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This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is critical for improving the economy of hybrid powertrains and the durability of energy sources. In this paper, a novel EMS based on a twin delayed deep deterministic policy gradient algorithm (TD3) is proposed for a fuel cell hybrid electric bus (FCHEB) to optimize the driving cost of the vehicle. First, a TD3-based energy management strategy is established to embed the limits of battery aging and fuel cell power variation into the strategic framework to fully exploit the economic potential of FCHEB. Second, the TD3-based EMS is compared and analyzed with the deep deterministic policy gradient algorithm (DDPG)-based EMS using real-world collected driving conditions as training data. The results show that the TD3-based EMS has 54.69% higher training efficiency, 36.82% higher learning ability, and 2.45% lower overall vehicle operating cost compared to the DDPG-based EMS, validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The performance of the proposed convex optimization algorithm, dual loop DP optimization algorithm, and nonlinear optimization algorithm is compared. The results show that the hydrogen consumption of electric vehicles optimized by the convex optimization algorithm is 95.364 g. This consumption is lower than 98.165 g of the DCDP optimization algorithm and 105.236 g of the nonlinear optimization algorithm before optimization. It is also significantly better than the 125.59 g of electric vehicles before optimization. The calculation time of the convex optimization algorithm optimization is 4.9 s, which is lower than the DCDP optimization algorithm and nonlinear optimization algorithm. The above results indicate that convex optimization algorithms have better optimization performance. After optimizing the power battery using a convex optimization algorithm, the overall performance of electric vehicles is higher. Therefore, this method can effectively improve the performance of current electric vehicle power batteries, make new energy vehicles develop rapidly, and improve the increasingly serious environmental pollution and energy crisis in China. [ABSTRACT FROM AUTHOR]

This article introduces an enhanced energy management strategy that employs the state of charge (SoC) of batteries in standalone DC microgrids with photovoltaic (PV) modules. Efficient energy management is crucial to ensure uninterrupted power supply to the load units in microgrids. To address the challenges posed by external factors such as temperature fluctuations and variations in solar irradiance, energy storage systems are deployed to compensate for the negative effects of the external factors on the output power of PV modules. The proposed approach takes into account various parameters of the microgrid elements, including the available power from the sources, demand power, and the SoC of batteries, in order to develop an efficient energy control mechanism with load-shedding capability. By considering these parameters, the strategy aims to optimize the utilization of available resources while ensuring a reliable power supply to the connected loads. The SoC of the batteries plays a critical role in determining optimal charging and discharging profiles, enabling effective energy management within the microgrid. To evaluate the effectiveness of the proposed approach, an algorithm is designed and simulations are conducted. The proposed algorithm utilizes a hybrid approach by combining power and SoC-based methods for efficient control. Through analysis of the simulation results, it is found that the presented approach is capable of delivering the intended load power while increasing the life cycle of the batteries with the pre-defined SoC levels.

In response to the issues of hybrid tractors’ energy management strategies, such as reliance on experience, difficulty in achieving optimal control, and incomplete analysis of typical operating conditions of tractors, an energy management strategy based on dynamic programming is proposed in combination with various typical operating conditions of tractors. This is aimed at providing a reference for the modeling and energy management strategies of series hybrid tractors. Taking the series hybrid tractor as the research object, the tractor dynamics models under three typical working conditions of plowing, rotary tillage, and transportation were established. With the minimum total fuel consumption of the tractor as the optimization target, the engine power as the control variable, and the state of charge of the power battery as the state variable, an energy management strategy based on a dynamic programming algorithm was established and simulation experiments were conducted. The simulation results show that, compared with the power-following energy management strategy, the energy management strategy based on the dynamic programming algorithm can reasonably control the operating state of the engine. Under the three typical working conditions of plowing, rotary tillage, and transportation, the battery SOC consumption increased by approximately 8.37%, 7.24%, and 0.77%, respectively, while the total fuel consumption decreased by approximately 25.28%, 21.54%, and 13.24%, respectively.

