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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...
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... With an energy resolution as low as 10 −10 eV, these systems offer an unprecedented level of sensitivity and precision. Such refinement would be invaluable for a spectrum of scientific and technological applications, including sophisticated spectroscopic analysis, quantum optical experiments, and the innovation of cutting-edge optoelectronic devices [40][41][42]. ...
In this work, we study the resonant tunneling (RT) of electrons and H atoms in double-barrier (DB) systems. Our numerical calculations directly verify the correspondence between the resonant tunneling energies and the energy levels of quasi-bound states (QBS) within the double barriers. Based on this, in-depth analyses are carried out on the modulation of QBS energy levels and numbers which show step variation with the inter-barrier spacing. The mathematical criterion for the existence of QBS is derived, and the impacts of the barrier width and barrier height on QBS levels are investigated. Taking the rectangular double-barrier as an example, we have studied the manipulation of electronic structures and optical properties of the inter-barrier region (quasi-potential well) by tuning the inter-barrier spacing (width of quasi-potential well). Atom-like optical absorption features are found in the range of infrared to visible spectrum, which can be continuously tuned by the variation of quasi-potential well width. The potential application of double-barrier nanostructures in ultrahigh-precision detection of electromagnetic radiations is demonstrated.
... 3,4 Battery management systems (BMS) play a crucial role in optimizing battery performance by continuously monitoring and analyzing battery states. [4][5][6] The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. To accurately estimate battery capacity degradation, a precise SOH estimate is essential to reflect the real-time status and performance of the batteries in the BMS. ...
Battery Management Systems (BMS) are essential for optimizing battery performance and extending lifespan through continuous monitoring and decision-making via control sensors. The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. However, one of the main challenges in the BMS domain development is finding accurate and effective algorithms for battery SOH prediction, especially for electric vehicles and grid-connected energy storage systems. This study introduces a new SOH prediction method using wavelet-convolutional neural regression networks (CNRN) algorithms. The methodology involves extracting detailed frequency profiles from Electrochemical Impedance Spectroscopy (EIS) data, which are processed through wavelet transformation to capture both time and frequency domain features. These transformed profiles are then input into the CNRN model for SOH prediction. The results demonstrate improved SOH prediction accuracy with EIS frequency profiles, evidenced by a reduction in root mean square error (RMSE) compared to the standard EIS profile. This improvement is due to the fact that the wavelet-CNRN algorithm efficiently captures both the time and frequency features of the battery impedance. Moreover, the performance of the proposed algorithm demonstrated robustness in early end-of-life (EOL) prediction, thereby enhancing the reliability and safety of BMS functions.
... A lot of research is currently being done on electrical energy storage systems [5][6][7][8][9], including the optimization of vehicle energy management [9][10][11][12], state-of-charge (SOC) estimation techniques [13][14][15][16][17] and cell aging [18,19]. In addition, the scientific literature contains several works that study the value of energy storage units to improve the performance of multi-source systems dedicated to transportation applications [20][21][22]. ...
... The criterion to be minimized is the total hydrogen (H 2 ) consumption for a given mission. The control objective function is to minimize hydrogen consumption [1]: (12) whereṁ h is the instantaneous hydrogen consumption, which depends on the PEMFC power supplied (P FC ) and its total efficiency (η FC ); t k is the current time step, and H p is the prediction length. The total demand power P dem is given by: ...
To enhance the fuel efficiency of fuel cell hybrid electric vehicles (FCHEVs), we propose a hierarchical energy management strategy (HEMS) to efficiently allocate power to a hybrid system comprising a fuel cell and a battery. Firstly, the upper-layer supervisor employs a fuzzy fault-tolerant control and prediction strategy for the battery and fuel cell management system, ensuring vehicle stability and maintaining a healthy state of charge for both the battery and fuel cell, even during faults. Secondly, in the lower layer, dynamic programming and Pontryagin’s minimum principle are utilized to distribute the necessary power between the fuel cell system and the battery. This layer also incorporates an optimized proportional-integral controller for precise tracking of vehicle subsystem set-points. Finally, we compare the economic and dynamic performance of the vehicle using HEMS with other strategies, such as the equivalent consumption minimization strategy and fuzzy logic control strategy. Simulation results demonstrate that HEMS reduces hydrogen consumption and enhances overall vehicle energy efficiency across all operating conditions, indicating superior economic performance. Additionally, the dynamic performance of the vehicle shows significant improvement.
