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This study proposes the use of a Quadruple Deep Q-Network (QDQN) for optimizing the energy management strategy of Plug-in Hybrid Electric Vehicles (PHEVs). The aim of this research is to improve energy utilization efficiency by employing reinforcement learning techniques, with a focus on reducing energy consumption while maintaining vehicle performance. The methods include training a QDQN model to learn optimal energy management policies based on vehicle operating conditions and comparing the results with those obtained from traditional dynamic programming (DP), Double Deep Q-Network (DDQN), and Deep Q-Network (DQN) approaches. The findings demonstrate that the QDQN-based strategy significantly improves energy utilization, achieving a maximum efficiency increase of 11% compared with DP. Additionally, this study highlights that alternating updates between two Q-networks in DDQN helps avoid local optima, further enhancing performance, especially when greedy strategies tend to fall into suboptimal choices. The conclusions suggest that QDQN is an effective and robust approach for optimizing energy management in PHEVs, offering superior energy efficiency over traditional reinforcement learning methods. This approach provides a promising direction for real-time energy optimization in hybrid and electric vehicles. [ABSTRACT FROM AUTHOR]

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy (EMSad-bg) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy's adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMSad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle. [ABSTRACT FROM AUTHOR]

In order to better improve the fuel economy of plug-in hybrid electric vehicle (PHEV), an adaptive control strategy is proposed with the application of traffic information obtained from internet of vehicles technology. Firstly, the P2-configuration PHEV simulation model is developed based on MATLAB/Simulink. Secondly, a virtual scenario based on SUMO is built to simulate internet of vehicles technology to obtain traffic information. Through the experimental vehicle speed compared with average Baidu API to extract the traffic speed, verify the validity of the virtual scene. Based on the extracted average traffic flow speed, approximate global driving condition is generated by the exponential weighted moving average method. Then, the SOC reference trajectory is generated by the dynamic programming (DP) algorithm based on the acquired approximate global driving condition information. PI control is employed to follow the SOC reference trajectory, enabling adjustment of the equivalent factor adaptively. Finally, the SUMO-MATLAB co-simulation platform is built to validate the effectiveness. It demonstrates that the adaptive equivalent fuel consumption minimization strategy (A-ECMS) with information of internet of vehicles saves 3.6% of fuel consumption compared with ECMS strategy without information of Internet of Vehicles (IoV). To verify the possibility of applying the proposed strategy to a vehicle, a Linux board that can acquire real-time road condition information is developed, applying real-time traffic information to the strategy. The experiment outcomes demonstrate that, in comparison to the ECMS strategy without IoV information, the proposed approach improves fuel efficiency by 3.8%. [ABSTRACT FROM AUTHOR]

To tackle the energy-saving optimization issue of plug-in hybrid electric trucks traversing multiple traffic light intersections continuously, this paper presents a double-layer energy management strategy that utilizes the dynamic programming–twin delayed deep deterministic policy gradient (DP-TD3) algorithm to synergistically optimize the speed planning and energy management of plug-in hybrid electric trucks, thereby enhancing the vehicle's passability through traffic light intersections and fuel economy. In the upper layer, the dynamic programming (DP) algorithm is employed to create a speed-planning model. This model effectively converts the nonlinear constraints related to the position, phase, and timing information of each traffic signal on the road into time-varying constraints, thereby improving computational efficiency. In the lower layer, an energy management model is constructed using the twin delayed deep deterministic policy gradient (TD3) algorithm to achieve optimal allocation of demanded power through the interaction of the TD3 agent with the truck environment. The model's validity is confirmed through testing on a hardware-in-the-loop test machine, followed by simulation experiments. The results demonstrate that the DP-TD3 method proposed in this paper effectively enhances fuel economy, achieving an average fuel saving of 14.61% compared to the dynamic programming–charge depletion/charge sustenance (DP-CD/CS) method. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) is a decision-making algorithm for effective power allocation between storage devices in a hybrid energy storage system (HESS). Source voltages, state of charge (SOC), the terminal voltage of the load, and the rate of change in the battery current must be considered while implementing the EMS and, hence, they are termed as performance indicators. This research work focuses on the development of an EMS, designed to manage the performance indicators of the sources (terminal voltage and battery current rate) and ensure efficient power distribution through a shared bus topology. A shared bus topology employs individual converters for each source, offering efficient control over these sources. Rule-based fuzzy logic control ensures efficient power distribution between batteries and ultracapacitors. Additionally, hardware has been developed to validate the power allocation strategy and regulate the DC-link voltage in the energy management system (EMS). dSPACE MicroLabBox is utilized for the implementation of real-time control strategies. A battery and an ultracapacitor bank are utilized in a hybrid energy storage system. The simulation outcomes have been corroborated by experimental data, affirming the efficacy of the proposed energy management strategy. The proposed EMS achieves a 2.1% battery energy saving compared to a conventional battery electric vehicle over a 25 s duration under the same load conditions. [ABSTRACT FROM AUTHOR]

Given the urgent challenges posed by global climate change and the ongoing energy crisis, fuel cell electric vehicles (FCEVs) have emerged as a promising solution. Incorporating sophisticated energy management strategies (EMSs) into FCEVs can significantly enhance the efficiency of the complex powertrain under diverse driving conditions. In this paper, a dual-model predictive control energy management strategy based on long short-term memory (LSTM)-based driving condition recognition is proposed to enhance the economic performance of FCEVs and robustness across diverse driving conditions. Firstly, to improve the generalization capability and adaptability of the LSTM model and to enhance the accuracy of driving condition recognition, wavelet transform (WT) is introduced into both the offline training and online application of LSTM. Secondly, to enhance the real-time performance and control effectiveness of the EMS, model predictive control (MPC) and explicit model predictive control (eMPC) are established based on a unified optimization objective and constraints. Thirdly, a dual MPC switching logic is developed using the information of driving condition prediction, ensuring the coordination of dual MPCs in practical applications and enhancing their adaptability to various conditions. Finally, an evaluation of the simulations demonstrates that the proposed dual-model predictive control energy management strategy based on wavelet transform LSTM driving condition recognition (WTL-DMPC EMS) can improve economic performance. Compared with other baselines, the energy-saving capability is remarkable, showcasing its promising performance. [ABSTRACT FROM AUTHOR]