Energy management strategies for hydrogen fuel cell hybrid electric vehicles (FCHEVs) are a key factor in achieving real-time vehicle energy optimization control, vehicle driving economy, and fuel cell durability. In this paper, for an FCHEV equipped with a fuel cell and supercapacitor, the quantitative information, logic rules, and operational constraints are transformed into linear integer inequalities according to its different operating modes, and the Hysdel language is used to establish its mixed logic dynamic model (MLD). Then, the energy management strategy based on model predictive control (MPC) is developed using the MLD model as the prediction model and the equivalent hydrogen consumption and the performance degradation of the fuel cell as the optimization performance indexes. Finally, under the World Light Vehicle Test Cycle, a joint simulation was carried out with Advisor and Simulink to verify the proposed strategy’s superiority by comparing it with the power following control strategy (PFCS) and the compound fuzzy control strategy (CFCS). The results show that the strategy not only ensures real-time FCHEV energy control, but also reduces hydrogen consumption by 10.98% and 1.98% and the number of start/stop times of a fuel cell by six and four, compared to PFCS and CFCS, respectively, which improves the economy of the whole vehicle as well as the durability of the fuel cell.

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table.

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

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

Aiming to solve the problems of insufficient dynamic responses, the large loss of energy storage life of a single power cell, and the large fluctuation in DC (direct current) bus voltage in fuel cell vessels, this study takes a certain type of fuel cell ferry as the research object and proposes an improved equivalent minimum hydrogen consumption energy management strategy, based on fuzzy logic control. First, a hybrid power system including a fuel cell, a lithium–iron–phosphate battery, and a supercapacitor is proposed, with the simulation of the power system of the modified mother ship. Second, a power system simulation model and a double-closed-loop PI (proportion integration) control model are established in MATLAB/Simulink to design the equivalent hydrogen consumption model and fuzzy logic control strategy. The simulation results show that, under the premise of meeting the load requirements, the control strategy designed in this paper improves the Li-ion battery’s power, the Li-ion battery’s SOC (state of charge), the bus voltage stability, and the equivalent hydrogen consumption significantly, compared with those before optimization, which improves the stability and economy of the power system and has certain practical engineering value.

Traditional mine dump truck using diesel-electric drive technology have high energy consumption and serious pollution. To improve fuel economy of a hybrid power dump truck, firstly, it analyzes system composition, working mode and transportation condition characteristics of the hybrid power electric-wheel mining dump truck. Secondly, according to the mine driving conditions of the electric-wheel dump truck, an energy management strategy based on the driving pattern recognition is proposed according to the driver's intention and the vehicle state. Finally,to minimize the instantaneous equivalent fuel consumption of the whole hybrid power system, the equivalent consumption minimization strategy is used to optimize the instantaneous power distribution between the engine and battery. In order to verify the proposed energy management strategy, on site mining road test is conducted. Test results show that the fuel consumption of the hybrid power electric-wheel dump truck is 45.52 L/trip, which is 10.2% lower than that of the electric-wheel dump truck with full diesel engine mode.

Abstract The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field of electric vehicles. In this paper, a fuzzy energy management strategy on the state-of-charge (SOC) estimation of power battery is proposed. Particle filter (PF) algorithm is used to estimate SOC of power battery, then estimated result is regarded as the input variable of fuzzy energy management controller, and the energy distribution result is obtained after fuzzy logic operation. The simulation results show that the SOC estimation result of the PF algorithm is closer to the actual value of power battery SOC. When the SOC estimation result of PF is embedded into the fuzzy controller for joint simulation, it is found that the charge and discharge current, and SOC consumption of the power battery are reduced, which shows the algorithm’s effectiveness. It also provides a specific reference value for the further study of the power supply control strategy of hybrid electric vehicles.

A real-time adaptive energy management strategy (EMS) used a model-based predictive control algorithm that continuously adapted to the changing driving patterns and traffic conditions. With battery degradation in an operation being considered, the algorithm was designed to minimize the total cost of electricity consumed by vehicles with hybrid energy storage systems (HESSs) while ensuring that the battery and supercapacitor cell were not overcharged or overdischarged. First, the objective function was taken as the instantaneous minimization of the comprehensive cost. Second, a hierarchical instantaneous optimal control EMS (HIOC-EMS) was suggested to solve the optimal power coupling coefficient of the supercapacitor that satisfied the constraints at any moment. Third, the HIOC-EMS was proven to be an efficient and robust method for optimizing the energy management system of HESSs. The experimental results of three different driving cycles showed that the HIOC-EMS, when compared to the particle swarm-optimized fuzzy EMS (PFZY-EMS), achieved reductions in battery losses of 18.41%, 13.94%, and 20.37% and comprehensive cost reductions of 11.16%, 7.37%, and 9.61%, respectively, in the three cycles. Furthermore, compared to the dynamic programming EMS (DP-EMS), the HIOC-EMS resulted in increased battery losses of 14.87%, 10.77%, and 4.87% and increased comprehensive costs of 8.48%, 2.98%, and 1.55%, respectively. These results proved the effectiveness of the HIOC-EMS in reducing the usage cost of electric vehicles with HESSs.

