options to substitute traditional logistics vehicles. Extended range electric logistics van (ERELV) provides a solution by providing satisfactory driving-range and lower production cost than battery electric logistics van (BELV). This work aims to present a thorough energy consumption and Total cost of ownership (TCO) analysis for an extended range electric van to present its energy potential. Both the ERELV and BELV mathematical model are constructed and compared, and their long-term battery degradation comparison is studied for the first time. Dynamic programming is adopted in the energy management strategy for energy consumption optimization, and the global optimization result reveals the optimal energy consumption of the ERELV. Comparative results demonstrate that the ERELV has a relatively longer drive distance, slower battery aging trend and cheaper TCO (6.6%) when comparing to the BELV. The adoption of ERELV will promisingly reduce operating cost as the alternative transportation solution for urban logistics.
... Due to the range limitation of the battery electric vehicle and insufficient charging stations in the current stage, many types of hybrid electric vehicles were invented to solve the range anxiety and improve the fuel-electricity efficiency for different transportation markets. For instance, the extended range electric vehicle (EREV) is perfectly suitable for logistics usage thanks to its long driving range, high fuel efficiency, and low operation cost [1]. ...
... However, it can only apply to a known system with prior knowledge, which makes it an offline strategy and can only be appliable to those potential analysis studies or used as a benchmark. DP was used to test the economic potential of hybrid vehicles [1,7], and widely used to compare with the results from the RL-based algorithms [8e11]. ...
... In Fig. 12, the SOC trajectories of the SAC and DDPG agents during two driving cycles are plotted against their DP results. The DP algorithm was adopted from Ref. [1] to search for optimal SOC trajectory while maintaining an optimal APU fuel efficiency. To make sure the same condition was applied, the DP algorithm picked the same starting battery SOC and final SOC from the RL result as its initial/final state. ...
The modern energy management strategy (EMS) plays a vital role in the energy efficiency of the extended range electric vehicle. However, some modern strategies such as model predictive control (MPC) and dynamic programming (DP) have limited practical potential because they are subject to the pre-known environment information and noise interference. The reinforcement learning (RL)control strategy can be adopted as online control to interact with the vehicle and the environment. In this study, a novel auxiliary power unit (APU) charging strategy with multi-object optimization is proposed to achieve high fuel conversion efficiency while maintaining battery charging health. The state-of-the-art algorithm, Soft Actor-Critic (SAC), is applied to achieve better exploration of the possible APU behaviour and solve the sensitivity and poor convergence problems from the current RL studies. Its performance is further verified by the results of the Deep Deterministic Policy Gradient (DDPG) algorithm and DP. Three innovative targets are selected as the RL rewards for optimization: the engine fuel rate, SOC charging trajectory, and the battery charging rate (C-rate). The first adoption of the battery C-rate monitoring in RL-based energy management strategy helps extend the battery lifespan from excessive discharge. The comparative results show that the SAC had a 36% faster convergence speed than DDPG while providing a smoother and more stable action space. The fuel consumption with SAC also outplays that of DDPG by around 3%, which achieves almost 95% of the global optimization result. The successful deployment of the SAC algorithm as an EMS indicates its standout ability in dealing with wide-range actions and states with high randomness, revealing the practical potential compared with the existing RL strategies.
... A series HEV acquires drive torque only from its motor, and its ICE/generator combination converts fuel to electrical energy which is then captured and stored in the battery. This type of structure [195] þþ þ þþ þ 0 1 -BEV [196] þ þþ þ 1 0 þþþ Parallel [8] þþ þþ þ þþ 1 1 þ Series [8] þþ þþ þ þ 2 0 þþ Series-Parallel [17] þþ þþ þ þþþ 2 1 þ PHEV [67] þþ þþ þ þþ 2 1 þþ EREV [195] þþ þþþ þþ þ 2 0 þþ has the highest power conversion efficiency but usually require a larger energy storage system (ESS) in comparison to other HEVs. Parallel HEV is capable of receiving drive torque from both engine and motor, but it needs a power splitting transmission to couple the two power plants. ...
... A series HEV acquires drive torque only from its motor, and its ICE/generator combination converts fuel to electrical energy which is then captured and stored in the battery. This type of structure [195] þþ þ þþ þ 0 1 -BEV [196] þ þþ þ 1 0 þþþ Parallel [8] þþ þþ þ þþ 1 1 þ Series [8] þþ þþ þ þ 2 0 þþ Series-Parallel [17] þþ þþ þ þþþ 2 1 þ PHEV [67] þþ þþ þ þþ 2 1 þþ EREV [195] þþ þþþ þþ þ 2 0 þþ has the highest power conversion efficiency but usually require a larger energy storage system (ESS) in comparison to other HEVs. Parallel HEV is capable of receiving drive torque from both engine and motor, but it needs a power splitting transmission to couple the two power plants. ...
