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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.

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 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.

The fuel economy of hybrid electric vehicles largely depends on the transmission system parameters and energy management strategy. However, due to the interaction between the transmission system parameters and energy management strategies on the optimization of fuel economy, and the difficulty in balancing the real-time and global optimality of energy management strategies, there is still a lack of an effective implementation method. This study takes the hybrid electric mining truck as a research object, establishes the mathematical model of the whole vehicle, takes reducing fuel consumption as the goal, establishes a collaborative optimization model that comprehensively considers the interaction between transmission system parameters and energy management strategies, and combines particle swarm optimization (PSO) algorithm and dynamic programming (DP) algorithm to build a two-layer interactive optimization algorithm to eliminate the interaction between transmission system parameters and energy management strategies. The optimal energy distribution can be achieved while optimizing the transmission parameters. On this basis, in order to solve the problem that the DP algorithm is difficult to realize online control, the optimal solution of DP energy management corresponding to the optimal transmission parameters is integrated, an effective method to extract the optimal control rules of DP is designed, and an energy management strategy based on the optimal control rules is established to realize online control of hybrid electric mining trucks. Thus, the integrated realization method of the coordinated optimization of transmission system parameters and energy management strategy, as well as the optimal energy management real-time control strategy, is established. The obtained optimal transmission system parameters and corresponding optimal energy management real-time control strategy are simulated and verified on the Stateflow-AMESim joint simulation platform. The results show that the fuel consumption results obtained by this method are better than those obtained by DP optimization alone, and they are closer to the results obtained by PSO-DP optimization, indicating that the optimization method is effective and feasible.

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.

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.

One of the key famous problems of a fuel cell (FC) is its slow dynamic response: the FC requires significant time to reach a new steady-state condition after a sudden change in the load. Consequently, the addition of supercapacitor and battery storage devices is highly appreciated. The SC will cover the rapid change in load, whereas the battery will supply load dusting deficit periods in the steady state. An energy management strategy (EMS) is highly required to optimize load sharing among SC, FC, and battery. For electric vehicle applications, optimizing hydrogen consumption is one of the most important challenges and an addition to saving the battery life. There are common EMSs, such as PI control and fuzzy logic control (FLC) strategies. PI is characterized by high consumption of hydrogen and high SOC of battery. In contrast, FLC is characterized by low consumption of hydrogen and the final SOC of the battery. Therefore, it is essential to combine both to build a hybrid PIFLC control strategy to acquire the advantages of both FLC and PI control strategies and to avoid high consumption of hydrogen and low SOC of battery. To evaluate the proposed strategy, the common FTP-75 drive cycle has been considered. The results demonstrated the superiority of the hybrid PIFLC strategy. Using the hybrid PIFLC succeeded in increasing the total performance index by 3.43 % and 9.1 %, respectively, compared with PI and FLC.

Hybrid energy storage systems (HESS), combining lithium-ion batteries and supercapacitors (SC), are increasingly used in electric vehicles (EVs) to leverage the high energy density of batteries with the high-power density of SC. Effective operation of HESS relies on an energy management strategy (EMS) that efficiently balances power distribution. The standard power follower (PF) EMS is widely used but can induce abrupt battery current changes, impacting battery health. This study introduces a refined PF EMS employing an exponential smoothing function with a single adjustable parameter to stabilize SC reference power, reducing abrupt transitions in battery current. A comprehensive evaluation was conducted through both large-scale simulations and small-scale experimental setups. Simulations, using a tuned smoothing factor of α = 0.03, demonstrated that the refined EMS achieved balanced performance across various sdriving cycles, with RMS battery current reductions of approximately 4.5 % in the new European driving cycle (NEDC) and 2.2 % in the worldwide harmonized light vehicles test cycle (WLTC). Experimental validation on a small-scale setup verified these improvements, demonstrating a reduction in peak battery current to 0.674 A, which outperforms the original PF (0.754 A) and FS (1.297 A) methods. Additionally, the proposed EMS achieved an 18.73 % reduction in RMS battery current compared to the original PF while maintaining a balanced rate of change (ROC) at 208.21 A/s. These findings emphasize the refined EMS's potential to enhance battery lifespan and energy efficiency, offering a practical and scalable solution for EV applications.

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 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.

Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method.

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.

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.

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.

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.