Cotton is an important economic crop and strategic resource. The 14th Five-Year Plan for agricultural mechanization development emphasizes the need to vigorously promote agricultural mechanization and intelligentization. As a key agricultural equipment for achieving whole process mechanization of cotton harvesting, cotton pickers have attracted increasing attention. In China, the traditional walking transmission system of cotton pickers usually adopts the form of hydraulic infinite variable speed combined with mechanical stepped transmission. However, during operation, these pickers face significant variations in working conditions and fluctuating external loads, resulting in an overall high load. The hydraulic walking transmission system finds it struggles to continuously adjust speed and torque to match the constantly changing actual load, which leads to frequent fluctuations in the engine operating points, mismatch of torque in transmission components, and low efficiency of the transmission system. At the same time, due to the large overall load during the operation of the cotton picker, the engine runs for a long time in the high-speed and high-load area, and it is unable to maintain the high-efficiency area, resulting in the problem of high fuel consumption. To address the high fuel consumption associated with traditional cotton picker engines operating in inefficient areas, as well as the low efficiency of the hydraulic drive transmission system, replacing the original 410 kW engine with a 560KW engine allows it to operate in the high-efficiency area. Simultaneously, employing hybrid technology to replace hydraulic transmission with electric drive can significantly improve the performance and efficiency of the cotton picker, yielding better operational outcomes and economic benefits. In order to solve the problems of low system efficiency and high energy consumption caused by the hydraulic transmission of the main working subsystem of the traditional diesel engine power six-row cotton picker, a series hybrid power system configuration is proposed for the first time. Based on this, a hybrid power system configuration for a six-row cotton picker is proposed for the first time. To accommodate the actual working conditions of cotton pickers, a two-speed gearbox is implemented, which includes a working gear and a transporting gear. The front and rear axles are driven by front and rear drive motors through gearboxes and front and rear axle reducers. This configuration allows for efficient operation and transmission of power, enhancing the overall performance of the cotton picker. A vehicle simulation model for a six-row series hybrid cotton picker has been developed using MATLAB/Simulink, and the effects of three strategies on the performance of the cotton picker are studied: power following strategy, equivalent consumption minimization strategy (ECMS), and torque distribution combined with equivalent consumption minimization strategy (TD-ECMS). Simulation results show that under a combined road transportation and field operation condition, the total simulation duration is 1 319 seconds, with 0 to 688 seconds allocated for road transportation and 689 to 1 319 seconds for field operation. The ECMS strategy significantly minimizes fluctuations in the engine's speed point. In comparison to the power-following strategy, which allows the engine to operate in a stable state only 34.34% of the time, the ECMS strategy enhances stability, increasing the stable operating time to 75.28%. This results engine speed stabilization time increased by 40.94 %. The TD-ECMS strategy achieves optimal control of the comprehensive efficiency of the dual motors, maintaining consistency with power-sharing control when the required power is high, and when the required power is low, the torque is completed by the front drive motor alone, the reduction of working points in the low-efficiency area of the rearmounted motor leads to a decrease in the overall proportion of operation within the low-efficiency area. The comprehensive efficiency of the dual motors under the TD-ECMS strategy increased from 93.92% to 94.83% compared to power-sharing control. Compared to the power-following strategy, the ECMS strategy has reduced fuel consumption by 4.87%, and the TDECMS has reduced fuel consumption by 5.62%, resulting in a significant decrease and an improvement in overall economic performance. This paper presents an efficient energy management strategy for a six-row series hybrid cotton picker, laying the foundation for engineering practice. [ABSTRACT FROM AUTHOR]

The maritime industry, a major contributor to carbon emissions, is under increasing environmental pressure due to global climate change. This study presents an innovative energy management strategy for hybrid power systems in ocean engineering vessels, based on an improved particle swarm optimisation algorithm. We convert the traditional powered vessel Marine Oil 257 to a hybrid model, and explore the energy storage requirements, system configurations, and control methods for a practical implementation. Post-conversion, the main diesel engine drives the propeller, and is supported by a lithium iron phosphate battery energy storage system in conjunction with the diesel engine and shaft generators to achieve certain energy efficiency and emission reduction goals. In our strategy, the shaft power of the main engine and the active power of the shaft generator are employed as decision variables, and the ship power balance, operational speed limits, generator output constraints, and system reliability are taken into consideration. Real-time optimisation of energy allocation is performed using an improved particle swarm optimisation algorithm in MATLAB. The effectiveness of this approach is validated through a comparative analysis with full-scale experimental data, and it is shown to be a practical pathway for retrofitting traditional power vessels to enhance the energy efficiency and for providing valuable insights for technological advancement. [ABSTRACT FROM AUTHOR]

With the increasing severity of environmental problems and energy scarcity, fuel cell electric vehicles (FCEVs), as a sustainable and efficient means of transportation, are attracting more attention. The ageing of fuel cells (FCs) has become an urgent problem with the development of FCEV. In order to prolong the lifetime of FCs, this paper builds a model of a vehicle driven by two power sources, FC and lithium battery (Lib) using AVL Cruise. A rule-based energy management strategy (EMS) is developed in Simulink to explore the optimal control strategy for the vehicle in terms of the durability of the FC. An FC ageing model is used to quantify the degradation voltage of different duty cycles. The results show that the FC engagement levels, OCV operations, and start/stop operations can affect the lifetime of the FC significantly. By optimising the EMS, the lifetime of the FC is improved by 9.47%. [ABSTRACT FROM AUTHOR]

The application of fuel cell tractors is expected to drive technological upgrades and sustainable development in agricultural machinery. However, fuel cell hybrid systems face issues such as slow dynamic response, low efficiency, and short lifespan. This paper proposes an energy management strategy based on signal reconstruction methods, including Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), to achieve optimal energy utilization and system efficiency based on frequency response characteristics. First, we collected data on the tractor's traction force and operating speed, and calculated the required traction power using a full-machine dynamics model built in MATLAB software. We conducted frequency response characteristic analysis of the fuel cell hybrid system based on EMD and VMD, establishing an energy management controller to sequentially meet the average power demand of the fuel cell under plowing load operations, the instantaneous acceleration power demand of the power battery, and the real-time compensation power demand of the supercapacitor. The results show that the EMD strategy exhibits good stability, while the VMD strategy performs better in terms of hydrogen consumption. Under the VMD strategy, the hybrid system achieves a maximum output efficiency of 55.0% with a total hydrogen consumption of 750 g. Compared to the EMD strategy, the maximum efficiency of the system increases by 27.31%, and hydrogen consumption decreases by 3.49%. This study provides a new theoretical foundation and technological route for the application of fuel cell hybrid systems in the field of agricultural machinery. [ABSTRACT FROM AUTHOR]