... Currently, the main energy management strategies (EMSs) are divided into rule-based (RB) and optimization-based (OB) strategies [2][3][4]. Rule-based strategies are widely applied in practice due to their simple and intuitive design, low computational requirements, good real-time performance, robustness, and ease of implementation and adjustment. Rule-based EMS typically relies on a set of predefined rules to execute, generating control commands based on key variable thresholds [5]. ...
The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine’s operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy.
... • Energy Management System (EMS): The EMS plays a crucial role in controlling and optimizing the power flow between the internal combustion engine and the electric motor. It determines when to use electric power, when to rely on the internal combustion engine, and when to switch between different modes to maximize efficiency and performance [20]. ...
This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.
... While charging times can be longer compared to refueling a gasoline car, the growing network of fast-charging stations is mitigating this issue. Overall, BEVs represent a significant step towards sustainable transportation, providing an environmentally friendly alternative with ongoing improvements in technology and infrastructure [3,4]. ...
... Despite these drawbacks, HEVs offer a practical compromise for consumers seeking be er fuel economy and lower environmental impact without the range limitations associated with BEVs. Unlike BEVs, HEVs do not require plugging in, making them a potentially a ractive permanent solution if targeted marketing strategies and buying or retrofi ing policies are implemented as soon as possible to accelerate the transition project [3,4]. PHEVs offer the advantage of operating on both electric power and gasoline, allowing drivers to enjoy the benefits of electric driving while maintaining the extended range provided by a gasoline engine (e.g., Mercedes-Benz GLE 350 de PHEV, electric range around 100 km). ...
... Furthermore, PHEVs require access to charging infrastructure, which can be a limitation in regions with fewer charging stations. In spite of these challenges, PHEVs offer a flexible solution for those looking to minimize their carbon footprint without compromising range and convenience [3,4]. ...
The significance of sustainable mobility transition projects extends beyond borders. Transportation, particularly passenger vehicles, is a crucial sector in achieving sustainability. Therefore, prioritizing sustainable green mobility has led to the inclusion of alternative solutions, with a focus on accelerating the shift towards electric vehicle (EV) technologies and implementing a ban on the sale of new petrol and diesel cars in all European countries by 2035. Italy has been making progress in this area as the country seeks to address environmental concerns, reduce emissions, and promote sustainable transportation. However, compared to other European countries in 2024, Italy still has a long way to go to achieve a sustainable market share. In this regard, this article aims to address several questions related to the promotion and scaling up of the electric mobility transition project in Italy, taking into account the current situation. Specifically, it seeks to identify internal and external factors associated with this technology ecosystem, along with their relative importance. To conduct this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was conducted to identify the factors, which was followed by the analytical hierarchy process (AHP) methodology to determine their priority and importance. A total of 8 internal factors and 14 external factors were analyzed, and their overall priority was determined. This study reveals that it is crucial to capitalize on the opportunities and strengths related to technology ecosystems while effectively mitigating the threats and technological limitations in order to scale up technology adoption. In particular, strengths S1 and S3 were given the highest overall priority scores, suggesting that they are the most important factors to leverage for the successful adoption of the technology. These prioritized factors and subfactors are crucial for expediting the transition process and can influence consumers’ decisions. However, without a substantial increase in consumer understanding and knowledge of these technologies, public education campaigns will be necessary. The significance of this study is paramount, and its results can contribute to the continuous enhancement in the formulation of practical plans and regulations to promote sustainable transportation, taking into account the identified factors.
... The influence of automotive technology on the environment encompasses both beneficial and detrimental aspects. The positive contributions include the adoption of electric vehicles (EVs), the utilization of advanced materials and lighter designs, the implementation of smart energy management systems, and the reduction of noise pollution (Jacobson & Delucchi, 2011;Zhang & Xu, 2022;Munsi & Chaoui, 2024;Campello-Vicente et al., 2017) Conversely, the negative aspects involve the production and recycling of batteries, the establishment of charging infrastructure, and challenges associated with electronic waste (Peters et al., 2017;. Ma et al., 2021;Costa et al., 2021;Modoi & Mihai, 2022). ...
