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ABSTRACT This paper introduces a novel hybrid approach, termed ZOA‐SNN, for fault detection and identification in converter‐interfaced microgrids. By integrating the Zebra Optimization Algorithm (ZOA) with Spiking Neural Network (SNN) technology, the proposed method provides a comprehensive solution suitable for both grid‐connected and autonomous microgrid operation scenarios. The technique effectively isolates faults in the microgrid while maintaining operation continuity, particularly in islanded conditions. When operating in grid‐connected mode, distributed generators (DGs) provide electricity as needed. When the grid is not available, power sharing amongst DGs is controlled by voltage angle droop control. By isolating malfunctioning portions, the proposed protection system reduces load shedding, while DG control guarantees smooth islanding and resynchronization. Evaluation on the MATLAB platform demonstrates the superior performance of the proposed technique compared to existing algorithms such as Augmented Lagrangian Particle Swarm Optimization (ALPSO), Graph Convolutional Network (GCN), and Buffalo Optimization (BO). With an accuracy, recall, precision, and F1‐score reaching 98.5%, 99.2%, 99.1%, and 99.1%, respectively, the ZOA‐SNN approach excels in fault detection and classification. Additionally, it significantly reduces computation times for parameter calculation, enhancing efficiency in microgrid control systems. These results highlight the innovation and advantages of the ZOA‐SNN approach in enhancing the reliability and efficiency of fault detection systems in microgrid environments. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations. [ABSTRACT FROM AUTHOR]

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively. [ABSTRACT FROM AUTHOR]

The reliance of the commercial transportation industry on fossil fuels has long contributed to pollutant and greenhouse gas emissions. Since full electrification of medium- and heavy-duty vehicles faces limitations due to the large battery capacity required for extended driving ranges, this study explores a Range-Extended Electric Vehicle (REEV) for medium-duty Class 6 pick-up and delivery trucks. This hybrid architecture combines an electric powertrain with an internal combustion engine range-extender. Maximizing the efficiency of REEVs requires an Energy Management Strategy (EMS) to optimally split the power between the two power sources. In this work, a hierarchical EMS is developed through model-based design and validated via Hardware-In-The-Loop (HIL) simulations. The proposed EMS demonstrated a 7% reduction in fuel consumption compared to a baseline control strategy, while maintaining emissions and engine start frequency comparable to a benchmark globally optimal EMS obtained with dynamic programming. Furthermore, HIL results confirmed the strategy's real-time implementation feasibility, highlighting the practical viability of the controller. This research underscores the potential of REEVs in significantly reducing emissions and fuel consumption, as well as providing a sustainable alternative for medium-duty truck applications. [ABSTRACT FROM AUTHOR]

A significant challenge in utilizing hydrogen in conventional internal combustion engines is achieving a balance between NOx emissions and brake power output. A lean premixed charge (Lambda ≈ 2.5) allows for efficient and stable combustion with minimal NOx emissions. However, this comes at the cost of reduced power density due to the higher air requirements of the thermodynamic process. While supercharging can mitigate this drawback, it introduces increased complexity, cost, and size. An intriguing alternative is the 2-stroke cycle, particularly in an opposed piston (OP) configuration. This study presents the virtual development of a single-cylinder 2-stroke OP engine with a total displacement of 0.95 L, designed to deliver 25 kW at 3000 rpm. Thanks to its compact size, high thermal efficiency, robustness, modularity, and low manufacturing cost, this engine is intended for use either as an industrial power unit or in combination with electric motors in hybrid vehicles. The overarching goal of this project is to demonstrate that internal combustion engines can offer a practical and cost-effective alternative to hydrogen fuel cells without significant penalties in terms of efficiency and pollutant emissions. The design of this novel engine started from scratch, and both 1D and 3D CFD simulations were employed, with particular focus on optimizing the cylinder's geometry and developing an efficient low-pressure injection system. The numerical methodology was based on state-of-the-art commercial codes, in line with established engineering practices. The numerical results indicated that the optimized engine configuration slightly surpasses the target performance, achieving 29 kW at 3000 rpm, while maintaining near-zero NOx emissions (<20 ppm) and high brake thermal efficiency (~40%) over a wide power range. Additionally, the cost of this engine is projected to be lower than an equivalent 4-stroke engine, due to fewer components (e.g., no cylinder head, poppet valves, or camshafts) and a lighter construction. [ABSTRACT FROM AUTHOR]

From the collected data of the Central Register of Vehicles, an average of 15,000 vehicles were registered per month in the Greater Poland region in 2020–2022. It should be borne in mind that most of them were conventionally powered cars-spark-ignition or compression-ignition engines, the daily operation of which negatively affects the environment. Institutions responsible for regulating the homologation of passenger vehicles are introducing increasingly stringent emission standards to reduce the harmful effects of vehicles on the environment. In addition, more and more public campaigns are being conducted on the use of cars equipped with alternative propulsion systems, e.g., electric, full-hybrid or increasingly popular hydrogen-powered vehicles. The publication presents forecasts for the replacement of vehicles equipped with spark-ignition and compression-ignition engines with cars equipped with hybrid propulsion systems or electric systems. Tests of vehicles in real operating conditions were carried out in the Poznań agglomeration in an urban area, where the number of vehicles per area is higher than in the case of a nonurban zone. The research objects were both cars equipped with a conventional drivetrain and a hybrid system. The research made it possible to draw up a hybridization index, which was then applied to the number of vehicles registered over the past few years. [ABSTRACT FROM AUTHOR]