Plug-in Hybrid Electric Vehicles offer a promising solution for the increasing CO2 emission problem. However, the improved economy strongly depends on the energy management strategy. Traditional rule-based strategies are no more practical considering the increasing complexity in control objectives. In this study, an adaptive online Reinforcement Learning (RL) agent is developed, which learned an energy management strategy with a near-optimal performance. A novel hybrid approach is proposed to integrate the agent into the existing rule-based hybrid control unit architecture with a limited operation domain for more practicality and suitability to series-production control systems. Dynamic Programming (DP) and rule-based strategy are used to benchmark the developed RL agent performance. The objective is to minimize the vehicle’s total fuel consumption and the frequent engine on/off switching to improve driver comfort and vehicle drivability. Several RL-based algorithms have been experimented and as a result, an Extended-Deep Q-Network (E-DQN) agent is proposed by this paper, trained on one cycle, and deployed on two other cycles with different onboard energy levels to evaluate the performance. The paper findings showed that E-DQN outperformed the rule-based strategy achieving up to 10.46% improvement in fuel economy closer to the DP performance alongside providing adequate compliance with the vehicle drivability and driver comfort objectives.

In the paper, a novel self-learning energy management strategy (EMS) is proposed for fuel cell hybrid electric vehicles (FCHEV) to achieve the hydrogen saving and maintain the battery operation. In the EMS, it is proposed to approximate the EMS policy function with fuzzy inference system (FIS) and learn the policy parameters through policy gradient reinforcement learning (PGRL). Thus, a so-called Fuzzy REINFORCE algorithm is first proposed and studied for EMS problem in the paper. Fuzzy REINFORCE is a model-free method that the EMS agent can learn itself through interactions with environment, which makes it independent of model accuracy, prior knowledge, and expert experience. Meanwhile, to stabilize the training process, a fuzzy baseline function is adopted to approximate the value function based on FIS without affecting the policy gradient direction. Moreover, the drawbacks of traditional reinforcement learning such as high computation burden, long convergence time, can also be overcome. The effectiveness of the proposed methods were verified by Hardware-in-Loop experiments. The adaptability of the proposed method to the changes of driving conditions and system states is also verified.

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

As one of the effective and crucial ways to achieve the energy saving and emission reduction of loaders, hybrid technology has attracted the attention of many scholars and manufacturers. Selecting an appropriate energy management strategy (EMS) is essential to reduce fuel consumption and emissions for hybrid loaders (HLs). In this paper, firstly, the application status of drivetrain configuration of HLs is presented. Then, the working condition characteristics of loaders are analyzed. On the basis of this, the configurations of HLs are classified, and the features and research status of each configuration are described. Next, taking the energy consumption optimization of HLs as the object, the implementation principle and research progress of the proposed rule strategy and optimization strategy are compared and analyzed, and the differences of existing EMSs and future development challenges are summarized. Finally, combining the advantages of intelligent control and optimal control, the future prospective development direction of EMSs for HLs is considered. The conclusions of the paper can be identified in two points: firstly, although the existing EMSs can effectively optimize the energy consumption of HLs, the dependence of the strategy on the mechanism model and the vehicle parameters can reduce the applicability of the strategy to heterogeneous HLs and the robustness to a complex working condition. Secondly, combining the advantages of intelligent control and optimal control, designing an intelligent EMS not depending on the vehicle analytical model will provide a new method for solving the energy consumption optimization problem of HL. [ABSTRACT FROM AUTHOR]

