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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]

Energy management strategies (EMSs) are a core technology in hybrid electric vehicles (HEVs) and have a significant impact on their fuel economy. Optimal solutions for EMSs in the literature usually focus on improving fuel efficiency by operating the engine within a high efficiency range, without considering the drivability, which is affected by noise–vibration–harshness (NVH) constraints at low vehicle speeds. In this paper, a dual-mode combustion engine was implemented in a plug-in series hybrid electric vehiclethat could operate efficiently either at low loads in homogeneous charge compression ignition (HCCI) mode or at high loads in spark ignition (SI) mode. An equivalent consumption minimization strategy (ECMS) combined with a dual-loop particle swarm optimization (PSO) algorithm was designed to solve the optimal control problem. A MATLAB/Simulink simulation was performed using a well-calibrated model of the target HEV to validate the proposed method, and the results showed that it can achieve a reduction in fuel consumption of around 1.3% to 9.9%, depending on the driving cycle. In addition, the operating power of the battery can be significantly reduced, which benefits the health of the battery. Furthermore, the proposed ECMS-PSO is computationally efficient, which guarantees fast offline optimization and enables real-time applications. [ABSTRACT FROM AUTHOR]

In the context of intelligent and connected transportation, obtaining the real-time vehicle status and comprehensive traffic data is crucial for addressing challenges related to speed optimization and energy regulation in intricate transportation situations. This paper introduces a control method for the speed optimization and energy management of a fuel cell hybrid bus (FCHB) based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The strategy framework is built on a dual-objective optimization deep reinforcement learning (D-DRL) architecture, which integrates traffic signal information into the energy management framework, in addition to conventional state spaces to guide control decisions. The aim is to achieve "green wave" traffic while minimizing hydrogen consumption. To validate the effectiveness of the proposed strategy, simulation tests were conducted using the SUMO platform. The results show that in terms of speed planning, the difference between the maximum and minimum speeds of the FCHB was reduced by 21.66% compared with the traditional Intelligent Driver Model (IDM), while the acceleration and its variation were reduced by 8.89% and 13.21%, respectively. In terms of the hydrogen fuel efficiency, the proposed strategy achieved 95.71% of the performance level of the dynamic programming (DP) algorithm. The solution proposed in this paper is of great significance for improving passenger comfort and FCHB economy. [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]

The research and development of hybrid electric vehicles has become a significant goal for large automotive manufacturers. The hybrid electric vehicle integrates a conventional engine and one or more electric motors powered by a battery, offering better fuel economy and lowering exhaust emissions. This paper develops an optimal energy management algorithm based on Model Predictive Control that can produce optimal control parameters for power distribution between the battery unit and generator. The energy management strategy adapts this optimal power distribution by adjusting the objective function equivalent parameter of the controller according to changes in driving conditions. Dynamic programming is utilized offline to find the reference state of charge of the battery and used as the reference trajectory of our proposed strategy. Simulation results using different driving cycles show that the proposed method has better power distribution compared with two other strategies. The final state of charge reached a higher level, and the energy-saving percentage rose compared to the conventional algorithm. [ABSTRACT FROM AUTHOR]

Plug-in Hybrid Electric Vehicles (PHEVs) represent an innovative breed of transportation, harnessing diverse power sources for enhanced performance. Energy management strategies (EMSs) that coordinate and control different energy sources is a critical component of PHEV control technology, directly impacting overall vehicle performance. This study proposes an improved deep reinforcement learning (DRL)-based EMS that optimizes real-time energy allocation and coordinates the operation of multiple power sources. Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces. They often fail to strike an optimal balance between exploration and exploitation, and their assumption of a static environment limits their ability to adapt to changing conditions. Moreover, these algorithms suffer from low sample efficiency. Collectively, these factors contribute to convergence difficulties, low learning efficiency, and instability. To address these challenges, the Deep Deterministic Policy Gradient (DDPG) algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay (PER) method, aiming to improve exploration performance and learning efficiency from experience samples. Additionally, the corresponding Markov Decision Process (MDP) is established. Finally, an EMS based on the improved DRL model is presented. Comparative simulation experiments are conducted against rule-based, optimization-based, and DRL-based EMSs. The proposed strategy exhibits minimal deviation from the optimal solution obtained by the dynamic programming (DP) strategy that requires global information. In the typical driving scenarios based on World Light Vehicle Test Cycle (WLTC) and New European Driving Cycle (NEDC), the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption (EFC) of 2696.77 g. Compared to the DP strategy baseline, the proposed method improved the fuel efficiency variances (FEV) by 18.13%, 15.1%, and 8.37% over the Deep Q-Network (DQN), Double DRL (DDRL), and original DDPG methods, respectively. The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance, stability, and reliability, effectively optimizing vehicle economy and fuel consumption. [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]