A problem has been noted regarding the admixture of fuel to a low viscosity lubricant in hybrid electric vehicles (HEVs). This is very detrimental to the wear and tear of engine operating components. In this study, the operating conditions of HEVs were analysed. Using X-ray fluorescence spectrometry (XRF), engine oils of two different viscosity classes were compared after the operating process and these data were compared with fresh reference samples. Attention was paid to the content of elements such as Ca, Zn, Mo, Sn, Cd, Fe Pb, Si, Cu, and Ni. The depletion of anti-wear additives, as well as the higher content of metallic wear products relative to the operated 5W30 (engine oil), contributed to the overall assessment of the lubricity of the 0W30 oil, as well as to the tribological results. Then, under laboratory conditions, oil samples contaminated with up to 1 to 8% fuel were subjected to rheological (mini AV-X viscometer) and tribological (four-ball tester) tests. The dependence of the local pressure at the metal-to-metal contact point in the kinematic node on viscosity showed the dissimilar nature of the used and fresh oil and the divergence of the domains for the two groups of samples. Increasing the fuel contamination of used oil above 4% drastically reduces the pressure responsible for maintaining the oil film. In order to improve lubricant performance during HEV operation in urban conditions, it was proposed to carry out extra-urban traffic driving in order to evaporate the fuel from the engine oil. A shorter oil change interval is also recommended. [ABSTRACT FROM AUTHOR]

Summary: This paper outlines the development of an interleaved bidirectional DC–DC converter featuring single‐phase high‐frequency isolation and employing two distinct pulse width modulations (PWMs), one for each power flow direction. The analysis of converter stages, expressions for DC voltage gains, and voltage/current stresses in continuous conduction mode are presented. The proposed converter offers several advantages, including a new modulation approach, a current ripple frequency, and voltage in two ports that are four times higher than the switching frequency. Additionally, it entails a reduction in the volume and weight of passive elements and ensures balanced current distribution between the arms. Moreover, the single‐phase transformer offers galvanic isolation between the DC bus and the battery or supercapacitor. An experimental prototype has been implemented in the laboratory to validate the mathematical and theoretical analysis for both energy flow directions under the test conditions, with a converter‐rated power of 2.5 kW, a low voltage side of 180 V, and a high voltage side of 380 V. [ABSTRACT FROM AUTHOR]

Hybrid and electric vehicles have been widely proposed in recent years. Considering the limited capacity of the batteries, the most requirement is designing a motor with high torque density and reasonable efficiency. To enable the motor to operate efficiently, the most operational condition of the vehicle is considered. According to the dominant urban cycle of operation, the electric motor is designed to have optimal performance in this cycle. The outer rotor configuration is adopted to increase the power density of the motor without increasing the electrical loading. A novel outer rotor fractional slot permanent magnet motor with fractional slot winding is introduced and its performance is compared with the Prius 2004 motor a well-known motor. As one of the main challenges of SPM motors is its demagnetization and this is not studied in detail. This paper proposes a comprehensive study of the airgap flux and decreasing the negative effects of harmonics. The proposed motor has 12% more efficiency at rated load compared with the Prius motor. [ABSTRACT FROM AUTHOR]

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model's effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter. [ABSTRACT FROM AUTHOR]

A notable trend in the modern automotive market is the increased interest in hybrid cars. Hybrid cars combine a standard internal combustion engine with an electric motor solution. Research into increasing the energy efficiency of a conventional unit while meeting increasingly stringent exhaust emission standards is becoming a key postulate in this matter. This article discusses an analysis of modifying the fuel used by hybrid vehicles using the example of a selected drive unit equipped with a spark-ignition engine. This effect was tested after the Eco Fuel Shot liquid catalyst was added to the fuel. The research process was carried out in two stages, as follows: in road conditions using the Dynomet road dynamometer; and on the V-tech VT4/B2 chassis dynamometer. Tests were carried out to replicate road tests with a catalytic additive in the fuel. A mathematical model was created and the following energy efficiency parameters of the hybrid vehicle were calculated: the torque of the internal combustion engine, electric motor, and generator; the rotational speeds of the internal combustion engine, electric motor, and generator; the power of the internal combustion engine, electric motor, and generator; the equivalent fuel consumption of the electric motor and generator; the fuel consumption of the internal combustion engine, electric motor, and generator; and the mileage fuel consumption of the internal combustion engine, electric motor, and generator. The results of the tests made it possible to identify the benefits of using the tested liquid catalyst on the operation of the drive system of the analyzed hybrid vehicle. This research will be of benefit to both the demand side in the form of users of this category of vehicles, and the supply side represented by the manufacturers of power units. [ABSTRACT FROM AUTHOR]

A new topology of multi-input bidirectional DC-DC converters is proposed in this paper. The converter has a boost behavior, i.e., the output voltage is higher than the sum of the input voltages. This family of converters is particularly suited for hybrid energy storage systems, where different DC sources are connected together and where the output voltage is significantly higher than the voltage of a single storage. The proposed converter reduces the number of required switches, leading to higher efficiency and reduced complexity compared to traditional n-input converters. The new topology demonstrates superior performance by enabling higher efficiency with fewer components. A dedicated control, based on PI controllers, is provided to ensure stable operation under dynamic conditions. The effectiveness of the proposed solution is tested using experimental results on a four-input 20 A/100 V converter prototype. [ABSTRACT FROM AUTHOR]