This article presents a comparative study between the performances of different energy management strategies for hybrid energy storage source supplying electric vehicle. In our case, the hybrid supply is composed of fuel cell as an energy source and lithium-ion batteries as a power buffer. The two storage systems are connected to the DC bus via DC/DC boost converters. The used management strategies influence greatly the hybrid energy storage source performances. For this reason, a new online strategy is developed to improve the fuel consumption and the hybrid source lifetime. The performances (hydrogen consumption and applied stress on each source) of this new strategy are compared with those obtained with literature strategies for 700 km of range. Furthermore, an experimental validation of two online energy management strategies is carried out to validate the simulation results using 1-kW test bench. The experimental results prove that the developed strategy improves hydrogen consumption compared to the frequency strategy and it improves also the lifetime of the hybrid storage system. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes an energy management strategy for a series hybrid electric vehicle based on driving pattern recognition, driving condition prediction, and model predictive control to improve the fuel consumption while maintain the state of charge of the battery. To further improve the computational efficiency, the discretization and linearization of the model is conducted, and the MPC problem is transferred into a quadratic programming problem, which can be solved by the interior point method effectively. The simulation is carried out by using Matlab/Simulink platform, and the simulation results verify the feasibility of the condition prediction method and the performance of the proposed method. In addition, the predictive control strategy successfully improves the fuel economy of the hybrid vehicle compared with the rule-based method. [ABSTRACT FROM AUTHOR]

In this paper, model predictive control (MPC) based energy management strategy is developed for dual motor coupled plug-in hybrid vehicle (PHEV) configuration. Firstly, the dynamic characteristics and working mode of dual motor coupled PHEV configuration are analyzed. Secondly, the rule-based strategy and dynamic programming (DP) strategy are developed and selected as the benchmark. Thirdly, vehicle speed prediction accuracy of three different prediction models for various time domain lengths is investigated considering the possibility of real-time applications. Thus, RBF neural network prediction model is applied in the following MPC strategy. Finally, the MPC strategy is developed through the rolling optimization and local optimization of DP strategy, which achieves the optimal torque distribution between engine and motors. Comparative analysis for rule-based, DP and MPC strategies are performed. It is seen that the MPC strategy can obtain similar fuel consumption with DP strategy, while the MPC strategy has certain real-time application potential with only 0.269s per single step calculation time. Compared with the rule-based strategy, the MPC achieves obvious fuel consumption and total cost improvement by 14.5% and 12.6% respectively. Therefore, MPC strategy can effectively improve the fuel economy and has certain real time application potential. [ABSTRACT FROM AUTHOR]

The hybrid electric dump truck is equipped with multiple power sources, and each powertrain component is controlled by an energy management strategy (EMS) to split the demanded power. This study proposes an EMS based on deep reinforcement learning (DRL) algorithm to extend the battery life and reduced total usage cost for the vehicle, namely the twin delayed deep deterministic policy gradient (TD3) based EMS. Firstly, the vehicle model is constructed and the optimization objective function, including battery aging cost and fuel consumption cost, is designed. Secondly, the TD3-based EMS is used for continuous action control of ICE power based on vehicle state, and the action mask is applied to filter out invalid actions. Thirdly, the simulations of the EMSs are trained under the CHTC-D driving cycle and C-WTVC driving cycle. The results show that the action mask improves the convergence efficiency of the strategies, and the proposed TD3-based EMS outperforms the deep deterministic policy gradient (DDPG) based EMS. Meanwhile, the battery life is extended by 36.17% under CHTC-D and 35.49% under C-WTVC, and the total usage cost is reduced by 4.30% and 2.49% when the EMS considers battery aging. In summary, the proposed TD3-based EMS can extend the battery life and reduce usage cost, and provides a method to solve the optimization problem for the EMS of hybrid power systems. [ABSTRACT FROM AUTHOR]

Due to the continuous high traction power impact on the energy storage medium, it is easy to cause many safety risks during the driving process, such as triggering the aging mechanism, causing rapid deterioration of the battery performance during the driving process and even triggering thermal runaway. Hybrid energy storage is an effective way to solve this problem. The ultracapacitor is an energy storage device that has high power density, which can withstand high instantaneous currents and can be charged and discharged quickly. By combining batteries and ultracapacitors in a hybrid energy storage system, energy sources with different characteristics can be combined to take advantage of their respective strengths and increase the efficiency and lifetime of the system. The energy management strategy plays an important role in the performance of hybrid energy storage systems. Traditional optimization algorithms have difficulty improving the flexibility and practicality of applications. In this paper, an energy management strategy based on reinforcement learning is proposed. The results indicate that the proposed reinforcement method can effectively distribute the charging and discharging conditions of the power supply and maintain the SOC of the battery and, at the same time, meet the power demand of working conditions at the cost of less energy loss and effectively realize the goal of optimizing the overall efficiency and effective energy management strategy. [ABSTRACT FROM AUTHOR]