In addressing challenges associated with the constrained enhancement of range performance in pure electric tractors, the difficulty in achieving genuinely eco-friendly agricultural machinery with hybrid powertrains, and the outdated approaches to energy optimization in hydrogen fuel cell tractors, a comprehensive methodology covering the entire spectrum of system design, optimization, and validation is presented. This method constructs an integrated electromechanical and hydraulic power bond graph model for an agricultural tractor, encompassing energy systems, drive systems, and lifting systems. It achieves rapid interaction between 20-sim software and Matlab/Simulink software through a script toolbox. In order to tackle the real-time applicability challenges of dynamic programming, a hierarchical dynamic programming is proposed. The dynamic programming strategy results serve as the upper-level output. At the lower layer, preprocessing of impact value variables is carried out to filter the input variables for the general regression neural network. This enables the real-time application of the dynamic programming algorithm and significantly reduces training time. Finally, validation is conducted through both model-in-the-loop and hardware-in-the-loop. The results demonstrate the correctness and superiority of the designed tractor simulation model and energy management strategy model compared to the power-following strategy. Specifically, in plowing conditions, the dynamic programming and hierarchical dynamic programming strategies exhibited reductions of 10.278% and 6.728%, respectively, in equivalent hydrogen consumption compared to power-following strategy. In transportation conditions, the reductions are 16.02% for dynamic programming strategy and 4.87% for hierarchical dynamic programming strategy, both relative to power-following strategy. This study lays the theoretical groundwork for modeling dynamic systems of tractors and optimizing energy management.

The rotary motion field of heavy equipment is a large energy-consuming field, characterized by frequent cyclical start-stop, significant load inertia, and braking energy is usually dissipated as thermal energy, for energy regeneration, no good solution has been found. This paper proposes a new energy-regenerative hybrid drive (ERHD) system and defines three operating modes. Taking the reversible rolling mill’s roll as load, and taking the energy consumption of electric motor (EM) and peak power as evaluation indexes, the influence law of key component parameters on the driving characteristics of the system is studied. Simulation models for the pure electric motor drive (PEMD) system and ERHD system were established by AMESim, along with a rule-based energy management strategy (EMS). The results show that the hydraulic pump/motor displacement has the greatest influence on braking time, peak power, and energy consumption. Under the optimal parameters, the proposed ERHD system can reduce the peak power of EM by 45.75% and energy consumption by 48.33% compared with the PEMD system. Starting and braking times were reduced by 44.54% and 1.21%, respectively. Based on the test results, the ERHD system reduces start-up time by 22.70% and peak power by 23.72%. The proposed system will significantly contribute to developing the rotary motion field of heavy equipment in terms of energy efficiency, low carbon and green.

The automobile industry is regarded as one of the primary producers of greenhouse gas emissions. Hence, one major approach for promoting global sustainability is vehicle electrification. Energy source hybridization is imperative in vehicle electrification because no alternative clean energy source can match the performance of vehicles with internal combustion engines. For a compact energy source system with minimal source degradation and low operational costs, it is crucial to determine the optimal sizing of multiple sources in a hybrid electric vehicle (HEV). The objective of this article is to conduct a thorough assessment of the optimal energy source sizing methodology for HEV alongside an analysis of energy management systems. The importance of dynamic energy source characteristics in the optimal source design is explained and the dynamic modelling of these sources is investigated in detail. Moreover, this paper discusses the dynamic source degradation models used in the literature. This analysis on HEV component sizing provides a comprehensive picture of the current state-of-the-art and highlights several shortcomings, difficulties, and knowledge gaps. The findings indicate that the establishment of an energy management system and energy source sizing are interdependent activities that can prolong the usable life of the energy sources and reduce energy consumption. The aging, temperature, depth of discharge, and charging state are found to be essential characteristics of vehicle energy sources, yet these dynamic aspects have largely been neglected in previous papers. Additionally, if the source deterioration and associated factors are considered throughout the design phase, the HEV system can be built with long-life characteristics. The assessment conclusions highlight the significance of continued analysis to overcome obstacles and improve the hybrid automobile sector.

Energy management strategies (EMSs) for hybrid vehicles have been extensively studied to achieve high system efficiency. EMSs usually focus on the torque split between the electric motor and the main power source. Other powertrain components, such as the gearbox or battery management system, are optimized individually. However, the cooperation between different powertrain components has been studied for specific hybrid architectures and demonstrated to be highly beneficial. A modular EMS that ensures the cooperation of multiple components with different characteristics, shared constraints and objectives, while taking advantage of predictive information will be highly beneficial. To address this research gap, a modular cooperative EMS is proposed using parametric controllers with parameter updates realized in the background using available predictive information. The strategy emphasizes modularity, feasibility, and systematically takes advantage of any available predictive information to improve the overall vehicle objectives, hence considering all the components playing a role in the EMS. The proposed cooperative strategy is first detailed for a generic EMS and then demonstrated for the control of the torque split and gear selection of a hybrid electric vehicle. A numerical study is presented to compare the proposed method with the optimal strategy derived from dynamic programming. The results are detailed for different available predictive information, both in terms of quantity and quality. The proposed method is revealed to be robust against incomplete predictive information and guarantees feasibility with low computational effort, making it real-time capable.

