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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]

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]

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]

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]

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]

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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]

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]

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]

The key to improving the fuel economy of plug-in hybrid electric vehicles (PHEVs) lies in the energy management strategy (EMS). Existing EMS often neglects engine operating conditions, leading to frequent start–stop events, which affect fuel economy and engine lifespan. This paper proposes an Integrated Engine Start–Stop Dynamic Programming (IESS-DP) energy management strategy, aiming to optimize energy consumption. An enhanced rule-based strategy is designed for the engine's operating conditions, significantly reducing fuel consumption during idling through engine start–stop control. Furthermore, the IESS-DP energy management strategy is designed. This strategy comprehensively considers engine start–stop control states and introduces weighting coefficients to balance fuel consumption and engine start–stop costs. Precise control of energy flow is achieved through a global optimization framework to improve fuel economy. Simulation results show that under the World Light Vehicle Test Cycle (WLTC), the IESS-DP EMS achieves a fuel consumption of 3.36 L/100 km. This represents a reduction of 6.15% compared to the traditional DP strategy and 5.35% compared to the deep reinforcement learning-based EMS combined with engine start–stop (DDRL/SS) strategy. Additionally, the number of engine start–stop events is reduced by 43% compared to the DP strategy and 16% compared to the DDRL/SS strategy. [ABSTRACT FROM AUTHOR]

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices' (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs' SOCs are obtained. [ABSTRACT FROM AUTHOR]

Abstract 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 The new energy vehicle plays a crucial role in green transportation, and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving. This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems. Additionally, it envisions the outlook for autonomous intelligent hybrid electric vehicles, with reinforcement learning as the foundational technology. First of all, to provide a macro view of historical development, the brief history of deep learning, reinforcement learning, and deep reinforcement learning is presented in the form of a timeline. Then, the comprehensive survey and review are conducted by collecting papers from mainstream academic databases. Enumerating most of the contributions based on three main directions—algorithm innovation, powertrain innovation, and environment innovation—provides an objective review of the research status. Finally, to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles, future research plans positioned as “Alpha HEV” are envisioned, integrating Autopilot and energy-saving control.

Hybrid energy storage system (HESS) can stabilize renewable energy power generation, but unreasonable energy storage power distribution and photovoltaic-load forecast errors will affect the economic benefits of the whole system. Aiming at the microgrid (MG) composed of photovoltaic (PV) and HESS, an energy management strategy (EMS) of MG considering forecast errors is proposed. Firstly, an optimization model considering the depreciation cost of battery is established. Secondly, day-ahead EMS is implemented under multiple operating modes considering minimum fluctuation and optimal economy. Then, according to the real-time forecast results and the feedback of system operation status, the intraday rolling energy management strategy (REMS) is developed to alleviate the impact of forecast errors. Finally, the real-time state of charge (SOC) of the supercapacitor (SC) is introduced to adjust the filter coefficient, which avoids the SC working in the charge/discharge restricted area for a long time and improves the adjustment effect. The results of the case analysis show that the proposed intraday REMS can effectively reduce the influence of forecast errors on energy management.

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]

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]

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]

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]

The environment pollution and petroleum problems, which are increasingly becoming serious, have caused the vehicle industry to transition into a low-carbon and energy-saving industry. During processes, plug-in fuel-cell electric vehicles (PFCEVs) play an important role due to their advantages of rapid fueling, high energy density and efficiency, low operating temperature, and zero onboard emissions. PFCEVs use high-capacity rechargeable batteries to avoid working in low-efficiency areas. However, a robust energy management strategy that can achieve reliable energy distribution by regulating the output power of the fuel cell and battery within the hybrid powertrain merits further investigation. Considering the close relationship between the driving cycle, state of charge (SOC), equivalent factor, and hydrogen consumption, a trip distance adaptive equivalent consumption minimum strategy integrating driving cycle prediction is proposed. A backpropagation-based neural network is used to predict short-term vehicle velocity and analyze future changes in vehicle demand power. Planning a path to the destination with the help of the global positioning system, the intelligent transportation system can also obtain traffic flow information for the entire trip. The equivalent factor is dynamically corrected in real time using the driving distance and SOC to realize the adaptability of the energy management strategy. Finally, the velocity prediction sequence is combined with the objective function. The sequential quadratic programming algorithm is used to optimize the equivalent hydrogen consumption of the objective function and to obtain the distributed power of the fuel cell and battery. The vehicle simulation model is built and compared with a traditional energy management strategy based on MATLAB/Simulink software. The simulation results show that the driving cycle prediction algorithm based on the backpropagation-based neural network predicts future short-term conditions better, with a 12.5% higher accuracy than the Markov method. The proposed energy management strategy allows the fuel cell to operate in high-efficiency areas. The hydrogen consumption is 55.6% less than that of the CD/CS strategy under the UDDS cycle. The hardware in the loop experiment verifies a hydrogen consumption that is 26.8% less than that of the CD/CS strategy under the EUDC cycle. The numerical validation results demonstrate the superior performance of the proposed strategy in terms of hydrogen consumption over the CD/CS strategy. The effectiveness of the proposed strategy is validated by hardware during the loop experiment.

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.

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.

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.

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%.

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.