This study employed the Life Cycle Assessment (LCA) method to evaluate the greenhouse gas (GHG) emissions of passenger vehicles across 31 provinces in China. It examined the potential of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) in significantly reducing emissions compared to Internal Combustion Engine Vehicles (ICEVs). The findings revealed that HEVs, PHEVs, and BEVs substantially lower emissions under the national average electricity GHG emissions coefficient, with PHEVs being the most effective, reducing emissions by over 30%. However, disparities exist at the provincial level due to varying electricity structures, with BEVs not fully effective in reducing emissions in about ten provinces, including Liaoning. The study further categorized provinces into six groups using the K-means method to guide the promotion of different types of New Energy Vehicles (NEVs). Sensitivity analysis highlights the critical role of reducing electricity emission coefficients and adjusting PHEV's utility factor in achieving emission reductions. The study also notes that battery replacements in PHEVs and BEVs during their lifecycle could impede emission reduction in most provinces. To maximize the environmental benefits of NEVs, there is a need to move to a greener electricity mix. At the same time, strategies for promoting NEVs should vary for different provinces. [ABSTRACT FROM AUTHOR]

The goal is to develop an adaptive energy management method that can predict driving circumstances, reducing stress on the primary storage battery in a hybrid electric vehicle (HEV). To improve battery lifespan and overall efficiency, power allocation among multiple energy storage sources (ESS) must go beyond traditional rule-based schemes. This paper offers a promising solution to address these challenges. Standardized driving cycles, such as the Urban Dynamometer Driving Schedule (UDDS), are used as load torque to evaluate the power demand in hybrid electric vehicles (HEV). First, feature extraction techniques improve the extraction of important features from historical driving cycle data, thereby facilitating the long short-term memory (LSTM) based on multistep velocity predictors using a warm-up algorithm. This predicted future velocity profile serves as input to the HEV system. Second, within the hybrid electric vehicle (HEV) system, the battery is linked to a modified interleaved DC-DC boost converter. The results show that this converter affects factors such as ripple, converter loss of 29%, state of charge (SOC) level, and efficiency improvement to 98%. Finally, the energy management system (EMS) with the modified rule-based scheme is implemented with neural networks for optimization. This EMS is responsible for continuously adjusting the power allocation. Simulation results show a significant average reduction of about 33.33% at peak battery level, thereby extending battery life. Compared with conventional rule-based EMS methods, the proposed strategy significantly improves efficiency by reducing the total energy loss by about 10.36%. These results emphasize the reliability and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

At present, the integration of microgrids into power systems presents significant power quality challenges in terms of the rising adoption of nonlinear loads and electric vehicles. Ensuring the stability and efficiency of the electrical network in this evolving landscape is crucial. This paper explores the implementation of cascading Proportional–Integral (PI-PI) and cascading Fractional-Order PI (FOPI-FOPI) controllers for a Distribution Static Compensator (DSTATCOM) in hybrid microgrids that include photovoltaic (PV) systems and fuel cells. A novel hybrid optimization algorithm, WSO-WOA, is introduced to enhance power quality. This algorithm leverages the strengths of the White Shark Optimization (WSO) algorithm and the Whale Optimization Algorithm (WOA), with WSO generating new candidate solutions and WOA exploring alternative search areas when WSO does not converge on optimal results. The proposed approach was rigorously tested through multiple case studies and compared with established metaheuristic algorithms. The findings demonstrate that the WSO-WOA hybrid algorithm significantly outperforms others in optimizing the PI-PI and FOPI-FOPI controllers. The WSO-WOA algorithm showed an improvement in accuracy, surpassing the other algorithms by approximately 7.29% to 14.1% in the tuning of the PI-PI controller and about 8.5% to 21.2% in the tuning of the FOPI-FOPI controller. Additionally, the results confirm the superior performance of the FOPI-FOPI controller over the PI-PI controller in enhancing the effectiveness of the DSTATCOM across various scenarios. The FOPI-FOPI provided controller a reduced settling time by at least 30.5–56.1%, resulting in marked improvements in voltage regulation and overall power quality within the microgrid. [ABSTRACT FROM AUTHOR]

This research study employs a comparative Multi-Criteria Decision-Making (MCDM) approach to select optimal thermoplastic materials for hybrid vehicle battery packs in the automotive industry, addressing the challenges posed by high-temperature environments. Through a detailed evaluation of materials based on criteria such as thermal stability, mechanical strength, chemical resistance, and environmental impact, the research identifies materials that enhance battery efficiency, longevity, and vehicle performance. Utilizing SWARA-ARAS, SWARA-EDAS, and SWARA-TOPSIS methods, the study systematically assesses and ranks various polymers, providing recommendations that prioritize safety, performance, and sustainability. The findings offer valuable insights for manufacturers in making informed material selection decisions, contributing to the advancement of sustainable automotive technologies. This research not only highlights the importance of material selection in the context of hybrid vehicle battery packs but also sets a foundation for future studies to explore emerging materials and decision-making frameworks, aiming to further enhance the efficiency and sustainability of hybrid vehicles. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries currently represent the most suitable technology for energy storage in various applications, such as hybrid and electric vehicles (HEVs and BEVs), portable electronics and energy storage systems. Their wide adoption in recent years is due to their characteristics of high energy density, high power density and long life cycle. On the other hand, they still face challenges from a safety point of view for the possible faults that could generate several problems, ranging from simple malfunctioning to a dangerous thermal runaway. Overcharge is one of the most critical types of faults, and, depending on the level of abuse, it may trigger a thermal runaway. To prevent high levels of overcharge abuse, some cells include integrated protection devices that cut off the circuit when a critical condition is met. In this paper, the performance of these protection devices is evaluated to assess their effectiveness. The cells were tested at different ambient temperatures and current levels. In the worst-case scenarios, the maximum cell temperature slightly exceeded 70 °C and the State of Charge (SOC) reached a peak of 127% when the Current Interruption Device (CID) was activated. These conditions were not critical, so serious events such as thermal runaway were not triggered. These outcomes confirm the effectiveness of the CID, which always intervenes in maintaining a safe state. However, since it never intervened in the overcharge abuse tests, a specific set up was also used to investigate the operation of the other protection device, the Positive Temperature Coefficient. [ABSTRACT FROM AUTHOR]