ABSTRACT This article deals with the modeling and control of a solid‐state transformer (SST) based on a dual active bridge (DAB) and modular multilevel converter (MMC) for integrating solar photovoltaic (SPV) and battery energy storage (BES) systems into the grid. SST uses DABs for bidirectional DC‐DC conversion and an MMC for DC‐AC conversion. A hybrid control method is developed in this study, which combines proportional‐integral (PI) controllers and model predictive control (MPC) to achieve the multiple objectives of the SST. The DABs' control system uses PI controllers for power extraction from the SPV system and to regulate the charging and discharging of the BES according to power generation and demand fluctuations. MPC is applied to control the active power injection, regulate the DC‐link and sub‐module capacitor voltages of the MMC. Moreover, the developed hybrid control method ensures reliable SST performance even under adverse conditions like grid voltage distortion, unbalance, and frequency variations. An energy management strategy is developed based on total power generation, reference active power injection, and the state of charge of the BES. This strategy ensures accurate reference power injection to the grid despite dynamic changes in operating conditions. The article also presents a detailed mathematical model of the DAB and MMC components, and the stability of the control algorithm is analyzed theoretically. The effectiveness of the SST and the proposed hybrid control scheme is demonstrated through MATLAB/Simulink simulations and hardware‐in‐the‐loop experimental results under various operating conditions. [ABSTRACT FROM AUTHOR]

Hydrogen has become an essential element in the pursuit of sustainable and clean energy solutions. Especially with the fast-paced advancement in demand, supply, and policy environment, its impact on hybrid renewable energy (HRE) management is becoming increasingly relevant. Efficient energy consumption, cost reduction, and enhanced user comfort are now critical factors in energy optimization. The production of green hydrogen, which is generated through water electrolysis using renewable energy sources (RES), has shown great potential as a sustainable energy solution. It offers several advantages, such as zero greenhouse gas emissions, high energy density, and versatile applications. This paper presents a detailed study on the power management and control of a hybrid renewable system (HRES) equipped with a diesel generator (DG) as a backup power source. The main objectives of the hybrid system are to satisfy the load power demand, ensure the most efficient use of the HRES, and keep the battery bank charged to prevent blackouts and extend the battery's life. To guarantee the system's reliability, the DG should be sized to meet the peak load demand when the RES generates less electricity than the load demand. This study explores the feasibility of modified versions of the load following and cycle charging control strategies to overcome the limitations of managing generation and storage systems' operations in different operating modes and to enhance the performance of an HRES with a DG that supplies electricity to a small and remote location. The proposed method not only maximizes the use of RES production but also enables multi-energy source management under different power generation and load demand scenarios. The study's outcomes demonstrate the feasibility of this proposed power dispatch strategy in a remote location environment. The paper includes a detailed discussion of overall control, mathematical models, energy storage in the battery model, and energy dispatching based on load following. To design and simulate the hybrid model system, MATLAB-SIMULINK is used, and the results are analyzed to identify the appropriate operation requirements, component selection, and energy management of the hybrid renewable energy system. [ABSTRACT FROM AUTHOR]

This paper proposes a new design method of energy management strategy (EMS) with adaptive super-twisting sliding mode control (ASTSMC) for fuel cell/battery/supercapacitor hybrid vehicle (FCHEV). The main objective of the proposed EMS is to improve power performance, fuel cell lifetime, and fuel consumption while considering the regulation of the DC-bus voltage. The proposed EMS is designed based on a frequency-decoupling technique using an adaptive low-pass filter, Harr wavelet transform (HWT), and FLC to decouple the required power into low, medium, and high-frequency components for fuel cell, battery, and supercapacitor, respectively. The presented frequency-decoupling-based strategy can improve the power performance of the vehicle as well as reduce load stress and power fluctuation in the fuel cell. Nevertheless, the neural network optimization algorithm (NNOA) is employed to optimize the membership functions of FLCs while considering the hydrogen consumption and constraints on the state of charge (SOC) of the battery and supercapacitor. To achieve robustness and high precision control, the ASTSMC is developed based on a nonlinear disturbance observer (NDOB) to stabilize the DC-bus voltage and currents of the energy sources, ensuring that the fuel cell, battery, and supercapacitor track their obtained reference values. The FCHEV system with the proposed EMS is modeled on MATLAB/Simulink, and three typical driving cycles such as HWFET, UDDS, and WLTP driving schedules are used for evaluation. The findings exhibit that the proposed EMS can effectively improve the fuel economy, reduce power fluctuation in the fuel cell, and prolong its lifetime compared to other existing methods such as the equivalent consumption minimization strategy (ECMS), state machine (SM), and FLC-based EMSs. [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]

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]

This paper investigates microgrid systems characterized by the coexistence of discrete events and continuous events, a typical hybrid system. By selecting the charging and discharging processes of the energy storage unit as logical variables, a mixed logical dynamic (MLD) model for the microgrid in islanded mode is established. Based on this model, model predictive control (MPC) theory is employed to optimize the energy management strategy, aiming to stabilize the DC bus voltage of the photovoltaic (PV) unit and minimize the switching frequency of the energy storage unit's charging and discharging processes during system operation. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility and benefits of integrating hydrogen storage systems into off-grid power systems. As a case study, a stand-alone microgrid located on a small island in southeastern Sardinia (Italy) and already equipped with a photovoltaic (PV) system coupled with batteries is chosen. To evaluate the integration benefits of the two storage systems (hydrogen and batteries) and the optimal sizing of the hydrogen storage section, a parametric analysis with a simulation model implemented in the MATLAB environment has been carried out. Results show that the optimal integration between the two storage systems is found by imposing a share of the batteries (18 kWh, 50% of the overall battery capacity) to exclusively supply the load demand (called battery energy buffer). In these conditions, an almost 100% self-sufficiency of the microgrid can be achieved by a hydrogen generator with the lowest size considered (2.4 kW), a hydrogen storage volume of 10 m3 and a fuel cell, mainly able to completely cover the night loads, of 1.5 kW. This sizing leads to a Levelized Cost of Electricity (LCOE) for the hydrogen section of about 10.5 €/kWh. [Display omitted] • Investigation of a hydrogen storage into off-grid systems characterized by seasonal loads. • Determination of the hydrogen storage sizing through parametric analysis. • Optimal integration with batteries found by imposing an energy buffer equal to 50%. • Through the hydrogen storage, self-sufficiency higher than 99% is achieved. • Significant energy surcharges detected to ensure 100% green electricity. [ABSTRACT FROM AUTHOR]