To address the urgent environmental challenges of transportation related air pollution and energy shortage, hybrid electric vehicles (HEV) and battery electric vehicles (BEV) with potential for higher energy efficiency are gaining in popularity and gradually replacing those conventional vehicles. Extended range electric vehicle (EREV) is a subset of these new energy vehicles aiming to gain benefits of both HEVs and BEVs and provide a solution to reducing tailpipe emissions whilst providing satisfactory driving-range compared with tradition internal combustion engine (ICE) vehicle counterparts. This paper systematically introduces the current developments of EREV in terms of the powertrain structure, energy management and vehicle performance, while the two key focus areas of auxiliary power unit (APU) charging control and (hybrid) energy storage system ((H)ESS) power management. A case analysis on a typical EREV is presented to study the application potential. Comprehensive comparisons of popular APU control strategies and different energy storage dispositions are shared, several existing issues are also discussed in this paper. Furthermore, a general manufacturing cost analysis for different types of vehicles is presented, and the outlook of EREV for future research is presented.
During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models' concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress-either failure or success-has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.
Traction batteries are a key factor in the environmental sustainability of electric mobility and, therefore, it is necessary to evaluate their environmental performance to allow a comprehensive sustainability assessment of electric mobility. This article presents an environmental assessment of a lithium-ion traction battery for plug-in hybrid electric vehicles, characterized by a composite cathode material of lithium manganese oxide (LiMn2O4) and lithium nickel manganese cobalt oxide Li(NixCoyMn1-x-y)O2. Composite cathode material is an emerging technology that promises to combine the merits of several active materials into a hybrid electrode to optimize performance and reduce costs. In this study, the environmental assessment of one battery pack (with a nominal capacity of 11.4 kWh able to be used for about 140,000 km of driving) is carried out by using the Life Cycle Assessment methodology consistent with ISO 14040. The system boundaries are the battery production, the operation phase and recycling at the end of life, including the recovery of various material fractions. The composite cathode technology examined besides a good compromise between the higher and the lower performance of NMC and LMO cathodes, can present good environmental performances.
The results of the analysis show that the manufacturing phase is relevant to all assessed impact categories (contribution higher than 60%). With regard to electricity losses due to battery efficiency and battery transport, the contribution to the use phase impact of battery efficiency is larger than that of battery transport. Recycling the battery pack contributes less than 11% to all of the assessed impact categories, with the exception of freshwater ecotoxicity (60% of the life cycle impact). The environmental credits related to the recovery of valuable materials (e.g. cobalt and nickel sulphates) and other metal fractions (e.g. aluminium and steel) are particularly relevant to impact categories such as marine eutrophication, human toxicity and abiotic resource depletion.
The main innovations of this article are that (1) it presents the first bill of materials of a lithium-ion battery cell for plug-in hybrid electric vehicles with a composite cathode active material; (2) it describes one of the first applications of the life cycle assessment to a lithium-ion battery pack for plug-in hybrid electric vehicles with a composite cathode active material with the aim of identifying the “hot spots” of this technology and providing useful information to battery manufacturers on potentially improving its environmental sustainability; (3) it evaluates the impacts associated with the use phase based on primary data about the battery pack's lifetime, in terms of kilometres driven; and (4) it models the end-of-life phase of the battery components through processes specifically created for or adapted to the case study.
Despite rapid increase of battery electric vehicles (BEV) in many countries, there are still concerns related to technology, usability and safety aspects of BEVs. The present study examines the role of perceived accident risk, knowledge, perceived car attributes, subjective norm and perceived behavioral control, together with demographic variables, on intention to buy a BEV among conventional car drivers. A web-survey is used to collect data from 205 conventional car drivers (76.5% male) living in different parts of Norway. A regression analysis is conducted to investigate the predictive role of the measured constructs for the intention to buy a BEV. Results show that environmental-economic attributes of BEVs, subjective norm and perceived behavioral control are positively related to the intention. Meanwhile, being male is negatively related to the intention. Neither perceived accident risk nor the knowledge about BEVs has a direct effect on the intention. However, further mediation analysis shows that both perceived accident risk and knowledge have an indirect effect on the intention through the perceived attributes of BEVs. Implications of the results for interventions aiming to increase BEV adoption are discussed.
Energy management strategies based on instantaneous optimization have been widely used in hybrid/plug-in hybrid electric vehicles (HEV/PHEV) in order to improve fuel economy while guaranteeing vehicle performance. In this study, an adaptive-equivalent consumption minimum strategy (A-ECMS) based on target driving cycle (TDC) generation was proposed for an extended-range electric bus (E-REB) operating on fixed routes. Firstly, a Hamilton function and a co-state equation for E-REB were determined according to the Pontryagin Minimum Principle (PMP). Then a series of TDCs were generated using Markov chain, and the optimal solutions under different initial state of charges (SOCs) were obtained using the PMP algorithm, forming the optimal initial co-state map. Thirdly, an adaptive co-state function consisting of fixed and dynamic terms was designed. The co-state map was interpolated using the initial SOC data and the vehicle driving data obtained by an Intelligent Transport System, and thereby the initial co-state values were solved and used as the fixed term. A segmented SOC reference curve was put forward according to the optimal SOC changing curves under different initial SOCs solved by using PMP. The dynamic term was determined using a PI controlling method and by real-time adjusting the co-states to follow the reference curve. Finally with the generated TDCs, the control effect of A-ECMS was compared with PMP and Constant-ECMS, which was showed A-ECMS provided the final SOC closer to the pre-set value and fully used the power of the batteries. The oil consumption solutions were close to the PMP optimized results and thereby the oil depletion was reduced.