A frequency-decoupling-based power split was used in this study to manage a direct-current microgrid (DC-MG)-based PV and hybridized energy storage system (HESS), which consisted of a battery and a supercapacitor. The HESS control integrated a dual-loop structure for bus voltage regulation and recovery and HESS charge/discharge control. Hysteresis current control (HCC) tracked the supercapacitor setpoint current using low-pass filtering (LPF) control. The system reliability was assessed using pulsing, smooth, and disturbed PV and load waveforms with standard and advanced LPF methods. The Matlab simulation results confirmed a superior HESS control performance under varying resource waveforms. Key performance indexes (KPIs) were used to evaluate the bus voltage, HESS current management, loading efficiency, and loading mismatch. [ABSTRACT FROM AUTHOR]

Hybrid electric air-ground vehicles (HEAGVs), which can run on the land and fly in the air, are considered a promising future transportation. The operation of HEAGVs, accompanied by high energy consumption, could lead to increasing battery temperature, which may affect the lifespan of the battery. To make the battery last longer and improve energy efficiency, an effective energy management strategy (EMS) is necessary for the operation of HEAGVs. In this regard, this paper proposes a predictive EMS based on model predictive control (MPC). Firstly, speed information is obtained by intelligent network technology to achieve a prediction of power demand, and then the state of charge (SOC) reference trajectory is planned. Secondly, a Pontryagin's minimum principle-based model predictive control (PMP-MPC) framework is proposed, including battery thermal dynamics. Under the framework, fuel efficiency is improved by reducing the temperature of the battery. Finally, the proposed method is compared to PMP, dynamic programming (DP), and rule-based (RB) methods. The effect of different preview horizon sizes on fuel economy and battery temperature is analyzed. Verification results under two driving cycles indicate that compared with the rule-based method, the proposed method improves fuel economy by 5.14% and 5.2% and decreases the temperature by 5.9% and 4.9%, respectively. The results demonstrate that the proposed PMP-MPC method can effectively improve fuel economy and reduce temperature. [ABSTRACT FROM AUTHOR]

Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin's minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective optimization energy management strategy for a parallel hybrid electric vehicle is established to balance fuel economy and battery temperature rise effect. A vehicle dynamics model and a battery temperature rise model are also established. A multi-objective parameter optimization based on Pareto optimal solutions is built by selecting the control parameters of power components and several battery related parameters. Considering the temperature accumulation effect under high rate currents, the upper limit control strategy of a motor torque based on temperature rise feedback is developed, so that the current amplitude is actively controlled to realize battery temperature control. The analysis results indicate that frontier Pareto solutions obtained by multi-objective optimization have a good balance between both fuel economy and battery temperature rise response. The motor torque threshold control strategy effectively adjusts current amplitude under high speed conditions, thus achieving the limit of battery heat accumulation. [ABSTRACT FROM AUTHOR]

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

In grid-connected operations, a microgrid can solve the problem of surplus power through regeneration; however, in the case of standalone operations, the only method to solve the surplus power problem is charging the energy storage system (ESS). However, because there is a limit to the capacity that can be charged in an ESS, a separate energy management strategy (EMS) is required for stable microgrid operation. This paper proposes an EMS for a hybrid AC/DC microgrid based on an artificial neural network (ANN). The ANN is composed of a two-step process that operates the microgrid by outputting the operation mode and charging and discharging the ESS. The microgrid consists of an interlinking converter to link with the AC distributed system, a photovoltaic converter, a wind turbine converter, and an ESS. The control method of each converter was determined according to the mode selection of the ANN. The proposed ANN-based EMS was verified using a laboratory-scale hybrid AC/DC microgrid. The experimental results reveal that the microgrid operation performed stably through control of individual converters via mode selection and reference to ESS power, which is the result of ANN integration. [ABSTRACT FROM AUTHOR]