Multi-rotor drones, a kind of unmanned equipment which is widely used in the military, commercial consumption and other fields, have been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell multi-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural light weight, hydrogen storage methods, energy management strategies, thermal management, etc. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented. [ABSTRACT FROM AUTHOR]

Nowadays, due to climate change and disappearance of fossil fuels, hybrid electric UAVs using renewable energy sources are being developed. In addition, although research on UAVs with a large wingspan and high weight is common due to their long endurance, research on mini UAVs has remained limited. This study aims to increase the energy capacity of solar-powered mini UAVs and thus extend their endurance by developing a fixed-wing hybrid UAV that can fly with solar energy as much as possible, especially during the cruise phase. In this study, a solar-powered mini VTOL (vertical take-off and landing) UAV with a wingspan of 1.8 m and weight of 3.3 kg is developed and a model of the system consisting of solar cells, a battery, a super capacitor, and a DC/DC converter is created in MATLAB/Simulink software (R2023b). Additionally, state machine control (SMC), a rule-based (RB) energy management strategy (EMS), has been applied to this model. While the power obtained from the sun is divided among the other energy components, the durability of the UAV is increased, and the excess energy is stored as altitude energy to be used when necessary. As a result, in this study, an energy management algorithm including altitude energy has been successfully applied to a solar-powered UAV, achieving an 11.11% energy saving. [ABSTRACT FROM AUTHOR]

To further improve the fuel economy of hybrid tractors, an energy management strategy based on model predictive control (MPC) solved by dynamic programming (DP) is proposed, taking into account the various typical operating conditions of tractors. A coupled dynamics model was constructed for a series diesel–electric hybrid tractor under three typical working conditions: plowing, rotary tillage, and transportation. Using DP to solve for the globally optimal SOC change trajectory under each operating condition of the tractor as the SOC constraint for MPC, we designed an energy management strategy based on DP-MPC. Finally, a hardware-in-the-loop (HIL) test platform was built using components such as Matlab/Simulink, NI-Veristand, PowerCal, HIL test cabinet, and vehicle controller. The designed energy management strategy was then tested using the HIL test platform. The test results show that, compared with the energy management strategy based on power following, the DP-MPC-based energy management strategy reduces fuel consumption by approximately 7.97%, 13.06%, and 11.03%, respectively, under the three operating conditions of plowing, rotary tillage, and transportation. This achieves fuel-saving performances of approximately 91.34%, 94.87%, and 96.69% compared to global dynamic programming. The test results verify the effectiveness of the proposed strategy. This research can provide an important reference for the design of energy management strategies for hybrid tractors. [ABSTRACT FROM AUTHOR]

The green port multi-energy microgrid, featuring renewable energy generation, hydrogen energy, and energy storage systems, is an important gateway to achieve the net-zero emission goal. But there are many forms of energy in green port multi-energy microgrid systems, the power fluctuates frequently, and the port loads with large fluctuations and fast changes. These factors can easily lead to the problem of the state of charge exceeding the limit of the energy storage system. To distribute the fluctuating power in the green port multi-energy microgrid system reasonably and maintain the state of charge (SOC) of the hybrid energy storage system in an moderate range, an energy management strategy (EMS) based on dual-stage fuzzy control with a low pass-filter algorithm is proposed in this paper. First, the mathematical model of a green port multi-energy microgrid system is established. Then, fuzzy rules are designed, and the dual-stage fuzzy controller is used to change the time constant of the low-pass filter (LPF) and modify the initial power distribution by an LPF algorithm. Finally, simulation models are built in Matlab 2016a/Simulink. The simulation results demonstrate that, compared with other algorithms under the control of the EMS proposed in this paper, the high-frequency component in the flywheel power is smaller, and the SOC of the supercapacitor is maintained in a reasonable range of 34–78%, which extends the lifespan of the flywheel and supercapacitor. Additionally, it has a faster automatic adjustment ability for the state of charge of the energy storage system, which is conducive to better maintaining the stable operation of green port multi-energy microgrid systems. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy. [ABSTRACT FROM AUTHOR]

Summary: To enhance the efficiency and prolong the battery life of hybrid energy systems equipped with battery and supercapacitor for a pure electric loader, this paper proposes a real‐time energy management strategy based on model predictive control (MPC). First, the working characteristics of the loader and the topology structure of the hybrid energy are analyzed. Second, considering the repetitive operation model of electric loaders, a neural network algorithm based on long short‐term memory is used to predict power and a dynamic programming algorithm is used as the rolling optimization solution for MPC strategy. Finally, the MPC is verified using the simulation in MATLAB/Simulink. The results indicate that compared with other strategies, the MPC strategy can improve hybrid energy efficiency and reduce the maximum battery current fluctuations. The proposed method demonstrates the feasibility and effectiveness of the hybrid energy system. [ABSTRACT FROM AUTHOR]

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements' SOCs at the end of a driving cycle are also regulated to be equal to the initial ones. [ABSTRACT FROM AUTHOR]