This paper proposes a forward conversion topology using a multi-winding transformer known as global modular balancing (GMB) for multiple supercapacitor cells connected in a series of high-voltage applications. The proposed topology can easily be modularized and is not limited to the number of supercapacitors existing in the modules. Moreover, the proposed GMB needs merely one MOSFET switch for each supercapacitor, and a one Pulse width modulation circuit that can control all the switches in the modules. Furthermore, the proposed topology is employed to achieve voltage balancing in a supercapacitor string. The proposed topology balancing performance is validated through MATLAB/Simulink Software. [ABSTRACT FROM AUTHOR]

The development of EVs (Electric Vehicles) as a zero-carbon alternative to fossil fuelpowered transport represents the importance of development in HESS (Hybrid Energy Storage System) and EMS (Energy Management Strategies). The present study focuses on a HESS model based on a parallel full-active configuration that integrates a (Li-ion) battery with a supercapacitor connected by two DC-DC bi-directional converters, are suitable options to power supply an electric vehicle. The EMS governing the HESS emerges as a critical element in the overall performance of EVs. The fuzzy logic control strategies have been widely used in high-level supervision and control; MPPT fuzzy logic is used for controller PV system. A complete study of the HESS; PV system, battery/supercapacitor is tested using MATLAB/Simulink. The simulation results show the performance and efficiency of the battery/supercapacitor for the EVs. Hence, the benefits of battery/supercapacitor use in a hybrid storage system are investigated and analyzed. [ABSTRACT FROM AUTHOR]

For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equivalent circuit model. Firstly, we constructed a fractional-order battery model that includes pseudo-capacitance and determined the values of the circuit elements offline using the least squares method from actual input–output data based on the driving profile of an automobile. Compared to the integer-order battery model, we confirmed that the proposed fractional-order battery model has higher accuracy. Secondly, we constructed a fractional-order Kreisselmeier-type adaptive observer as an observer that performs state estimation and parameter adjustment simultaneously. Applying the general adaptive law to the battery model results in a redundant design with many adjustable parameters, so we proposed an adaptive law that reduces the number of adjustable parameters without compromising the stability of the observer. The effectiveness of the proposed method was verified through numerical simulations. As a result, the high estimation accuracy and convergence of the proposed adaptive law were confirmed. [ABSTRACT FROM AUTHOR]

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm's engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in Li-ion battery State-of-Health (SOH) estimation due to its critical role in ensuring the safe and reliable operation of Electric Vehicles (EVs). Effective energy management and accurate SOH prediction are essential for the reliability and sustainability of EVs. This paper presents an in-depth review of SOH estimation techniques, starting with an overview of seminal methods that lay the theoretical groundwork for battery modeling and SOH prediction. The review then evaluates recent advancements in Machine Learning (ML) and Artificial Intelligence (AI) techniques, emphasizing their contributions to improving SOH estimation. Through a rigorous screening process, the paper systematically assesses the evolution of these advanced methods, addressing specific research questions to evaluate their effectiveness and practical implications. Key findings highlight the potential of hybrid models that integrate Equivalent Circuit Models (ECMs) with Deep Learning approaches, offering enhanced accuracy and real-time performance. Additionally, the paper discusses limitations of current methods, such as challenges in translating laboratory-based models to real-world conditions and the computational complexity of some prospective methods. In conclusion, this paper identifies promising future research directions aimed at optimizing hybrid models and overcoming existing constraints to advance SOH estimation and battery management in Electric Vehicles. [ABSTRACT FROM AUTHOR]

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This article substantiates the possibility of solving a difficult task to offer a reliable estimate of the residual resource of the power plant of a hybrid car, which is exposed during operation to the influence of a complex set of external factors. This task is solved by monitoring the time change of diagnostic parameters and controlling the load–speed mode of the hybrid power plant, as well as using physical and chemical processes that cause the degradation of the traction battery. Methodological principles for assessing the resource of a hybrid power plant of a car have been developed, which are based on the disclosure of cause-and-effect relationships between realized and nominal quality indicators, operating conditions, and indicators of technical condition. The choice of rational solutions for the operation of a hybrid car is given, and the external conditions are determined, under which the residual life of the hybrid power plant will change. The scientific result of the study is a theoretical generalization of the scientific provisions of forecasting the resource of the hybrid power plant of a car. This ensures the efficient use of hybrid vehicles and reduces maintenance costs. [ABSTRACT FROM AUTHOR]