The fuel economy and ride comfort of range-extender electric vehicles are closely related to the adopted energy management strategy (EMS). The appropriate EMS with noise constraints can not only effectively reduce fuel consumption, but improve ride comfort during vehicle travel. To this end, an EMS was proposed in this paper in which an adaptive equivalent consumption minimization method was developed considering the noise level in the driving cabin based on vehicle speed feature recognition so that the vehicle fuel economy and ride comfort can be optimized simultaneously through a calibratable cost function. The linear state of charge reference trajectory based on the route information, which could be obtained by a mileage-indication knob equipped in the cockpit, was employed to determine the start-stop of the range-extender. The proposed strategy was verified on the road in a light-duty range-extender electric van. The results were compared with those obtained from the conventional equivalent consumption minimization strategy and a dynamic equivalent consumption minimization strategy. The experimental results indicated that the proposed strategy could reduce the vehicle fuel consumption by 3.28 % and 1.40 %, respectively, on an urban-suburban integrated route. Moreover, the ride comfort indicated by the noise level in the driving cabin was better as well. [ABSTRACT FROM AUTHOR]

To address the instability of the input voltage of photovoltaic (PV) in a stand-alone PV storage power generation system, a wide input range non-isolated three-port converter that can operate in a range that is greater than and less than the voltage of the storage port is proposed in this paper. The proposed converter can realize the energy flow and power balance between the PV, the battery, and the load. This converter is a combination of conventional Buck, Boost, and Buck–Boost converters with a four-switch bi-directional Buck–Boost converter (FSBB). The FSBB is used to connect the PV and battery ports, which can reduce the voltage constraint of the storage port on the PV port voltage, and broaden the selection of the voltage levels of the storage port to meet the application requirements of the PV port with a wide voltage input. In this paper, an experimental prototype is designed by combining the Boost converter and the FSBB converter. Finally, the correctness of the theoretical analysis and the feasibility of the energy management strategy of the converter are verified through experimental results. [ABSTRACT FROM AUTHOR]

Predictable and unpredictable variations are two major causes of renewable energy-related intermittency. Such a drawback could be overcome by applying new energy management strategies, particularly storage systems. This paper investigates the feasibility of introducing a photovoltaic management string, enabling the maintenance of continuous energy supply storage capacity. Accordingly, the PV system is liable to perform at maximum efficiency following the implementation of an MPPT-controlled DC/DC boost converter of the Perturb and Observe (P&O) type. Most of the energy-management approaches target goals lie mainly in optimizing the energy resources' operational and storage capacities. The current MATLAB/Simulink-based simulation study achieved results that turned out to testify well to the advanced design's high performance and efficiency in maintaining the load's energy autonomy while increasing the battery's life span. [ABSTRACT FROM AUTHOR]

Diesel-electric hybrid propulsion system (HPS) is widely applied for shunting locomotive due to the characteristics of flexible configuration, economic and environmental protection in the world. Energy management strategy (EMS) is an important design factor of HPS that can optimize the energy distribution of each power sources, improve system efficiency, and reduce fuel consumption. In this paper, the model of HPS for shunting locomotive and system operating profile are firstly carried out. Then the EMS consist of the conventional rule-based (RB) strategy rule, and a fuzzy neural network base on dynamic programming (FNN-DP) strategy are studied. Finally, the simulations were carried out with these EMSs in the system model at full operating conditions to derive the fuel consumption. The conclusion is that the theoretical optimal solution of DP provides reference and guidance for the fuzzy neural network strategy to improve the rules, and the fuel consumption of the FNN-DP strategy is 10.2% lower than the conventional RB strategy. [ABSTRACT FROM AUTHOR]

Due to the complexity of energy systems, they require robust control methods to ensure efficient operation. This is particularly true when reconfiguration is required to increase the system’s resilience. One such scenario is when Peer-to-Peer (P2P) energy trading is employed. This work uses systems theory to model and optimize complicated systems when several assets with heterogeneous characteristics are interconnected. This is achieved by adopting graph theory, propositional logic and state space analysis of dynamical systems. This approach simplifies P2P energy exchange and improves the system’s efficiency and resilience. The results obtained showed that the complexity of modelling P2P systems using structures like flowcharts increases with addition or introduction of new peers to the network and/or constraints on the network’s Energy Management System (EMS). The results obtained were validated by comparing the outcome of a P2P system modelled using E-variables and that modelled using structures like flowchart. We proved that using the proposed approach, it is easy to model such complicated systems. Since it is scalable, it can easily accommodate changes in the EMS (which manages how subsystems within a microgrid operate in order to ensure system efficiency) without significantly modifying the representation of the EMS.