Single hydrogen fuel cell has a slow response speed when it is used as the energy source of vehicle operation. This fuel is difficult to cope with various operating conditions. Aiming at the problem of the automobile energy belt engine with most fuel cells, the energy management strategy (EMS) is analyzed. The objective function is designed by controlling the lowest hydrogen consumption index. Considering the constraints of battery state of charge and fuel cell power, an adaptive minimum principle, EMS based on fuzzy control is designed. After analyzing the simulation results of the proposed strategy, it is found that SOC of Pontryagin's Minimum Principle (PMP) is 15.53% lower than that of PFS. The low value of hydrogen consumption is 26.5g. The difference of equivalent hydrogen consumption per hundred kilometres is 56.8g. The automobile engine system under FLC-PMP control strategy is more stable and the error deviation is less than 1%. The result of this strategy can effectively reduce energy consumption and improve economy and power. It effectively provides a new research idea for the performance optimization of fuel vehicle engine system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cloud computingbased parallel deep reinforcement learning (DRL) energy management strategy (EMS) for connected plug-in hybrid vehicles. First, a proximal policy optimization (PPO) algorithm is developed. Since the cloud computing can reduce the computational burden of the connected vehicles, the PPO is deployed in the proposed cloud computing-based EMS. In order to improve the strategy adaptation, a parallel mechanism is proposed to achieve the information interaction with multiple vehicles. Considering the real-time control requirements, thread pool is proposed and applied in the cloud computing based parallel EMS. The thread pool-based strategy provides an efficient real-time control ability and strategy improvement solution. To verify the PPO based EMS, the dynamic programming, deep Q-network and double deep Q-network strategies are developed for comparison. It is found that the PPO can achieve similar fuel efficiency improvement with the DP strategy among the three DRL algorithms. For parallel training of multiple connected vehicles, the cloud computing-based parallel EMS improves fuel economy by approximately 7.7%. Threadpool based parallel real-time EMS reduces average time for computational interactions by 20% and further improves the fuel efficiency. The proposed strategy has the advantages of realtime control, adaptability and continuous learning for improved fuel efficiency. [ABSTRACT FROM AUTHOR]

Net-zero emission targets have imposed the transition to clean and efficient energy technologies, considering the problems arising from the discontinuous and variable nature of renewable energy resources. Integrated energy systems based on solar and hydrogen energy are becoming essential to obtain uninterrupted power, especially for off-grid applications. This study focuses on the design, modeling, and evaluation of such a system that meets the energy consumption of a residence in Türkiye. The framework basically consists of photovoltaic/thermal panels (PV/Ts), batteries, a PEM electrolyzer (PEM-El), hydrogen (H 2) storage tanks, and PEM fuel cells (PEM-FC). A zero-dimensional mathematical model was developed for each of the components of the system. Yearly analyses were conducted with a new control strategy based on power balancing, and dynamic variations in power generated by the PV/T panel, the SOCs of batteries, the power consumption of the PEM-El, the power generated by the PEM-FC and the amount of hydrogen in the tank were examined. The results showed that the electricity requirement of a 100 m2 house can be met continuously for a year with 20 PV/T panels (total area of 32.71 m2) with a total capacity of 62 kW, 8 batteries with a total capacity of 192 kWh, and 7 H 2 storage tanks with a total capacity of 29.4 kg. It was also shown that the electricity needs of the house is met by stored hydrogen between December and April. Furthermore, the hybrid system's economic evaluation was carried out and the investment cost of the system was calculated. • PV/T and PEM fuel cell-based integrated energy system is examined. • A novel energy management strategy is determined for the integrated system. • Key design parameters and investment cost of the energy system are found. • To supply the demand, a hydrogen tank with a total capacity of 29.4 kg is needed. [ABSTRACT FROM AUTHOR]

A novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques. [ABSTRACT FROM AUTHOR]

Among the global carbon emissions in different fields, the carbon emissions in the transportation sector account for more than 1/4. Only the development of passenger car technology makes it difficult to achieve the climate goals. E-bike is considered to be one of the effective solutions. Hydrogen energy, as one of the representatives of new energy, has the advantages of cleanliness, high efficiency, and high energy density. Therefore, in this paper, a long-range hydrogen-electric hybrid bike and its energy management strategy are designed to achieve optimal performance. Herein, two energy management strategies, including the threshold-based and fuzzy logic optimized threshold-based strategy, are designed and compared by evaluating the parameters of equivalent hydrogen consumption, state of charge fluctuation, and the final state of the control system. The results show that with an initial SOC of 50% for Li-ion battery, the fuzzy logic optimized threshold-based strategy reduces equivalent hydrogen consumption by up to 16% compared to the threshold-based strategy. Besides, the fluctuation of the Li-ion battery's state of charge could be reduced by up to 54.7%, indicating that the fuzzy logic optimized threshold-based strategy is a promising energy management strategy for fuel cell hybrid E-bike. [Display omitted] • A long-range hydrogen-electric hybrid E-bike with metal hydride hydrogen storage is designed. • A fuzzy logic optimized threshold-based energy management strategy (EMS) is proposed. • Li-ion battery SOC fluctuations could be reduced by up to 54.7% via fuzzy logic optimized EMS. • Fuzzy logic optimized EMS shows self-adaptability and economic H 2 consumption for hybrid E-bike. [ABSTRACT FROM AUTHOR]

For hydrogen fuel cell vehicles, energy management strategies (EMS) are vital for balancing fuel cell and battery power, limiting fuel cell power, maintaining state of charge (SOC) fluctuation range and mitigating degradation. Reinforcement learning-based EMS, especially using deep Q-network (DQN) and deep deterministic policy gradient (DDPG), have demonstrated potential for enhancing the fuel economy of hybrid electric vehicles (HEV) and fuel cell electric vehicles (FCEV). This research proposes mutation protection DQN (MPD)-based EMS to improve hydrogen fuel economy and reduce fuel cell degradation under driving cycles with plenty of mutations. By quantifying mutation, exploring its relationship with driving conditions and integrating a mutation protection module with DQN, MPD-based EMS achieves approximately 11% and 6% better fuel economy compared to the other two learning-based EMS. Additionally, it also reduces fuel cell degradation by approximately 21% and 13%. • Quantifying the mutation of driving conditions through variance. • Utilizing information entropy to process mutations. • Introducing a dynamic mutation threshold value. • Designing mutation protection module and integrating it into the DQN. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively. [ABSTRACT FROM AUTHOR]

To address the limitations of battery-powered unmanned surface vehicles (USVs) in terms of endurance mileage, this study introduces proton exchange membrane fuel cells (PEMFCs) into the power system of USVs. This paper focuses on the design and testing of a hybrid power system and its energy management strategy for a lightweight USV. The test results demonstrate that the hybrid power system effectively fulfills the power requirements of the USV across various operational conditions. Specifically, the amplitudes of bus voltage overshoot and undershoot remain below 0.31 V even during sudden changes in working conditions. The energy management strategy, in conjunction with an auxiliary battery, successfully limits the PEMFC power fluctuation to 200 W/s, thereby enhancing the lifespan of the PEMFC. Moreover, the study reveals that replacing Li-ion batteries with the PEMFC system as the primary power source can significantly enhance the USV's endurance mileage by 1.33–1.70 times carrying the same weight and 1.07–1.36 times carrying the same volume, respectively. And with increased hydrogen storage, the PEMFC system's energy density advantage over Li-ion batteries becomes more pronounced. These findings contribute to the exploration of PEMFCs as a promising solution for addressing the endurance limitations of battery-powered USVs, leading to their improved performance and extended operational range. • A whole vessel model including electrical, dynamic and control system is established. • A strategy based on bus voltage and PEMFC operating stability is proposed. • The energy management strategy has good adaptability to operating conditions. • Replacing Li-ion batteries with PEMFCs effectively increases the range of USVs. [ABSTRACT FROM AUTHOR]