The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.
A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery’s state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies.
Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the battery state of charge (SOC). Therefore, online recursive ECM parameter estimation is one means that may help to improve the modelling accuracy. Because a battery system consists of both fast and slow dynamics, the classical least squares (LS) method, that estimates together all the model parameters, is known to suffer from numerical problems and poor accuracy. The aim of this paper is to overcome this problem by proposing a new decoupled weighted recursive least squares (DWRLS) method, which estimates separately the parameters of the battery fast and slow dynamics. Battery SOC estimation is also achieved based on the parameter estimation results. This circumvents an additional full-order observer for SOC estimation, leading to a reduced complexity. An extensive simulation study is conducted to compare the proposed method against the LS technique. Experimental data are collected using a Li ion cell. Finally, both the simulation and experimental results have demonstrated that the proposed DWRLS approach can improve not only the modelling accuracy but also the SOC estimation performance compared with the LS algorithm.
As concerns of oil depletion and security of supply remain as severe as ever, and faced with the consequences of climate change due to greenhouse gas emissions, Europe is increasingly looking at alternatives to traditional road transport technologies. Battery Electric Vehicles (BEVs) are seen as a promising technology, which could lead to the decarbonisation of the Light Duty Vehicle fleet and to independence from oil. However it still has to overcome some significant barriers to gain social acceptance and obtain appreciable market penetration. This review evaluates the technological readiness of the different elements of BEV technology and highlights those technological areas where important progress is expected. Techno-economic issues linked with the development of BEVs are investigated. Current BEVs in the market need to be more competitive than other low carbon vehicles, a requirement which stimulates the necessity for new business models. Finally, the all-important role of politics in this development is, also, discussed. As the benefit of BEVs can help countries meet their environmental targets, governments have included them in their roadmaps and have developed incentives to help them penetrate the market.
Rapid vehicle and powertrain development has become essential to for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer while keeping pace with reduced development cycle and more frequent product releases. Recently, advances in large-scale additive manufacturing have provided the means to bridge hardware-in-the-loop (HIL) experimentation and preproduction mule chassis evaluation. This paper details the accelerated development of a printed range-extended electric vehicle (REEV) by Oak Ridge National Laboratory, by paralleling hardware-in-the-loop development of the powertrain with rapid chassis prototyping using big area additive manufacturing (BAAM). BAAM’s ability to accelerate the mule vehicle development from computer-aided design to vehicle build is explored. The use of a hardware-in-the-loop laboratory is described as it is applied to the design of a range-extended electric powertrain to be installed in a printed prototype vehicle. The integration of the powertrain and the opportunities and challenges it presents are described in this work. A comparison of offline simulation, HIL and chassis rolls results is presented to validate the development process. Chassis dynamometer results for battery electric and range extender operation are analyzed to show the benefits of the architecture.
This paper describes a coordination control procedure in auxiliary power unit (APU) power source system for a range extended electric vehicle (RE–EV). Based on the characteristics of urban traffic conditions, the matching on power and selection on parts for the power system of RE-EV is carried on aiming to improve the power and economy. A more economical region for the APU is determined and two kinds of optimization strategy routes are proposed. The coordination control strategy on engine and generator is studied. Simulation results show that the proposed strategy improves the control performance of fuel economy and easy to realize. The comprehensive power control strategies are verified on APU test bench and good agreement with simulation analysis.
As motor-supplied braking torque is applied to the wheels in an entirely different way to hydraulic friction braking systems and it is usually only connected to one axle complicated effects such as wheel slip and locking, vehicle body bounce and braking distance variation will inevitability impact on the performance and safety of braking. The potential for braking energy recovery in typical driving cycles is presented to show its benefit in this study. A general predictive model is designed to analysis the economic and dynamic performance of blended braking systems, satisfying the relevant regulations/laws and critical limitations. Braking strategies for different purposes are proposed to achieve a balance between braking performance, driving comfort and energy recovery rate. Special measures are taken to avoid any effects of motor failure. All strategies are analyzed in detail for various braking events. Advanced driver assistance systems (ADAS), such as ABS and EBD, are properly integrated to work with the regenerative braking system (RBS) harmoniously. Different switching plans during braking are discussed. The braking energy recovery rates and brake force distribution details for different driving cycles are simulated. Results for two of the cycles in an ‘Eco’ mode are measured on a drive train test rig and found to agree with the simulated results to within approximately 10%. Reliable conclusions can thus be gained on the economic benefit and dynamic braking performance. The strategies proposed in this paper are shown to not only achieve comfortable and safe braking during all driving conditions, but also to significantly reduce cost in both the short and long term.