The continuous expansion of the vehicle fleet contributes to escalating emissions, with the transportation sector accounting for approximately 21% of CO2 emissions, based on 2023 data. Focused on reducing emissions and reliance on fossil fuels, the study observes the shift from internal combustion vehicles to electric and hybrid models since 2017. Despite advancements, these vehicles still lack optimal efficiency and suffer from limited range, deterring potential buyers. This article aims to evaluate the range-extending technologies for electric vehicles, emphasizing efficiency, low pollution, and integration compatibility. An algorithm incorporating equations representing mechanical or electrical component curves is developed for Extended-Range Electric Vehicles, facilitating insight into potential range extender behavior. The core objectives of this study involve optimizing the entire powertrain system to ensure peak efficiency. Experimental tests demonstrate that integrating an auxiliary power unit enhances range, with an internal combustion engine generator configuration extending the travel distance by 35.35% at a constant speed. Moreover, with the use of an Equivalent Consumption Minimization Strategy control, the distance traveled increases up to 39.28% on standard driving cycles. The proposed methodology, validated through practical implementations, allows for comprehensive energy analyses, providing a precise understanding of vehicle platform performance with integrated range extenders. [ABSTRACT FROM AUTHOR]

In the past two decades, people have started to develop efficient, clean and safe transportation vehicles. The development of a fuel cell vehicle has become the focus of attention. First, we analyze the common components of a hybrid electric vehicle with a fuel cell. Second, we build a simulation model of the hybrid electric vehicle with a fuel cell. Through the simulation model, we study and analyze the fuel cell and battery under various conditions related to fuel consumption parameters. Through the above-described research, we hope to optimize the parameter structure of the fuel cell hybrid electric vehicle and improve the working efficiency of fuel cells. [ABSTRACT FROM AUTHOR]

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle's ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles. [ABSTRACT FROM AUTHOR]

In recent years, the implementation of Tier 5 emission regulations for tractors has compelled manufacturers to conduct new research. Additionally, the process of hybridization has impacted the agricultural vehicle industry. This study focuses on the design and implementation of an energy management strategy (EMS) for a diesel–electric hybrid electric agricultural vehicle, based on real‐time optimization. Tractor drive systems, including both conventional and hybrid options, were introduced. Simulations were performed to compare fuel consumption and CO2 emissions between the two types of tractors. The data shows that hybrid vehicles produce 8.93% less CO2 than conventional vehicles. Furthermore, the fuel consumption of the tractor decreased from 1,880.2 to 1,712.5 g/km under hybrid operating conditions. [ABSTRACT FROM AUTHOR]

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle's hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, those in close proximity to the sink often experience rapid power depletion, leading to the emergence of hotspot issues. To address this, the concept of a mobile sink (MS) has emerged as a potential solution, effectively mitigating power usage in SNs and thereby extending the network's overall lifespan. Furthermore, many sensor-based applications necessitate specific data collection timeframes, underscoring the necessity of effective strategies. Leveraging rendezvous points (RPs) to enhance network efficiency becomes imperative in enabling the MS to efficiently collect data from all SNs within designated time periods. A sophisticated cost function is employed to strategically determine RPs, considering multiple factors that influence the efficacy of each RP. This process culminates in the selection of RPs, optimizing for the longest path with minimal delays. Through the proposed hybrid mobile vehicle (HMV) method, compared against the prevailing MOOVor method, significant enhancements are observed in terms of sensor coverage and reduced hop count within the network. [ABSTRACT FROM AUTHOR]

To achieve the 8000-h proton exchange membrane fuel cell stack (PEM FCS) life target set by the U.S DoE and promote fuel cell hybrid electric vehicles (FCHEVs) massive introduction in the automotive market, using multi-fuel cell stack (MFCS) systems instead of single-fuel cell stack systems seems to be an interesting solution that deserves to be explored. MFCS systems' concept combines several small FCSs modules instead of using a single high-powered FCS module. The modularity in such systems can be exploited through energy management to improve their durability and extend their good energy-efficiency power range. However, FCSs' multiplicity makes it challenging to implement effective energy management strategies (EMSs). This paper proposes a remaining useful life (RUL) prognostic-based EMS to extend MFCS systems' lifetime while keeping their hydrogen consumption reasonable. For this purpose, a prognostic algorithm is developed to predict PEM FCSs' RUL in real-world automotive application scenarios. Then a rule-based EMS allocates the demand between stacks using prognostic results. The proposed strategy's performance is evaluated on a hybrid MFCS/battery system using Matlab/Simulink's environment. Simulation results show that implementing the proposed strategy instead of conventional EMSs can extend MFCS systems' lifetime by at least a factor of 2.35 while keeping their hydrogen consumption reasonable. © 2001 Elsevier Science. All rights reserved. • A post-prognostic decision-making strategy is proposed for MFCS systems. • RUL predictions are performed in real-world automotive scenarios. • The MFCS system's lifetime is significantly extended with the proposed EMS. • RUL is a suitable parameter for adaptive energy management decision-making. [ABSTRACT FROM AUTHOR]