Fuel-cell hybrid trucks (FCHTs), as primary force in fuel-cell vehicles, currently lack comprehensive development of rule-based energy management strategies (EMSs). The current optimization rule of the dynamic programming (DP) algorithm exhibits poor adaptability to changes in operating conditions and battery state of charge (SOC). Therefore, the work proposed real-time rule EMS of DP-optimized FCHTs. First, DP was taken offline to determine the optimal relationship between fuel cell power and demand power under driving conditions and SOCs. Secondly, real-time online recognition was achieved through driving pattern recognition (DPR) controller. A backpropagation neural network, optimized by the northern goshawk algorithm, was used for constructing DPR. Finally, the MATLAB/Simulink simulation showed that the proposed strategy exhibited superior DPR accuracy. Compared to rule-based strategies, significantly lower hydrogen consumption reduced vehicle-operating costs. • A DP-optimized real-time energy management strategy was designed. • The multivariate DP offline of the optimal rule control sequence library was constructed. • Driving conditions and the initial SOC were defined as multivariates. • A driving pattern recognizer was constructed using the northern goshawk optimization and back-propagation neural network. [ABSTRACT FROM AUTHOR]

To optimize the hydrogen consumption, lifespan of the fuel cell (FC), and durability of the lithium-ion battery in fuel cell electric vehicles (FCEVs), energy management strategies (EMSs) have been developed and implemented in the vehicle control units (VCUs) by engineers. Recently, with the rapid development of artificial intelligence, reinforcement learning (RL)-based EMS have shown great promise among various strategies. However, RL-based EMS tends to exhibit relatively poorer performance when faced with novel environments owing to the requirement for learning. This study proposes an enhanced EMS, Scalable Learning in Novel Environment (SLNE)-based EMS. This EMS performs well in unknown and dynamic environments by integrating a memory library (ML) composed of the Dirichlet process (DRP) clustering algorithm combined with the Chinese restaurant process (CRP) using the expectation-maximization (EM) algorithm for updates and the deep Q-network (DQN). Simulation and comparison are conducted under two novel driving cycles to evaluate the performance of the dynamic programming (DP)-based EMS, rule-based EMS, DQN-based EMS, and SLNE-based EMS. By comparing reward, power, and degradation, the results indicate that the proposed SLNE-based EMS demonstrates excellent convergence and adaptability when facing a novel and dynamic environment. Additionally, the proposed SLNE-based EMS achieves approximately 5% improvement in fuel economy, approximately 4.5% reduction in fuel cell degradation rate, and improved durability of the lithium-ion battery, compared with DQN-based EMS. [Display omitted] • The scalable learning in novel environment-based EMS is proposed for an FCEV. • The SLNE-based EMS performs well from the start when facing novel driving cycles. • The SLNE-based EMS demonstrates a higher reward ceiling and lower fluctuations. • Considering the fuel cell degradation, proposed EMS can better enhance the lifespan. • The fuel consumption of the SLNE-based EMS is 5% lower than that of the DQN-based EMS. [ABSTRACT FROM AUTHOR]

The hydrogen combustion engine (H 2 ICE) is known to be able to burn H 2 , producing no CO 2 emissions and extremely low engine-out NO x eo emissions. In this work, the potential to reduce the NO x eo emissions through the implementation of electric hybridization of an H 2 ICE-equipped passenger car (H 2 -HEV), combined with a dedicated energy management system (EMS) is discussed. Achieving a low H 2 consumption and low NO x eo emissions are conflicting objectives, the trade-off of which depends on the EMS and can be represented as a Pareto front. The dynamic programming algorithm is used to calculate the Pareto-optimal EMS calibrations for various driving missions. Through the utilization of a dedicated energy management calibration, H 2 -HEVs exhibit the potential to decrease the NO x eo emissions by more than 90%, while, decreasing the H 2 consumption by over 16%, compared to a comparable non-hybridized H 2 -vehicle. The present paper represents the initial potential analysis, suggesting that H 2 -HEVs are a viable option towards a CO 2 -free mobility with extremely low NO x eo emissions. • H 2 -HEVs with a dedicated energy management can achieve extremely low NO x eo • Mixed H 2 -HEVs have a superior H 2 -NO x eo trade-off than series and parallel H 2 -HEVs • Mixed H 2 -HEVs lower the NO x eo emissions by 90% and decrease the H 2 consumption by 16% • H 2 -HEV's optimal EMS calibrations for the entire H 2 - NO x eo Pareto front [ABSTRACT FROM AUTHOR]

The traditional rule-based energy management strategy for plug-in hybrid vehicles has issues, such as difficulty in online correction and limited online optimization capabilities. In addition, the global optimization energy management strategy cannot be applied online or in real-time. Considering the above difficulties, this study proposes a real-time optimization energy management strategy based on the Markov chain for driving condition prediction and online optimization with the minimum principle. To verify the proposed control strategy, the plug-in hybrid vehicle dynamics model, driving condition prediction model, and online optimization control model were first established. The initial value of the battery state of charge was set to 0.4 under the UDDS (Urban Dynamometer Driving Schedule) standard cycle. The simulation results showed that the comprehensive fuel consumption cost was 1.66 yuan, which was 8.28% better than the energy economy of the traditional rule-based energy management strategy. At the same time, a complete vehicle test was also conducted based on a sample vehicle test platform. The experimental results indicated that the energy management strategy proposed herein exhibits better fuel economy compared to that exhibited by the traditional rule-based energy management strategy. Simulations and experiments have verified the effectiveness of the proposed control strategy in this study. [ABSTRACT FROM AUTHOR]