This paper presented an optimal design of a grid-independent hybrid renewable energy system (HRES) that comprises Photovoltaic, Biomass, Hydrogen Fuel Cell, and battery storage. Renewable energy-based system have been endorsed for remote off-grid communities electrification. However, it is difficult to design an optimal hybrid energy system due to the stochastic resource nature, load variation, and high cost of renewable components. The sizing of components for the proposed HRES is determined through the application of an innovative metaheuristic optimization technique called salp swarm algorithm (SSA). Addressing the limitations of the salp swarm algorithm, which include low precision, optimization dimension and convergence rate, a modified version of the salp swarm algorithm (SSA), known as the Levy and sine cosine operator-based (LSC-SSA), was introduced. The proposed algorithm is compared with standard SSA and Genetic Algorithm (GA). The primary goal of the research is to reduce the annualized cost of the hybrid system, whilst taking into account the reliability constraint. The novelty of this research lies in its approach to enhance the performance of a HRES by optimizing its size and energy management strategy (EMS). It is achieved by employing a combined framework that integrate the proposed LSC-SSA into the supervisory EMS. The potential benefits of this approach include reducing the cost of energy and the annualized system cost. The comparative results validate that the LSC-SSA surpasses the standard SSA, and GA algorithms examined, by realizing significant cost reductions amounting to ${\$}$ 82,023, and ${\$}$ 202,127 respectively. Additionally, the outcome indicates that, the LSC-SSA offers the least cost of energy (COE) of ${\$}$ 0.927/kWh, in comparison with the COE values of ${\$}$ 0.931/kWh for SSA and ${\$}$ 0.949/kWh for GA, which are higher. Furthermore, the results indicate that the applied supervisory EMS has effectively assisted the establishing an eco-friendly and economical energy system.

A charging station that integrates renewable energy sources is a promising solution to address the increasing demand for electric vehicle (EV) charging without expanding the distribution network. An efficient and flexible energy management strategy is essential for effectively integrating various energy sources and EVs. This research work aims to develop an Energy Management System (EMS) for an EV charging station (EVCS) that minimizes the operating cost of the EVCS operator while meeting the energy demands of connected EVs. The proposed approach employs a model-free method leveraging Deep Reinforcement Learning (DRL) to identify optimal schedules of connected EVs in real time. A Markov Decision Process (MDP) model is constructed from the perspective of the EVCS operator. The real-world scenarios are formulated by considering the stochastic nature of renewable energy and the commuting behavior of EVs. Various DRL algorithms for addressing MDPs are examined, and their performances are empirically compared. Notably, the Truncated Quantile Critics (TQC) algorithm emerges as the superior choice, yielding enhanced model performance. The simulation findings show that the proposed EMS can offer an enhanced control strategy, reducing the charging cost for EVCS operators compared to other benchmark methods.

In campus facilities, classrooms and labs often use a large amount of energy due to their variable consumption pattern and lack of use management. Installing solar photovoltaic (PV) rooftop in the campus building through net-energy-metering (NEM) scheme has recently increased to reduce energy usage as well as generate income from the sales of excess solar energy to the grid. Meanwhile, the Malaysian government has introduced the Enhanced Time of Use (ETOU) tariff to encourage consumers to shift their electricity usage to reduce their bills through demand response. Despite offering different rates at various times of the day, the ETOU tariff may lead to an increase in electricity expenses if consumers do not effectively shift their consumption to lower rate hours and eventually using more during peak hours. Additionally, there has been lack of quantitative analysis conducted to determine how effective the compensation scheme provided by NEM. Therefore, considering a university building that holds a commercial electricity tariff, this study aims to develop an optimum energy management strategy integrating demand response and solar energy rooftop using improved Evolutionary- Particle Swarm Optimisation (EPSO). To determine the best load shifting strategy, the model was used to evaluate two Universiti Teknologi MARA (UiTM) campuses where NEM solar PV rooftop systems were installed. The analysis involved testing various weightage factors for controlling the loads, ranging from 5% to 20% under 2 different electricity tariffs offered in Malaysia. Results show the electricity cost under EPSO provides a significant cost reduction compared to PSO for both tariffs with a lesser computational time. Furthermore, EToU tariff with a weightage factor of 20% gives a higher energy and cost saving compared to fixed tariff. The findings from this research will assist the energy users in managing the load and generation from solar, whilst supporting the policymakers in designing effective compensation schemes.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Energy management strategies (EMS) for the flexible traction power supply system (FTPSS) typically depend on the accurate predictive model or probabilistic estimation of traction load and renewable energy generation. However, it can be challenging due to the ultra-short-term dramatic fluctuation in traction load profile. To address the issue, a model-free EMS is proposed to achieve the power dispatch in real time based on the current state. Firstly, a sequential stochastic optimization model is built to optimize the long-term operating cost and load fluctuation. Through the relaxation of time-coupling constraints and incorporation of the Lyapunov optimization technique, the problem is decomposed into individual optimization problems at each time slot. Considering the complementary characteristic of hybrid energy storage system, a bi-level optimization framework is constructed to separately control powers of the supercapacitor and battery with the hierarchical sequence method. More importantly, a fully informative control strategy with the known future load profile and renewable energy generation is adopted as the evaluation benchmark. The gap between the proposed method and the benchmark can be demonstrated to fall within a certain range of below 10% by mathematical derivation and extensive cases.