This study elucidates the findings derived from the implementation of the integrated AHP-PROMETHEE multi-criteria decision-making method aimed at selecting the most suitable electric vehicle for urban transport. The AHP methodology facilitated the identification of critical criteria influencing the selection of an electric vehicle for urban use, including factors such as price, battery warranty, charging speed, acceleration to 100 km/h, maximum speed, engine power, efficiency, battery performance, and mileage. The subsequent application of the PROMETHEE method allowed for the ranking of nine electric vehicles based on these established criteria. Each vehicle alternative was assessed regarding its capacity to fulfill the specified requirements and preferences of the decision-makers. The analysis revealed that the Mini Cooper E emerged as the model that most effectively aligns with the prioritized criteria. This vehicle is deemed the optimal selection for urban transport needs, considering all pertinent factors. The decision to select a specific electric vehicle is expected to influence the efficiency, sustainability, and economic viability of the urban transportation system. Furthermore, it is advisable to maintain ongoing surveillance of advancements in electric vehicle technology and charging infrastructure to ensure that the selected model continues to represent the best option in the future.
... Therefore, considering the advantages and disadvantages of each energy source, an energy management system (EMS) is necessary for increasing the hybrid energy storage system (HESS) lifespan and save fuel of the primary source. To achieve the aforementioned targets, many optimizationbased strategies have been developed in the literature [7]. In this context, the real-time optimization, as the equivalent consumption minimization strategy (ECMS) [8], [9] and model predictive control (MPC) [10], presents better performance under different drive cycles. ...
This paper proposes an energy management system (EMS) applied to a series hybrid vehicle (SHV) powered by an internal combustion engine (ICE) and a hybrid energy storage system (HESS) consisting of chemical batteries and supercapacitors. The EMS enables power sharing among the ICE and the HESS under a fully active topology to improve control flexibility, considering that the ICE-generator set and HESS efficiencies are limited to a specific power injection operating range and transient responses. In this context, power sharing is performed using S-shaped functions that focus on maximizing supercapacitor usability to reduce fuel consumption and battery current stress. The advantage of the S-shaped function lies on its easy configuration and reduced number of control parameters. Additionally, meta-heuristic optimization is used to tune the S-shaped functions according to the sources requirements for optimal performance in standard driving cycles, while Lyapunov's indirect method performs the stability analysis of the control strategy. Finally, experimental and computational simulations are accomplished to evaluate the effectiveness of the proposed EMS compared with traditional methods and an optimal approach.
... This section delves into the specific characteris-tics of the primary power sources used in these vehicles, namely fuel cells and various energy storage systems. By understanding the unique attributes and operational dynamics of each power source, we can better appreciate their roles and synergies within the vehicle's powertrain [31]. ...
... Although these strategies are often termed EMSs, this paper uniformly refers to them as algorithms for EMSs to align their objectives and methodologies. RBS can be subdivided into deterministic rulebased strategy (DRBS) and fuzzy rule-based strategy (FRBS) [31]. RBS relies on human intelligence and experience to design control rules, typically without knowing beforehand of a specific driving cycle [73]. ...
... This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4 FIGURE 6: Categorization of Energy Management Strategies for FC-HEV [31], [73]. and the state of charge is high, the ESS supplies most or all of the required power. ...
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 study explores advances in the efficiency of power conversion systems in electric vehicles (EVs), a crucial aspect to optimize their performance and range. Through a qualitative literature review, DC-DC converters, DC-AC inverters and fast charging systems are examined, highlighting the impact of advanced materials such as silicon carbide (SiC) and gallium nitride (GaN) on energy efficiency. The findings suggest that the use of these materials reduces thermal losses and enables higher power density, benefiting the autonomy of EVs. In addition, it emphasizes the importance of thermal management and advanced control algorithms to optimize power flow and extend component lifetime. It concludes that innovations in semiconductors, together with the implementation of thermal management and control techniques, are key to increasing the viability and competitiveness of EVs in the global market. However, challenges remain, such as the high cost of materials and the need to develop an adequate charging infrastructure for mass adoption.