For a fuel cell hybrid electric vehicle (FCHEV) powered by a fuel cell (FC), a battery (BAT) and an ultracapacitor (UC), an improved convex-optimization-based energy management strategy (EMS) is proposed in this article. To protect the fuel cell and the battery from peak power, an adaptive fuzzy filter is used to complete the frequency decoupling of the power demand. Subsequently, an adaptive equivalent consumption minimization strategy (A-ECMS) is introduced to improve fuel economy. To improve the optimality of the EMS obtained, an improved convex optimization method is utilized by introducing a slack variable and a barrier function. Finally, based on experimental data and an improved convex optimization algorithm, the optimal EMS is calculated and verified by a series of simulations under different driving cycles. Simulation results confirm that, compared with the traditional ECMS-based EMS, the proposed EMS can improve fuel economy and achieve lower power fluctuation and optimal FC efficiency for the FCHEV. [ABSTRACT FROM AUTHOR]

Reinforcement learning has shown success in solving complex control problems, yet safety remains paramount in engineering applications like energy management systems (EMS), particularly in hybrid electric vehicles (HEVs). An effective EMS is crucial for coordinating power flow while ensuring safety, such as maintaining the battery state of charge within safe limits, which presents a challenging task. Traditional reinforcement learning struggles with safety constraints, and the penalty method often leads to suboptimal performance. This study introduces Lagrangian-based parameterized soft actor–critic (PASACLag), a novel safe hybrid-action reinforcement learning algorithm for HEV energy management. PASACLag utilizes a unique composite action representation to handle continuous actions (e.g., engine torque) and discrete actions (e.g., gear shift and clutch engagement) concurrently. It integrates a Lagrangian method to separately address control objectives and constraints, simplifying the reward function and enhancing safety. We evaluate PASACLag's performance using the World Harmonized Vehicle Cycle (901 s), with a generalization analysis of four different cycles. The results indicate that PASACLag achieves a less than 10% increase in fuel consumption compared to dynamic programming. Moreover, PASACLag surpasses PASAC, an unsafe counterpart using penalty methods, in fuel economy and constraint satisfaction metrics during generalization. These findings highlight PASACLag's effectiveness in acquiring complex EMS for control within a hybrid action space while prioritizing safety. [ABSTRACT FROM AUTHOR]

In electric vehicles, the battery is a key component that calculates vehicle performance. Due to their greater efficiency and the lower cost of power, charging an electric vehicle is more affordable than purchasing gasoline or diesel for travel needs. To increase the battery's lifespan, the accuracy of the battery model for electric vehicles must be enhanced. To operate at their peak efficiency, batteries must be managed properly by a battery management system. This research illustrates the functioning of a rechargeable electric vehicle battery's charging system. There are several types of rechargeable batteries used, including lead acid, lithium‐ion, and nickel‐cadmium batteries. In recent trends, are used in the majority of cars use rechargeable lithium‐ion batteries. In general, accumulated heat, rapid utilization, and total energy throughput have an impact on the battery life of electric vehicles. In this study, 50 papers were analyzed about battery charging in an electric vehicle, which utilized different measures, as well as achievements attained by various methods. This survey reviewed the information from a different journal, along with their advantage, disadvantages, and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of electric vehicles by offering a thorough interpretation of the methods currently in use. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%. • The economy and durability of vehicular fuel cell system are both considered. • The LSTM neural network is imported into MPC theory for vehicular power prediction. • The key parameters of MPC and LSTM are determined by referring to the greedy algorithm. • The computational load in MPC optimisation is reduced by simplifying DP algorithm. • The real-time performance of the supposed EMS on-board is validated. [ABSTRACT FROM AUTHOR]

Continuous efforts to preserve the environment and to reduce gaseous emissions due to the massive growth of urban economic development and heightened concerns over crude oil depletion have accelerated researchers to find long‐term solutions, particularly in the transportation sector with the focus on powertrain electrification. This article delivers a comprehensive overview of electric vehicle architectures, energy storage systems, and motor traction power. Subsequently, it emphasizes different charge equalization methodologies of the energy storage system. This work's contribution can be identified in two points: first, providing an overview of different energy management methods to researchers and scholars. Second, to highlight the state‐of‐the‐art leanings in major components and to highlight promising approaches to hybrid electric vehicle future development. [ABSTRACT FROM AUTHOR]

Environmental working conditions have a great impact on the power demand and fuel consumption of vehicles, and the rational use of road terrain information plays an important role in the improvement of vehicle economy and energy sources durability. In this paper, a fuel cell hybrid electric bus (FCHEB) energy management strategy (EMS) based on deep deterministic policy gradient (DDPG) algorithm taking into account future terrain information is proposed based on cyber-physical system (CPS) to reduce the economic cost of FCHEB. First, this paper implements the information exchange between the vehicle system and the network layer through CPS to obtain the environmental working conditions of the vehicle operation. Second, future terrain information is introduced into the framework for the first time with hydrogen consumption, battery degradation and fuel cell durability constraints to rationally allocate the power of the FCHEB. The results show that the proposed strategy improves the power battery durability by 7.39% and reduces the total operating cost by 5.76% compared to the EMS that ignores the future terrain information. • An energy management strategy that considers future terrain information is proposed. • The proposed strategy achieves an effective combination with cyber physical system. • A trade-off between hydrogen consumption and energy sources durability is achieved. • The proposed strategy reduces the total cost by 7.81% compared to NTI EMS. [ABSTRACT FROM AUTHOR]

This paper provides a cost-effective energy management method for the microgrid's standalone and on-grid operational modes. The considered microgrid based on Green-to-Green systems such as a photovoltaic array (PV), wind turbine (WT) as renewable generators, hydrogen fuel cell (FC) system, microturbine (MT), and battery storage system. The suggested technique is based on the marine predator algorithm (MPA) that and created for a scheduling horizon of one day. The main objective of this paper is to reduce operating costs while satisfying the demand power. To approve the performance of the suggested management strategy, the simulation results are compared with other recent optimization algorithms. These algorithms include particle swarm optimization (PSO), salp swarm algorithm (SSA), and coot optimization algorithm (COOT). To approve the performance of the proposed EMS, the system will be evaluated for three cases: free renewable generation, restricted generation mode, and unlimited main grid power mode. For the first case, the proposed EMS reduces the operating costs by 0.8732%. For case 2, the economic benefits have been raised by 1.0815% and by 0% for the last case because of the reduced problem complexity. • A cost-effective energy management strategy for the microgrid based PEM fuel cells is proposed. • Implementation of marine predator algorithm to minimize the operating costs. • The performance has been investigated under various operating conditions. [ABSTRACT FROM AUTHOR]