Battery electric vehicles (BEVs) have been slow to diffuse on the international as well as the Swedish market. Previous studies have indicated situational factors such as economic factors, size and performance to be of major importance for vehicle purchasers in their choice of vehicle. In this paper, the authors explore a consumer centric total cost of ownership (TCO) model to investigate the possible discrepancy between purchase price and the TCO between internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs) and BEVs. The creation and testing of the TCO model reveals that computation could be a challenging task for consumers due to bounded access of relevant data and the prediction of future conditions. The application of the model to the vehicle sample found that BEVs could be cheaper compared to ICEVs and HEVs. The findings in this paper could prove to be of importance for policy and marketing alike in designing the most appropriate business models and information campaigns based on consumer conditions in order to further promoting the diffusion of BEVs in society.
The equivalent fuel consumption minimization strategy (ECMS), as an instantaneous optimization energy management strategy (EMS), is one of the method used to realize real-time optimal control for extended range electric vehicles (EREVs). However, owing to the complexity of the fuel consumption and battery consumption models, the fuel economy optimization problem of EREVs is nonlinear and non-convex; thus, an equivalent factor (EF), i.e., the key parameter of ECMS, can only be solved by numerical iteration methods, such as the shooting method. This paper rewrites the output and state equations of the optimization problem by polynomial fitting and variable substitution of fuel and battery consumption models, transforms the fuel economy problem of an EREV into convex optimization, proposes a quadratic programming-based analytical solution method to solve EF, and provides an adaptive updating rule for terminal SOC to correct the influence of the prediction error of the driving cycle. Thereafter, an adaptive ECMS (A-ECMS) for the real-time optimal control of an EREV is presented and verified. Simulation results show that the proposed A-ECMS can maintain a terminal SOC close to the target value, and in terms of fuel economy, the difference between the proposed A-ECMS and dynamic programming-based global optimization EMS is less than 2%. Compared with the shooting method based on A-ECMS, the proposed A-ECMS achieves better results in all the performance of state of charge, including maintenance, fuel economy, and real-time performances. Especially for real-time performance, the mean computational efficiency of the proposed method is 13–23 times higher than that of the shooting method.
Open circuit voltage (OCV) test is an effective way of ageing diagnosis for lithium ion batteries and it constitutes a basis for state of charge (SOC) estimation. However, onboard OCV tests are rarely feasible due to the time-consuming nature. In this paper, we propose a method to estimate the results of offline OCV based ageing diagnosis, including electrode capacities and initial SOCs, termed electrode ageing parameters (EAPs). In this method, parts of daily charging profiles are sampled and directly fed into a convolutional neural network to estimate EAPs without feature extraction. Validation results on eight cells show that the estimated EAPs are very close to those obtained by using offline OCV tests. Therefore, this method enables a fast ageing diagnosis at an electrode level. Furthermore, we can use the estimated EAPs to reconstruct OCV-Q (charge amount) curves of batteries at different ageing levels over the entire battery life. The error for the OCV-Q reconstruction is within 15 mV compared with actual OCV-Q curves. Based on the OCV-Q curves, we show that battery capacity can be accurately obtained with an error of less than 1% although it is not explicitly considered as a target. The influence of voltage ranges on estimation results is also discussed.
State of health (SOH) estimation is necessary for lithium ion batteries due to ineluctable battery ageing. Existing SOH estimation methods mainly focus on voltage characteristics without considering temperature variation in the process of health degradation. In this paper, we propose a novel SOH estimation method based on battery surface temperature. The differential temperature curves during constant charging are analysed and found to be strongly related to SOH. Part of the differential temperature curves in a voltage range is adopted to establish a relationship with SOH using support vector regression. The influence of battery discrepancy, voltage range and sampling step are systematically discussed and the best combination of voltage range and sampling step is determined using leave-one-out validation. The proposed method is then validated and compared with an incremental capacity analysis (ICA) based SOH estimation method using the Oxford battery degradation dataset. The results show that the proposed method is capable of estimating SOH with the maximum absolute error and root-mean-square error less than 3.90% and 1.80%, respectively. In addition, the proposed method can improve the SOH estimation accuracy and robustness by combining with the ICA based method with little computational burden.
Given the increasing painful repercussions of air pollution caused by massive vehicular emissions and the unsustainable development of transport sectors, the integration of traffic models with emission estimation models has been investigated in depth to evaluate the performance of the pre-determined environment-friendly transportation policies. Nevertheless, there is a lack of the optimization approach to design the traffic control strategy combining the coupled models. Therefore, an optimization framework is developed to optimize the traffic control strategies to mitigate the vehicular emissions in the considered network. The proposed framework integrates a macroscopic analytical traffic model with a macroscopic emissions estimation model to account for the vehicle emissions, and then genetic algorithm (GA) is employed to minimize vehicle emissions. A congested urban area in Xi’an city that consists of two signalized intersections is selected for a case study. The performance of the signal plans derived by the proposed framework is compared with the currently in use signal plans via the microscopic simulator AIMSUN. It indicates that the proposed traffic control scheme reduces expected vehicle trip travel time and vehicle emissions of four sorts of pollutants for the considered area. This optimization framework helps traffic operators to design environment-friendly traffic signal control strategy.