The deterioration of environment and the proposal of carbon neutrality are causing the transformation of port freight transportation. Fuel cell hybrid trucks (FCHTs) may be a promising solution for port logistic with the characteristics of low speed, heavy duty and frequent start-stops. How to achieve efficient energy management for FCHTs in ports is a key issue, which is still in the initial stage. The objective of this study is to investigate the effects of dynamic programming (DP) strategy on energy management of FCHTs. With the intention of calculating power demands, the FCHT structure and the models of each component are established. In the DP method, the state of charge (SOC) of the battery, the power supplied by the battery, and hydrogen fuel consumption are regarded as the state variable, the decision variable, and the objective function, respectively. The simulation results under two different typical working conditions in ports show that the proposed DP outperforms existing genetic algorithm (GA) and rule-based strategy for reducing hydrogen fuel consumption by over 10%. Moreover, a set of instances among several FCHTs with different parameters and conditions suggest different influences of key feature parameters on hydrogen fuel consumption and SOC. Nearly 50% cost reduction of the FCHT in the "quay crane-yard " (QCY) cycle compared with diesel trucks at the current price indicates wide application prospect of FCHTs. At last, the results under different conditions reconfirm that DP is an excellent strategy to tackle energy management of FCHTs in ports with frequent start-stops and heavy-load scenarios. •Attempt to adopt dynamic programming (DP) into energy management of fuel cell hybrid trucks (FCHTs) in ports. •Two typical working conditions of port freight transportation are investigated. •The impacts of key feature parameters on the energy management strategies (EMSs) are analyzed. •DP has obvious advantages in heavy-load and frequent start-stop conditions in ports. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Road gradients not only affect the actual performance of control strategies but also impact battery life due to the drastic changes in power demands. To balance battery degradation with fuel economy using gradient information, this study proposes a gradient-aware trade-off control strategy. Initially, a vehicle dynamics model and a battery degradation model are established. Based on the characteristics of known road information and remaining driving distance, state of charge planning of the battery is conducted. Subsequently, the Non-dominated Sorting Genetic Algorithm-II is applied for bi-objective optimization, yielding a set of Pareto solutions that represent different levels of energy consumption and battery degradation. Thereafter, by introducing a real-time battery degradation severity factor, an optimized bias coefficient is obtained, which adjusts in accordance with the gradient changes. Through the optimization of the bias line, the optimal bias solution set under different working conditions is determined, achieving the optimal control for power system. The fuel economy of the proposed strategy is improved by 6.8% relative to the mileage adaptive Equivalent Consumption Minimization Strategy, and the battery degradation inhibition is improved by 9.3%. After real-world conditions validation, the proposed strategy demonstrates good performance in both economic efficiency and battery life. [Display omitted] • The global optimal SoC is planned according to the characteristic of the energy required by the slope. • Battery degradation and energy consumption are considered for generating Pareto optimal solutions. • Optimized bias lines are introduced to adaptively trade-off and select the optimal solutions for different conditions. [ABSTRACT FROM AUTHOR]

As an energy-efficient transportation innovation, hybrid vehicles (HVs) have the potential to minimize carbon emissions and address environmental issues. The study is novel as it presents significant theoretical contributions within the theory of planned behavior (TPB) framework. Firstly, it integrates the perceived seriousness of environmental problems as a predictor, enhancing understanding of individual responses to environmental concerns. Secondly, it expands the TPB model by introducing willingness to pay for HVs as a moderator, acknowledging the economic aspect of adopting environmentally friendly technologies. These additions enrich the TPB framework, offering insights into the interplay between psychological determinants and economic considerations in behavior change. The study therefore aims to extend the TPB by incorporating two key elements: perceived seriousness of environmental issues and willingness to pay for HVs. We find that perceived seriousness of problems in the environmental has positive impact on attitude toward adoption of HVs, subjective norms, perceived behavioral control, intentions to adopt and actual use of HVs. TPB constructs have positive impact on intentions to adopt and actually use HVs. Moreover, intentions to adopt HVs also positively influence the actual use of HVs. Moreover, the results show that the serial mediations are also in play, whereby perceived seriousness of environmental issues influences TPB constructs, which influence intentions to adopt HV which in turn influence the actual use of HVs. We also find that willing to pay for HVs plays an enhancing role as a moderator in the TPB model. [ABSTRACT FROM AUTHOR]

Rare earth elements (REEs) are fundamental for various modern technologies and industrial applications. One significant application of REEs is in the production of neodymium-iron-boron (NdFeB) magnets, which are key components in electric vehicles (EVs), wind turbines, and electronic devices. These applications play a crucial role in driving the ecological and digital transition, highlighting the significance of REEs as strategic materials. With the dominance of very few countries in the REEs global supply and the rising of EVs demand, several concerns regarding resource availability, supply chain security, and price volatility have heightened the importance of efficient NdFeB magnet recycling, especially in Europe. This study assessed the elemental recycling potential of REEs from EV components through collaboration with authorized treatment facilities and metal recyclers in Italy. The study focused on three representative electric vehicles: a compact car, a van, and a hybrid vehicle. NdFeB magnets were found in various components, including the electric drive motor, air conditioning system, electric power steering, alternator, and electric gear box. The content of NdFeB magnets and REEs inside these components has been determined and economic feasibility of their recycling has been estimated by considering the intrinsic value of the raw materials contained. Despite being preliminary results, the economic value of REEs and Cu recoverable attested a promising potential for recycling, while the direct dismantling of magnets from the engine proves economically unviable for the studied components. Therefore, the study emphasizes the need for the development of specific recycling processes such as demagnetization and mechanical processing of the motors. The study also analysed the dismantling times of the target components from the vehicle and their relative economic impact on the potential for recovery. [ABSTRACT FROM AUTHOR]