With the prosperity of artificial intelligence in recent years, energy management strategies (EMSs) based on deep reinforcement learning (DRL) have become the mainstream approach to ensure efficient energy distribution of hybrid electric vehicles (HEVs). Moreover, in terms of developing sustainable urban public transportation systems, zero-emission fuel cell hybrid electric buses (FCHEBs) are more promising than hybrid electric buses (HEBs). Given that, this article proposes a novel data-driven EMS based on DRL to enhance the training efficiency of the proposed EMS while improving the fuel economy of an FCHEB. In this article, to optimize the power allocation of the FCHEB, an improved twin delayed deep deterministic policy gradient (TD3) algorithm combined with prioritized experience replay is innovatively formulated, and then a promising EMS based on it is proposed. Furthermore, to enhance the adaptability and improve the training efficiency of the proposed EMS, a stochastic training environment is established with massive real-world velocity data, and a pre-training method using the global optimal experience is designed. Simulation results show that the proposed EMS improves fuel economy by 5.87% compared with the EMS based on original TD3, reaching 97.15% of the global optimal dynamic programming method. • TD3 algorithm combined with prioritized experience replay is innovatively designed. • A stochastic training environment is designed based on massive real driving data. • A pre-training method is proposed to provide a heuristic effect for the TD3 agent. • The proposed strategy improves fuel economy by 8.03% than the DDPG-based strategy. • The robustness and computational efficiency of the proposed strategy are verified. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

Adding battery locomotives to traditional diesel locomotives to form a hybrid consist has the potential to reduce fuel consumption and emissions in freight rail operations. This paper provides route-based estimates of diesel (hpd/ton) and battery (hpb/ton) power requirements and diesel (gal/ton) and battery (hpbhr/ton) energy requirements for hybrid consists. Given a route-specific power profile, the power split between the battery pack and the diesel engine is optimized to minimize fuel consumption by running the diesel engine at maximum efficiency during the entire route. Simulations for 200-mile round trips between Chicago and Harrisburg show that the diesel power is maximum in low-gradient regions at 1.05 (hpd/ton) and battery power is maximum in hilly regions at 2 (hpb/ton) for discharge and 6.5 (hpb/ton) for charge. Maximum fuel efficiency gains of approximately 60% are seen in the mountains, where 2.09 (hpbhr/ton) of battery energy is needed. Minimum fuel economy gains are observed in flat regions, with 20% fuel consumption reduction. With battery current and voltage limits, the battery pack size increases, and fuel savings decrease. [ABSTRACT FROM AUTHOR]

To balance the hydrogen consumption of fuel cell vehicle (FCV), the durability of the fuel cell (FC), and the life of the power battery (PB) to further reduce the whole lifecycle costs of FCV. A multi-objective reinforcement learning-based (MORL-based) energy management strategy (EMS) is proposed in this research. First, the composition mechanism of the FCV lifecycle costs is analyzed, and the equivalent hydrogen consumption model, FC durability degradation model, and PB life decay model are established; Then, a three-dimensional reward function is constructed by integrating the objectives of equivalent hydrogen consumption, FC durability degradation, and PB life decay. And the penalty terms coupled with the decay factors are introduced into the reward function to satisfy the mutual constraint characteristics between the PB and the FC system to ensure the stability of the MORL-based EMS; In addition, the prioritized experience replay technology is introduced into the MORL-based EMS to improve the learning efficiency and convergence of traditional deep Q network (DQN) algorithm; After that, the evaluation and target network of the embedded dueling network are introduced to solve the multi-objective overestimation problem encountered in the training process by generalizing the behavior learning in the presence of similar value behaviors; Finally, the performance of MORL-based EMS and DQN-based EMS is compared by numerical simulation under various driving cycles. The results show that the MORL-based EMS proposed in this paper has better convergence ability, adaptability, and lower lifecycle costs than the DQN-based EMS. In addition, the lifecycle costs of the MORL-based EMS can achieve a 99.2% control effect of the dynamic programming-based EMS. • The prioritized dueling double deep Q network-based EMS is proposed for an FCV. • Three objectives are considered in the PD3QN-based EMS. • Considering the battery life decay, EMS can better reduce the life cycle costs. • The PD3QN-based EMS can achieve a 99.2% control effect of DP-based EMS. • The PD3QN-based EMS can achieve good convergence ability and adaptability. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have been regarded as one of the promising alternatives to zero-emission transportation. New EV registrations have increased as a result of government policies and consumers' awareness of climate change. Moreover, EV technology is being improved through ongoing research and development efforts. Among these, a powertrain with a combination of two electric motors has been proposed for high driving and efficiency performance. The study presents a comprehensive state-of-the-art review of architectures and energy distribution for a dual-motor equipped with dual-source EV system. In detail, various dual-motor configurations, and energy management strategies (EMSs) used in the literature are investigated and categorized. A comparison of the benefits and drawbacks of existing topologies and the EMSs of hybrid energy storage systems (HESSs) is also discussed. Following that, research gaps have been considered. This study can be used as a reference for researchers who are interested in the design and optimal control of the dual-motor dual-source EVs. [ABSTRACT FROM AUTHOR]

Environmental issues such as air pollution and abnormal climate are global concerns. To overcome these problems, the automobile industry is prioritizing the development of eco-friendly vehicles that reduce greenhouse gas emissions Among these, fuel-cell electric vehicles (FCEVs) use hydrogen as a fuel and do not emit exhaust gas and their higher mileage and shorter fuel charging time compared to electric vehicles make them promising next-generation eco-friendly vehicles. However, conventional energy management strategies have not effectively implemented both real-time capability and optimal energy distribution in FCEVs. To address these issues, a powertrain utilizing multi-energy sources is utilized, and a real-time energy control strategy based on long short-term memory (LSTM) is proposed. The training data for LSTM is obtained from the results of dynamic programming, utilizing six-city bus driving cycles, and the Braunschweig city driving cycle is chosen for test simulation. As a result, the LSTM prediction performance is evaluated, resulting in the development of an effective algorithm for real-time energy management of multi-energy sourced FCEVs. [ABSTRACT FROM AUTHOR]