In order to achieve near-optimal fuel economy for plug-in hybrid electric vehicles (PHEVs) using the equivalent consumption minimum strategy (ECMS), it is necessary to dynamically tune the equivalent factor (EF). Unlike widely used model-based approaches, this paper proposes a data-driven ECMS that determines the EF using an artificial neural network (ANN). First, by comparing Pontryagin's Minimum Principle (PMP) with the ECMS, one can find that the EF is related to the co-state value of the PMP method. Then, an ANN is constructed with three accessible input variables, including the current demanded power, the ratio of the distance travelled to the total distance, and the battery State of Charge (SOC). The neural network is subsequently trained using real-world speed profiles. Simulations are performed considering different initial SOC values. The results reveal that the proposed data-driven ECMS demonstrates satisfactory fuel economy compared to global optimization methods like dynamic programming and PMP methods. The computational time of the proposed method relative to the duration of the entire trip indicates a great potential for the development of a time-conscious energy management strategy. Moreover, the impact of training sample size on the ANN performance is discussed.
The joint analysis of friction coefficient and time-frequency characteristics of vibration is employed in this study to explore braking behaviours under dry and wet conditions. The results indicate that there is a weak braking period in the braking process under wet conditions, which would last until the effective friction coefficient is achieved. The duration of the weak braking period is dominant at low initial braking velocities and would be prolonged by strong water flow but shortened by a higher braking pressure. A partial transition from wet to dry stage on the front part of braking interface is deduced in the weak braking period, causing an obvious increasing trend of friction coefficient in this period and asymmetric abrasion of braking surface.
This brief proposes a prediction method of remaining useful life (RUL) based on Gauss-Hermite particle filter (GHPF) in nonlinear and non-Gaussian systems of Lithium-ion batteries (LIBs). In this brief, to improve the accuracy and reduce the computational complexity of the estimation of state of health (SOH), multiscale extended Kalman filter is proposed to execute state of charge (SOC) and SOH joint estimation with dual time scales because of the slow-varying characteristic of SOH and fast-varying characteristic of SOC. Based on the estimation of SOH, a GHPF is developed to update the parameters of the capacity degradation model in real time and predict the RUL of LIBs. The simulation results show that the proposed prediction method of RUL has a better performance and higher precision than the method based on standard PF.
For reliable lifetime predictions of lithium-ion batteries, models for cell degradation are required. A comprehensive semi-empirical model based on a reduced set of internal cell parameters and physically justified degradation functions for the capacity loss is developed and presented for a commercial lithium iron phosphate/graphite cell. One calendar and several cycle aging effects are modeled separately. Emphasis is placed on the varying degradation at different temperatures. Degradation mechanisms for cycle aging at high and low temperatures as well as the increased cycling degradation at high state of charge are calculated separately. For parameterization, a lifetime test study is conducted including storage and cycle tests. Additionally, the model is validated through a dynamic current profile based on real-world application in a stationary energy storage system revealing the accuracy. Tests for validation are continued for up to 114 days after the longest parametrization tests. The model error for the cell capacity loss in the application-based tests is at the end of testing below 1% of the original cell capacity and the maximum relative model error is below 21%.
In order to democratize electric vehicles, automakers have to give a solution to reduce the Total Cost of Ownership (TCO) and bring it better than thermal vehicles one. This paper presents results of works on costs improvement of a 3.5t light-duty commercial electric vehicle. TCO (Total Cost of Ownership) was separated into investment costs and operating costs. The batteries of an electric vehicle were the most expensive element of the powertrain, which can represent until 45% of the vehicle price. What's more, contrary to the life of thermal vehicles, electric energy storage systems have a limited life around 5 years, highly dependent on conditions of use. Thus, study of batteries represents a capital step toward TCO improvement. Inspired by literature, battery aging model is proposed and identified by the calendar and cycling aging tests. A multi-physical model of the battery, including aging, was identified and incorporated inside the energetic vehicle model. Charging strategies were developed and adapted to operate according to the influence of the conditions of use. These models and strategies are integrated in a software tool. Taking into account the intended use of the vehicle, this tool, first, designates the most suitable battery solution dimensions, and then, associates the charging strategy to the requirements of use, in order to ensure the best battery life or the lowest TCO.
Electric vehicles (EVs) have recently attracted considerable attention and so did the development of the battery
technologies. Although the battery technology has been significantly advanced, the available batteries do not
entirely meet the energy demands of the EV power consumption. One of the key issues is non-monotonic consumption of energy accompanied by frequent changes during the battery discharging process. This is very
harmful to the electrochemical process of the battery. A practical solution is to couple the battery with a supercapacitor, which is basically an electrochemical cell with a similar architecture, but with a higher rate
capability and better cyclability. In this design, the supercapacitor can provide the excess energy required while
the battery fails to do so. In addition to the battery and supercapacitor as the individual units, designing the
architecture of the corresponding hybrid system from an electrical engineering point of view is of utmost importance.
The present manuscript reviews the recent works devoted to the application of various battery/supercapacitor
hybrid systems in EVs.