The torsional vibration of the shaft system in hybrid car engines has a significant impact on the overall performance of the vehicle, and it is more complex in hybrid cars compared to traditional cars. Traditional methods for torsional vibration analysis of shaft systems have significant limitations and cannot handle nonlinear and transient problems. To explore the torsional vibration characteristics of hybrid vehicle shaft systems, a simplified engine shaft system torsional vibration equivalent model is innovatively constructed. In addition, a method for quickly determining the confidence level of the torsional vibration equivalent model is proposed. Additionally, the transient dynamic characteristics of a multi-body dynamics model containing a dual mass flywheel are analyzed in depth using the time-domain solver of AVL-exact PU. The results demonstrated that the simulation of 4th and 6th harmonics resonated at critical speeds of 4,195 rpm and 2,771 rpm, respectively, with angular displacement amplitudes of 0.141 deg and 0.047 deg. In fact, resonance was measured at 4,250 rpm and 3,040 rpm, with amplitudes of 0.14 deg and 0.052 deg. These two were basically consistent in key parameters. When the shaft model was started under operating conditions, the amplitudes of harmonics 1, 2, and 4 were basically consistent below 750 rpm, and there were slight differences after 750 rpm. Therefore, the AVL-based engine torsional vibration simulation model constructed has high credibility. [ABSTRACT FROM AUTHOR]

The rapid transformation from conventional vehicles to electric vehicles (EVs) holds promise for enhancing air quality, reducing CO2 emissions, revitalizing the renewable energy market, and enhancing countries' economies. This study investigated the performance of EVs in Jordan and the key challenges hindering their rapid development. It implemented a structured questionnaire of 202 EV owners/drivers and 15 interviews with technicians at many service centers. Findings revealed that Jordan leads among MENA countries in Battery Electric Vehicles(BEVs) market share and growth rate, with 52% market share of BEVs new cars in 2023, projected to surpass 80% by 2025. This rapid development is tied up with many internal and external influences; government backing, high fossil fuel prices, and tax exemptions are common causes that enhanced the market of EVs. Respondents highlighted prevalent issues of BEVs, including the rapid battery depletion, low battery capacity, electric inverter malfunctions, battery overheating, low performance in hot and cold climates, and inflating costs. Finally, this study recommends educational institutions to offer targeted training programs for EV maintenance and service, and the government should provide more incentives for this industry, and combat the influx of counterfeit charging accessories, batteries, and spare parts. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids' utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network. [ABSTRACT FROM AUTHOR]

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Reducing CO2 emissions is becoming a particularly important goal for motorcycle manufacturers. A fully electric transition still seems far away, given the difficulties in creating an electric motorcycle with an acceptable range and mass. This opens up opportunities for the application of hybrid powertrains in motorcycles. Managing mass, cost, and volume is a challenging issue for motorcycles; therefore, an MHEV architecture represents an interesting opportunity, as it is a low-complexity and low-cost solution. Firstly, in this work, an adequate sizing of the powertrain components is studied for the maximum reduction in fuel consumption. This is performed by analyzing many different system configurations with different hybridization ratios. A 1D simulation of the motorcycle traveling along the homologation cycle (WMTC) is performed, and the powerunit use strategy is optimized for each configuration using the Dynamic Programming technique. The results are analyzed in order to highlight the impact of kinetic energy recovery and engine load-point shifting on fuel consumption reduction. The results show the applicability of MHEV technology to road motorcycles, thus providing a useful tool to analyze the cost/benefit ratio of this technology. The developed methodology is also suitable for different vehicles once a specific test cycle is known. [ABSTRACT FROM AUTHOR]

The automotive industry faces development challenges due to emerging technologies, regulatory demands, societal trends, and evolving customer mobility needs. These factors contribute to a wide range of vehicle variants and increasingly complex powertrains. The layout of a vehicle is usually based on standardized driving cycles such as WLTC, gradeability, acceleration test cases, and many more. In real-world driving cycles, however, this can lead to limitations under certain boundary conditions. To ensure that all customer requirements are met, vehicle testing is conducted under extreme environmental conditions, e.g., in Sweden or Spain. One way to reduce the development time while ensuring high product quality and cost-effectiveness is to use model-based methods for the comprehensive design of powertrains. This study presents a layout methodology using a top-down approach. Initially, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet with specific test cases. An overall vehicle model with detailed thermal sub-models is developed to evaluate the different requirements. A baseline design for a C-segment plug-in hybrid vehicle was developed as part of the FVV research project HyFlex-ICE using standardized test cases, highlighting the influence of customer profiles on the design outcome through varying weighting factors. The target customer's design is analyzed in four real driving scenarios, considering variations in parameters such as the ambient temperature, traffic, driver type, trailer pulling, and battery state-of-charge, to assess their influence on the target variables. In the next step, the potential of hardware technologies and predictive driving functions is examined in selected driving scenarios based on the identified constraints of the baseline design. As a result, four application-specific technology packages (Cost neutral, Cold country, Hot country, and Premium) for different customer requirements and sales markets are defined, which, finally, demonstrates the applicability of the holistic methodology. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system's responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. [ABSTRACT FROM AUTHOR]