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.

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.

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.

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.

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.

The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.

The wide distribution of data centers and the delay tolerance of computing tasks endow data center loads with adjustable characteristics in both temporal and spatial dimensions. Due to the characteristics of abundant spatiotemporal flexibility, data centers can participate in the optimization of power system operation and regulation. To quantify this flexibility, this paper proposes a spatiotemporal load migration potential evaluation model. Meanwhile, a data center energy management strategy is combined with the characteristics of spatiotemporal load migration, which deeply exploits the migration potential through the spatiotemporal redistribution of delay-tolerant tasks, as well as server ON/OFF scheduling and CPU operating frequency scaling across different spatial locations. A case study demonstrates that adopting the proposed approach considering an energy management strategy can effectively improve the load migration potential of data centers. The migration characteristics of data centers have great application prospects in reducing carbon emissions and enhancing operational flexibility.

Introduction. Nowadays, environmental pollution becomes an urgent issue that undoubtedly influences the health of humans and other creatures living in the world. The growth of hydrogen energy increased 97.3 % and was forecast to remain the world’s largest source of green energy. It can be seen that hydrogen is one of the essential elements in the energy structure as well as has great potential to be widely used in the 21st century. Purpose. This paper aims to propose an energy management strategy based a fuzzy logic control, which includes a hybrid renewable energy sources system dedicated to the power supply of a three-phase AC variable load (unpredictable high dynamic). Photovoltaic (PV), fuel cell (FC), Li-ion battery, and supercapacitor (SC) are the four sources that make up the renewable hybrid power system; all these sources are coupled in the DC-link bus. Unlike usual the SC was connected to the DC-link bus directly in this research work in order to ensure the dominant advantage which is a speedy response during load fast change and loads transient. Novelty. The power sources (PV/FC/Battery/SC) are coordinated based on their dynamics in order to keep the DC voltage around its reference. Among the main goals achieved by the fuzzy control strategy in this work are to reduce hydrogen consumption and increase battery lifetime. Methods. This is done by controlling the FC current and by state of charge (SOC) of the battery and SC. To verify the fuzzy control strategy, the simulation was carried out with the same system and compared with the management flowchart strategy. The results obtained confirmed that the hydrogen consumption decreased to 26.5 g and the SOC for the battery was around 62.2-65 and this proves the desired goal.

Renewable energy (solar and wind) sources have evolved dramatically in recent years around the globe, primarily because they have the potential to generate environmentally friendly energy. However, operating systems with high renewable energy penetration remain challenging due to the stochastic nature of these energy sources. To tackle these problems, the authors propose a state machine-based energy management strategy combined with a hysteresis band control strategy for renewable energy hybrid microgrids that integrates hydrogen storage systems. By considering the power difference between the renewable energy source, and the demand, the battery’s state of charge, and the hydrogen storage level, the proposed energy management strategy can control the power of fuel cells, electrolyzers, and batteries in a microgrid and the power imported into/exported from the main grid. The results showed that the energy management strategy provides the following advantages: (1) the power supply and demand balance in the microgrid was balanced, (2) the lifespans of the electrolyzer and fuel cell were extended, and (3) the state of charge of the battery and the stored level of the hydrogen were appropriately ensured.