With increasingly serious global environmental issues and energy shortages, energy conservation in transportation has become a significant, fundamental objective. The objective of the current research is to investigate the impacts of different types of optimal control strategies on the plug-in hybrid electric vehicle (PHEV) performance in real-world conditions. The optimal control strategies according to Pontryagin’s minimum principle (PMP) and optimized rule-based approaches are developed for the optimal pattern of a PHEV energy management system to reduce fuel consumption and emissions simultaneously, without sacrificing the vehicle performance. For this purpose, first, using test data for engine and battery, an experimental map-based model of the parallel PHEV is developed. Then, the powertrain components are sized by using a genetic algorithm (GA), over the real-world driving cycles. Subsequently, GA-fuzzy and PMP controllers are developed for energy management of the PHEV. Simulation results show the significant effectiveness of the proposed optimal control approaches on the fuel consumption and emissions reduction in various driving cycles. The convergence speed and global searching ability of PMP are significantly better than GA-fuzzy for the design of control strategy parameters. The sensitivity of battery initial state of charge, driving cycle, and road grade are analyzed on vehicle emissions and fuel consumption. The findings reveal that PMP could be adapted to different conditions by tuning co-state in a short time. This advantage makes it more adaptable to variation of real-world conditions. On the other hand, a fuzzy controller needs less computational effort and so is more appropriate for a certain condition.
Electric vehicles (EVs) and their application raise new questions and demands. Accordingly, the charging process of EVs is a novelty in everyday life when using such vehicles. This paper presents results of user charging behaviour within a field test. In the field test, 500 people with above average yearly mileage took part in integrating the vehicles into their everyday lives. For the results of this paper the charging behavior of 200 testing people are taken into account. The differences in user behaviour between battery electric vehicles (BEVs) and extended range electric vehicles (EREVs) are discussed. The results indicate, that there are various opportunities for grid services based on electric vehicles as the vehicles are connected or parked close to a charging station the majority of the time. Moreover, the results also show deviating user behaviour for the BEVs and EREVs. IEEE
In the pursuit of fuel economy improvement and emission reduction, a trajectory optimization-based engine multi-operating-point control strategy is proposed in this paper for the auxiliary power unit (APU) in an extended range electric vehicle (EREV). This strategy design deeply considers the transient process of the operating point switching which somehow influences the engine fuel economy and emission, and was often ignored. First, a combined cost MAP fully integrated of several indices, including fuel consumption rate, CO, HC, and NOx emissions, is presented to synthetically evaluate the effect of the operation trajectory. Then an improved genetic algorithm (IGA), in terms of computational load reduction and convergence improvement, is employed to optimize the engine trajectory in the transient operating point switching process of the APU system, so it is able to perform with the best fuel consumption and emissions. Finally, simulated and experimental results indicate that the proposed control strategy has obvious improvements in fuel economy and emissions, compared with conventional ones. IEEE
Supercapacitors (SCs) have high power density and exceptional durability. Progress has been made in their materials and chemistries, while extensive research has been carried out to address challenges of SC management. The potential engineering applications of SCs are being continually explored. This paper presents a review of SC modeling, state estimation, and industrial applications reported in the literature, with the overarching goal to summarize recent research progress and stimulate innovative thoughts for SC control /management. For SC modeling, the state-of-the-art models for electrical, self-discharge, and thermal behaviors are systematically reviewed, where electrochemical, equivalent circuit, intelligent, and fractional-order models for electrical behavior simulation are highlighted. For SC state estimation, methods for State-of- Charge (SOC) estimation and State-of-Health (SOH) monitoring are covered, together with an underlying analysis of aging mechanism and its influencing factors. Finally, a wide range of potential SC applications is summarized. Particularly, co-working with high energy-density devices constitutes hybrid energy storage for renewable energy systems and electric vehicles (EVs), sufficiently reaping synergistic benefits of multiple energy-storage units.
A method of multi-objective optimal energy management is proposed to match the APU fuel consumption and the battery state of health (SOH) in the power system of a range-extended electric bus (REEB). Models are established for the calculation of an auxiliary power unit (APU) system fuel consumption and battery SOH loss. The APU fuel consumption and battery SOH are selected as the optimization objectives, and a performance functional of the multi-objective optimization is provided. The dynamic program (DP) algorithm is applied in the control strategy design for solving the multi-objective problem. Under the conditions of the Modified New European Drive Cycle (MNEDC) and Chinese Typical Urban Drive Cycle (CTUDC), which have been used to evaluate the performance of the proposed methodology, the matching-relation between SOH penalty coefficients of battery pack and APU fuel consumption, the SOH of a battery pack can be analysed. Taking the costs of a battery pack and fuel consumption in the whole life cycle as the comprehensive evaluation index, an optimization design method is proposed to match the capacity and the SOH of the battery pack. The simulation results show that when batteries do not need to be replaced, the best economical results will be achieved if the parameters of battery pack and the control strategy parameters are both taken the minimum.
In China, where air pollution has become a major threat to public health, public awareness of the detrimental effects of air pollution on respiratory health is increasing—particularly in relation to haze days. Air pollutant emission levels in China remain substantially higher than are those in developed countries. Moreover, industry, traffic, and household biomass combustion have become major sources of air pollutant emissions, with substantial spatial and temporal variations. In this Review, we focus on the major constituents of air pollutants and their impacts on chronic respiratory diseases. We highlight targets for interventions and recommendations for pollution reduction through industrial upgrading, vehicle and fuel renovation, improvements in public transportation, lowering of personal exposure, mitigation of the direct effects of air pollution through healthy city development, intervention at population-based level (systematic health education, intensive and individualised intervention, pre-emptive measures, and rehabilitation), and improvement in air quality. The implementation of a national environmental protection policy has become urgent.