The power split plug-in hybrid electric bus (PHEB) boasts the capability for concurrent decoupling of rotation speed and torque, emerging as the key technology for energy conservation. The optimization of energy management strategies (EMSs) and powertrain parameters for PHEB contributes to bolstering vehicle performance and fuel economy. This paper revolves around optimizing fuel economy in PHEBs by proposing an optimization algorithm for the combination of a multi-layer rule-based energy management strategy (MRB-EMS) and powertrain parameters, with the former incorporating intelligent algorithms alongside deterministic rules. It commences by establishing a double-planetary-gear power split model for PHEBs, followed by parameter matching for powertrain components in adherence to relevant standards. Moving on, this paper plunges into the operational modes of the PHEB and assesses the system efficiency under each mode. The MRB-EMS is devised, with the battery's State of Charge (SOC) serving as the hard constraint in the outer layer and the Charge Depletion and Charge Sustaining (CDCS) strategy forming the inner layer. To address the issue of suboptimal adaptive performance within the inner layer, an enhancement is introduced through the integration of optimization algorithms, culminating in the formulation of the enhanced MRB (MRB-II)-EMS. The fuel consumption of MRB-II-EMS and CDCS, under China City Bus Circle (CCBC) and synthetic driving cycle, decreased by 12.02% and 10.35% respectively, and the battery life loss decreased by 33.33% and 31.64%, with significant effects. Subsequent to this, a combined multi-layer powertrain optimization method based on Genetic Algorithm-Optimal Adaptive Control of Motor Efficiency-Particle Swarm Optimization (GOP) is proposed. In parallel with solving the optimal powertrain parameters, this method allows for the synchronous optimization of the Electric Driving (ED) mode and the Shutdown Charge Hold (SCH) mode within the MRB strategy. As evidenced by the results, the proposed optimization method is tailored for the EMSs and powertrain parameters. After optimization, fuel consumption was reduced by 9.04% and 18.11%, and battery life loss was decreased by 3.19% and 7.42% under the CCBC and synthetic driving cycle, which demonstrates a substantial elevation in the fuel economy and battery protection capabilities of PHEB. [ABSTRACT FROM AUTHOR]

Abstract 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.

To reduce hydrogen consumption in fuel cell electric vehicles, mitigate power fluctuations, and maintain the battery system's state of charge, a novel transition process recognition method based on work condition recognition framework of energy management strategy is proposed. This method offers the advantages such as higher recognition rates and faster recognition speed comparing with the traditional condition recognition methods. A comparison is made with the commonly used LVQ recognition method, and the simulations are conducted to demonstrate the superiority of this condition recognition algorithm and the improved performance of the energy management strategy based on the present recognition method under mixed work conditions.

Single-gear-ratio plug-in hybrid vehicles (SRPHEVs) are favored by major manufacturers due to their excellent energy-saving potential, simple structure, ease of maintenance and control, great cost-saving potential, and the benefits of vehicle lightweighting. Implementing an energy management strategy (EMS) is the key to realizing the energy-saving potential of PHEVs. In this paper, based on a newly developed coaxial configuration, P1-P3 SRPHEV, with the purpose of reducing PHEV fuel consumption, the advantages of various methods were synthesized. An improved intelligent optimization algorithm, the Particle Swarm Optimization (PSO) algorithm, was used to find the optimal rule-based strategy parameters. The PSO algorithm could be easily adjusted to the parameters and obtains the desired results quickly. Different long-distance speed profiles tested under real-world driving cycle (RDC) conditions were used to validate the fuel savings. And an energy flow analysis was conducted to further investigate the reasons for the algorithm optimization. The results show that the optimization plans of the PSO algorithm in different cycle conditions can improve the equivalent fuel consumption of vehicles in different long-distance conditions. Considering the optimization effect of the equivalent fuel consumption and actual fuel consumption, the best case of the equivalent fuel consumption is improved by 2.98%, and the actual fuel consumption is improved by 2.37%. Through the energy flow analysis, it is found that the energy-saving effect of the optimization plan lies in the following principle: lowering the parallel mode switching threshold to increase the parallel mode usage time and to reduce the fuel–mechanical–electrical transmission path loss, resulting in increasing the energy utilization of the whole vehicle. [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]

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]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The growing use of proton-exchange membrane fuel cells (PEMFCs) in hybrid propulsion systems is aimed at replacing traditional internal combustion engines and reducing greenhouse gas emissions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial and has led to a growing emphasis on developing energy management systems (EMSs) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydrogen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention on reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches—control based, optimization based, and deterministic rule based—are conducted to verify the effectiveness of the proposed strategy. Simulation results indicated that the EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distribution between the fuel cell and energy storage systems. [ABSTRACT FROM AUTHOR]

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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]

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]

Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively. [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]

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 energy management system of a fuel cell electric robot should be highly responsive to provide the required power for various tactical operations, navigation of different routes, and acceleration. This paper presents a new multi-level online energy management strategy for a fuel cell electric robot based on the proposed functions of equivalent hydrogen fuel value evaluation, classification of the battery state of charge via the squared combined efficiency function, identification of the robot maneuver condition based on the proposed operation state of robot function, improvement of the overall energy efficiency based on the proposed function of the battery overcharge control, and separation of the functional points of the fuel cell based on the operational mode control strategy. The simulation study of the proposed online multi-level energy management strategy was carried out with MATLAB R2018b software to verify its superiority by comparing with other strategies. The results indicate a reduction in hydrogen consumption, reduction in fuel cell power fluctuations, prevention of battery overcharging, and incrementation in the total energy efficiency of energy storage systems compared to other energy management strategies. [ABSTRACT FROM AUTHOR]