During the process of moving, the power splitting hybrid vehicle is required to start the engine to transfer from pure electric mode to hybrid drive mode. According to the structural characteristics of the system, the engine starting process was divided into four stages, the engine starting process was established, and a multi-stage engine starting coordination control strategy was designed. In the engine reverse-drag process, the coordination control strategy was transformed into the optimal rotation rate tracker problem. In order to solve the load torque required in the tracking problem, a reduced-order observer was designed. Finally, the validity of the coordination control strategy was verified on the simulation platform of the electro-mechanical composite transmission system. The feasibility of the coordination control strategy was verified by hardware-in-the-loop simulation. The results show that the engine start coordination control strategy could achieve steady and fast engine start control. The maximal impact of the whole vehicle is reduced from 30 to 2.5 m/s3. [ABSTRACT FROM AUTHOR]

Featured Application: The application of this work is fuel cell hybrid electric vehicles. It solves the problem of balancing between the economy of the vehicle and the lifetime of the energy sources during the power distribution process. The energy management strategy plays an essential role in improving the fuel economy and extending the energy source lifetime for fuel cell hybrid electric vehicles (FCHEVs). However, the traditional energy management strategy ignores the lifetime of the energy sources for good fuel economy. In this work, an adaptive equivalent consumption minimization strategy considering performance degradation (DA-ECMS) is proposed by incorporating fuel cell and battery performance degradation models and establishing an optimal covariate predictor based on a long short-term memory (LSTM) neural network. The comparative simulations show that, compared with the adaptive equivalent consumption minimization strategy (A-ECMS), the DA-ECMS reduces the fuel cell stack voltage degradation by 17.1%, 23.2%, and 16.6% for the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), the China Light-Duty Vehicle Test Cycle (CLTC), and the New European Driving Cycle (NEDC), respectively, and the corresponding battery capacity degradation is reduced by 5.1%, 11.1%, and 11.2%. The average relative error between the hardware-in-the-loop (HIL) test and simulation results of the DA-ECMS is 5%. In conclusion, the proposed DA-ECMS can effectively extend the lifetime of the fuel cell and battery compared to the A-ECMS. [ABSTRACT FROM AUTHOR]

A genetically optimized fuzzy control algorithm is used to realize the parallel hybrid system's real-time energy distribution between the engine and the electric motor. This paper first adopts fuzzy control to improve the robustness and real-time performance of the entire system. Then, a membership function optimization method based on a genetic algorithm is proposed by simulating the working state of CYCUDDS. Simulation experiments show that compared with unoptimized fuzzy control, the improved fuzzy controller can improve the vehicle's fuel economy and prevent continuous battery discharge, thereby improving battery endurance. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic consensus-based economic dispatch (ED) algorithm utilizing the Alternating Direction Method of Multipliers (ADMM) to optimize real-time pricing and generation/demand decisions within a decentralized energy management framework. The increasing complexity of modern energy markets, driven by the proliferation of Distributed Energy Resources (DER) and variable demands from hybrid electric vehicles, necessitates a departure from traditional centralized dispatch methods. This research proposes a novel ADMM-based solution tailored for non-responsive and responsive demand units that integrates demand response mechanisms to adaptively manage real-time fluctuations while enhancing security and privacy through distributed data management. The testing of the algorithm on the IEEE 39 bus system under various load conditions over 24 h demonstrated the algorithm's effectiveness in handling traditional and renewable energy sources, particularly highlighting the economic benefits of shifting controllable loads to periods of low-cost renewable availability. The findings underscore the algorithm's potential to reduce energy costs, enhance energy efficiency, and offer a scalable solution across diverse grid systems, contributing significantly to advancing global energy policy and sustainable management practices. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of five different vehicle technologies with a particular emphasis on fuel cell vehicles (FCVs) in comparison to conventional gasoline and diesel ICEs, hybrid electric vehicle (HEV), and electric vehicle (EV) technologies. Key parameters in comparison included greenhouse gas emissions (GHG), social cost of carbon (SCC), energy efficiency, fuel consumption, fuel price, driving range, and total cost of ownership (TCO). A normalization approach is employed to rank the technologies across parameters, providing valuable insights for sustainable transportation decision‐making. FCV technology showed the highest average performance (5.343/10) followed by EV technology (4.760/10). The paper also presents current and anticipated hydrogen demand and supply centers, accompanied by a brief overview of commercially available fuel cell technologies. Finally, the sustainability of FCV technology as well as economic, legal, and infrastructural limitations associated with the widespread adoption are also discussed. [ABSTRACT FROM AUTHOR]

In order to maintain the battery SOC, the fuel cell power will fluctuate dramatically, as well as frequent start-stop, which will greatly increase the life attenuation of the fuel cell and reduce the durability. An optimization-based energy management strategy with a real-time model predictive control and pontryagin's maximum principle for FCHEV is proposed in this paper, both the fuel economy and the fuel cell durability are considered in the optimization. A novel model predictive control is studied to achieve energy distribution. After the calculation of predicted speed sequence through back propagation neural network, pontryagin's maximum principle is introduced to solve the optimal control problem in each prediction horizon and obtain the ideal control strategy. In addition, the fuel cell degradation model is introduced in the modeling process, the minimum power point of the fuel cell system is designed to improve the fuel economy and durability of the fuel cell. Compared with the rule-based strategy, the proposed MPC strategy has better performance to reduce the total equivalent hydrogen consumption, which can save up to 8.44% in the test case of the mid-size fuel cell passenger car while maintaining the stability of the battery's state of charge. [ABSTRACT FROM AUTHOR]