Range-extended electric vehicles (REEVs) are becoming a development trend of new vehicles. Energy management is one of the core problems in REEVs. The structure and control method of the auxiliary power unit (APU) is determined based on the configuration analysis in this paper. An energy management optimization problem is proposed to solve the power distributions of APUs and batteries in the charge-sustaining (CS) stage of REEVs, which are determined by dynamic programming and pseudo-spectral optimal control, respectively. The results show that different limits of the APU power changing rate significantly influence fuel consumption. To obtain the power changing rate of APUs and to evaluate the energy management optimization method of REEVs, a model of the APU control system is built and verified by a platform test; the dynamic response characteristics and control parameters of the APU are obtained by step-changing conditions. Two types of strategies for tracking APU power are proposed for different power changing rates, and the fuel consumption of REEVs is analyzed in four types of driving cycles. The effect on fuel consumption caused by the power changing rate of the APU is verified.
Range extenders (REs) increase the driving range of electric vehicles (EVs) at the price of additional weight and encumbrance, which are unnecessary in normal urban usage. An innovative concept of an extended range EV is studied here, i.e., an EV that can exploit a rental RE only when necessary to complete a mission. The energy management system not only dispatches power between a battery and an RE but also decides whether or not and when to rent the RE. This paper investigates the optimal control policy that minimizes the monetary cost attained by the user. A simulation analysis carried out in a representative set of scenarios demonstrates the interest of this formulation and shows that mixed-integer convex programming (MI-CP) attains high accuracy in a fraction of the time required by dynamic programming, with a negligible loss of performance. In a real-time framework, the MI-CP core is fed by a forecast of the mission and integrated with a power dispatch strategy that optimizes local performance. The simulation study shows that the resulting policy approaches the global optimum.
Reduction of fuel consumption (FC) and emissions are an indispensable part of automotive industry in recent years, which caused hybrid electric vehicle (HEV) be taken into consideration. The main objective of this paper is developing a predictive optimized control strategy based on traffic condition prediction for minimizing FC and emissions. For this purpose, initially traffic condition is predicted by utilizing driving cycle classification based on its specifications. Then, control strategy based on fuzzy logic controller (FLC) is developed for various driving conditions which their membership function (MF) parameters and rules are tuned by employing genetic algorithm (GA). In the next step, by recognition and prediction of upcoming traffic condition, control strategy is switched between optimized FLCs to enhance the optimal power split between sources and manage the internal combustion engine (ICE) to work in the vicinity of its optimal condition. Finally, the effects of control strategy, mass and aerodynamic parameters on HEV performance and energy usage of components are investigated. Simulation results indicate that proposed approach in real world driving condition reduces emissions and FC significantly.
To achieve the optimal energy allocation for the engine-generator, battery and ultracapacitor of a plug-in hybrid electric vehicle, a novel adaptive energy management strategy has been proposed. Three efforts have been made. First, the hierarchical control strategy has been proposed for multiple energy sources from a multi-scale view. The upper level is for regulating the energy between the engine-generator and hybrid energy-storage system, while the lower level is for the battery and ultracapacitor. Second, a driving pattern recognition based adaptive energy management approach has been proposed. This approach uses a fuzzy logic controller to classify typical driving cycles into different driving patterns and to identify the real-time driving pattern. Dynamic programming has been employed to develop optimal control strategies for different driving blocks, and it is helpful for realizing the adaptive energy management for real-time driving cycles. Third, to improve the real-time and robust performance of the energy management, the previous 100 s duration of historical information has been determined to identify a real-time driving pattern. Finally, an adaptive energy management strategy has been proposed. The simulation results indicate that the proposed energy management strategy has better fuel efficiency than the original and conventional dynamic programming-based control strategies.
Reduction of greenhouse gas emission and fuel consumption as one of the main goals of automotive industry leading to the development hybrid vehicles. The objective of this paper is to investigate the energy management system and control strategies effect on fuel consumption, air pollution and performance of hybrid vehicles in various driving cycles. In order to simulate the hybrid vehicle, the combined feedback–feedforward architecture of the power-split hybrid electric vehicle based on Toyota Prius configuration is modeled, together with necessary dynamic features of subsystem or components in ADVISOR. Multi input fuzzy logic controller developed for energy management controller to improve the fuel economy of a power-split hybrid electric vehicle with contrast to conventional Toyota Prius Hybrid rule-based controller. Then, effects of battery’s initial state of charge, driving cycles and road grade investigated on hybrid vehicle performance to evaluate fuel consumption and pollution emissions. The simulation results represent the effectiveness and applicability of the proposed control strategy. Also, results indicate that proposed controller is reduced fuel consumption in real and modal driving cycles about 21% and 6% respectively.