This paper proposes a collaborative optimization strategy for speed planning and energy management of intelligent plug-in hybrid electric vehicles (PHEVs). In this study, a single-axis parallel PHEV with the powertrain of P2 configuration is employed as the research object. Then, dynamic programming (DP) is leveraged to ensure optimal fuel economy with the consideration of the simultaneous optimal demand torque distribution and autonomous speed selection. An adjustment coefficient that reasonably constrains the feasible domain of vehicle speed is designed to limit the range of autonomous speed selection according to driving conditions. The reasonableness of speed selection is enhanced by adding penalty functions to inhibit gear shifting and speed fluctuations. To consider the variability of the control strategies based on the constraints of travel time and distance, a time-domain and a space-domain collaborative optimization model are established respectively, and a simulation analysis of the collaborative optimization energy management strategy is conducted. The simulation result shows that the strategy achieves the collaborative optimization of speed autonomous planning and reasonable allocation for demand torque. In addition, the proposed strategy demonstrates preferable energy economy under different constraints based on time and space domains.

Energy management strategies with prediction message show great potential in optimizing control objectives for fuel cell hybrid vehicles. This paper presents a novelty model-predictive-control based energy management framework for fuel cell commercial vehicle, in which the vehicle mass is firstly introduced as a variable parameter for controller. A vehicle mass identification model is established and embed in the framework based on recursive least square algorithm, and the influence of variable algorithm parameters on estimator is evaluated. Then the effects of mass varying on vehicle performance is talked about in detail. In addition, the performance for different drive cycles on different loaded is also given. The simulation results show that the fuel consumption is positively correlated with the mass identification error, and the designed controller can reduce the additional fuel consumption to around 0.1%, which is much batter to the deterministic parameters scheme. Finally, the shortcomings of the designed controller are given. This paper provides a reliable theoretical basis to address varying-mass vehicle energy management problems.

Hybrid construction machinery (HCM), in recent times, has been widely used in building and other industries due to its outstanding advantages in reducing greenhouse gas (GHG) emissions, preventing harmful impacts on the environment, and limiting fossil fuel consumption. Many scholars have demonstrated that the development of an energy management strategy (EMS) is important to determine sufficient power distribution and ensure output performance for HCM. Although conventional EMSs can deliver the required energy effectively, advances in technology indicate that the optimization of EMS is indispensable to optimize fuel consumption, enhance working efficiency, cost-saving, and prolong the lifetime of devices. Aiming to provide a study on the relevant works and prospects, this paper presents a comprehensive review of optimization-based EMS techniques for types of engine hybrid and fuel cell hybrid construction machinery to deal with the existing limitations and make an underpinning for future improvements. First, HCM configurations are briefly described including engine hybrid and fuel cell hybrid construction machinery. Second, the optimization-based EMSs for engine hybrid configuration are clearly analyzed with the optimization rules, objectives, and parameters. Third, optimization techniques of EMSs for fuel cell hybrid configuration are reviewed involving global optimization-based, local optimization-based, and the combination of global and local optimization-based. Finally, technological challenges, commercial opportunities, and recommendations for future development are discussed to contribute more ideas for the innovation of optimization-based EMSs of HCM.

Extended range electric vehicles (EREVs) are an effective solution to solve the lack of driving range of pure electric vehicles. Reducing the fuel consumption of EREVs and prolonging the service lifetime of battery play a positive role in solving environmental pollution and energy crisis. In this paper, hybrid energy storage system (HESS) is introduced into EREVs, and two energy management strategies based on HESS are proposed to reduce fuel consumption and prolonging the service life of battery. This paper proposes two energy management strategies for EREVs based on a HESS to reduce fuel consumption and the amount of energy flowing through the battery. HESS adopts the rule-based energy management strategy, whereas Auxiliary power unit (APU) adopts the equivalent fuel consumption minimization strategy (ECMS) or the rule-based energy management strategy as its first and second hierarchical energy management strategies respectively, both of which are embedded into the charge depleting/charge sustaining (CD/CS) control strategy. A simulation model for an EREV is built in AVL Cruise to verify the effectiveness of these strategies. The battery charge and discharge C-rate and the energy consumption of EREVs without HESS under the ECMS and rule-based energy management strategy are then compared. Results show that under the first hierarchical energy management strategy, the average discharge C-rate and cumulative ampere hours of the battery are the lowest, thereby reducing its operating load and extending its service lifetime. Meanwhile, for those EREVs without HESS, using the rule-based energy management strategy can reduce fuel consumption by 11.33%, whereas using ECMS increases fuel consumption by no more than 1.61%. Therefore, the first hierarchical energy management strategy proposed in this study can effectively extend the service lifetime of the battery and reduce the fuel economy of EREVs.