A fuzzy logic-based energy management strategy for a hybrid power system used in electric vehicles was developed and verified in this paper. First, the topology structure of a hybrid power system was put forward that the ultracapacitors connected with the battery pack in parallel after a bidirectional DC/DC converter. To improve the systematic efficiency, a fuzzy logic-based energy management strategy was designed and the control model was built. We proposed an active electricity management module for the ultracapacitors on the basis of the real-time vehicle velocity. Then, the vehicle model, the interface model of the electrical load and the xPC Target were built with the Simulink/State flow soft. Finally, the hybrid power/energy system-in-loop simulation experiment was carried out to verify the energy management strategy under the Urban Dynamometer Driving Schedule (UDDS) dynamic driving cycle. The results show the proposed fuzzy logic-based energy management strategy can ensure the battery pack working in high efficiency range and show better performance than the traditional logic threshold-based control strategy. The hybrid power system’s electricity economy was improved by 4.1% and the bad influences of the high-current discharging and charging on battery pack were avoided successfully.
Batteries are the most commonly used electrical energy storage devices. The cost and performance of an electric vehicle (EV) are strongly affected by the proper selection of technology, number, and arrangement of the battery cells used. This paper formulates the problem of optimal sizing of the battery unit (BU) for a plug-in EV (PEV), given the specifications of the vehicle, battery cell data, and drive cycle. This paper employs a metaheuristic optimization algorithm to find the optimal size of the BU with the objective of minimizing the overall cost. Feasibility of the optimization results in terms of respecting the constraints is verified via simulation. The developed optimization platform is highly flexible and allows use of different vehicle designs, component data, and drive cycles.
This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
This paper presents a causal optimal control-based energy management strategy for a parallel hybrid electric vehicle (HEV). This strategy not only seeks to minimize fuel consumption while maintaining the state-of-charge of the battery within reasonable bounds but to minimize wear of the battery by penalizing the instantaneous battery usage with respect to its relative impact on battery life as well. This impact is derived by means of a control-oriented state-of-health model. The results indicate that the proposed causal strategy effectively reduces battery wear with only a relatively small penalty on fuel consumption. Ultimately, in terms of cost of fuel and battery replacements, the total cost of ownership over the entire life of the vehicle is significantly reduced.
Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.
This paper is the second of a two part study which quantifies the economic and greenhouse performance of conventional, hybrid and fully electric passenger vehicles operating in Australian driving conditions. This second study focuses on the life cycle greenhouse gas emissions. Two vehicle sizes are considered, Class-B and Class-E, which bracket the large majority of passenger vehicles on Australian roads.Using vehicle simulation models developed in the first study, the trade-offs between the ability of increasingly electric powertrains in curtailing the tailpipe emissions and the corresponding rise in the embedded vehicle emissions have been evaluated. The sensitivity of the life cycle emissions to fuel, electricity and the change in the energy mix are all considered. In conjunction with the total cost of ownership calculated in the companion paper, this allows the cost of mitigating life cycle greenhouse gas emissions through electrification of passenger transport to be estimated under different scenarios. For Class-B vehicles, fully electric vehicles were found to have a higher total cost of ownership and higher life cycle emissions than an equivalent vehicle with an internal combustion engine. For Class-E vehicles, hybrids are found to be the most cost effective whilst also having lowest life cycle emissions under current conditions. Further, hybrid vehicles also exhibit little sensitivity in terms of greenhouse emissions and cost with large changes in system inputs.
Detailed discussions of forecasting methodology and analytical topics concerning short-term energy markets are presented. Major assumptions necessary to make the energy forecasts are also discussed. Supplementary analyses of topics related to short-term energy forecasting are also given. The discussions relate to the forecasts prepared using the short term integrated forecasting system. This set of computer models uses data from various sources to develop energy supply and demand balances. Econmetric models used to predict the demand for petroleum products, natural gas, coal, and electricity are discussed. Price prediction models are also discussed. The role of oil inventories in world oil markets is reviewed. Various relationship between weather patterns and energy consumption are discussed.
The fuel economy and all-electric range (AER) of hybrid electric vehicles (HEVs) are highly dependent on the onboard energy-storage system (ESS) of the vehicle. Energy-storage devices charge during low power demands and discharge during high power demands, acting as catalysts to provide energy boost. Batteries are the primary energy-storage devices in ground vehicles. Increasing the AER of vehicles by 15% almost doubles the incremental cost of the ESS. This is due to the fact that the ESS of HEVs requires higher peak power while preserving high energy density. Ultracapacitors (UCs) are the options with higher power densities in comparison with batteries. A hybrid ESS composed of batteries, UCs, and/or fuel cells (FCs) could be a more appropriate option for advanced hybrid vehicular ESSs. This paper presents state-of-the-art energy-storage topologies for HEVs and plug-in HEVs (PHEVs). Battery, UC, and FC technologies are discussed and compared in this paper. In addition, various hybrid ESSs that combine two or more storage devices are addressed.
Global Smart Logistics Summit
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A look into the world's largest logistics market -China
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The eVito panel van: your holistic solution for electromobility
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What's the life expectancy of my car
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Advanced Vehicle Testing Activity -Cold Weather On-road Testing of a 2012 Chevrolet Volt
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