Aiming at the problems that existing energy management strategies (EMS) are rarely applied to 100-ton loaders and the engine start-stops frequently under complex driving conditions, this paper proposes a novel EMS for 100-ton extended range hybrid loaders based on an artificial tree algorithm (AT). Firstly, using the equivalent fuel consumption minimization strategy (ECMS) as a foundation, a penalty function is designed to restrict the range extender's start-stop frequency and integrated into the ECMS control framework. Secondly, a real-time driving condition recognition model based on AT optimized back propagation (BP) Neural Network is proposed. Finally, with the equivalent factor, scale factor of state of charge (SOC) penalty function and range extender start-stop penalty function as optimization variables, and fuel economy, SOC stability, and range extender start-stop frequency as optimization objectives, AT is used for multi-objective optimization to obtain the optimal control parameters corresponding to the identified driving conditions. The simulation results demonstrate that compared with ECMS and Proportional-Integral-Derivative (PID) based ECMS, the proposed strategy is more effective for maintaining SOC stability. Besides, it improves the fuel economy by 5.937% and 1.353%, respectively, and decreases the number of range extender start-stops by 50.000% and 55.556%, respectively. • A novel energy management strategy applied to extended range hybrid loaders. • Integration of driving condition recognition and APU start-stop optimization. • A novel optimization strategy based on an artificial tree algorithm. • BP neural network optimized by artificial tree algorithm has better performance. • Fuel economy, SOC stability, and APU start-stop frequency have been improved. [ABSTRACT FROM AUTHOR]

The recent advancements in artificial intelligence have promoted deep reinforcement learning (DRL) as the preferred method for developing energy management strategies (EMSs) for fuel cell vehicles (FCVs). However, the development of DRL-based EMSs is a time-consuming process, requiring repetitive training when encountering different vehicle types or learning tasks. To surmount this technical barrier, this paper develops a transferable EMS rooted in heterogeneous deep transfer reinforcement learning (DTRL) across both FCV types and optimization tasks. Firstly, a simple source EMS based on the soft actor-critic (SAC) algorithm is pre-trained for a fuel cell sedan, solely focusing on hydrogen saving. After that, a heterogeneous DTRL framework is developed by integrating SAC with transfer learning, through which both heterogeneous deep neural networks and experience replay buffers can be transferred. Subsequently, the source EMS is transferred to the target new EMS of a fuel cell bus (FCB) to be reused, with additional consideration of the fuel cell (FC) longevity. Experimental simulations reveal that the heterogeneous DTRL framework expedites the development of the new EMS for FCB by 90.28 %. Moreover, the new EMS achieves a 7.93 % reduction in hydrogen consumption and suppresses FC degradation by 63.21 %. By correlating different energy management tasks of FCVs, this article both expedites the development and facilitates the generalized application of DRL-based EMSs. • A heterogeneous DTRL framework is designed by integrating TL with SAC algorithm. • A transferable EMS across FCV types and learning tasks is proposed based on DTRL. • Both heterogeneous DNNs and experience replay buffer are transferred and reused. • Fuel economy performance and fuel cell longevity are collaboratively optimized. • The proposed EMS shows great potential for real-time application via online test. [ABSTRACT FROM AUTHOR]

The propulsion and power systems are critical to the performance of unmanned aerial vehicles (UAVs). The rapid development of electrified propulsion systems powered by proton exchange membrane fuel cells (PEMFCs) has driven technological innovation of propulsion and power systems for UAVs. In this work, a 1.5 kW PEMFC integrated propulsion system with direct airflow intake design (Beihang Hydrogen-1) is developed and verified by flight test on a UAV with a wingspan of 3.6 m. The propulsion system can directly use the airflow behind the propeller to feed the PEMFC cathode for cooling and reaction and the power generated by the PEMFC is used to drive the propeller. A DC-DC module and a lithium battery pack are also added to the propulsion system to stabilize the output voltage and increase the instantaneous power for takeoff, transition, and landing stages. The "dynamic distribution" energy management strategy for the propulsion system is proposed to increase the system dynamic response ability. The detailed data of the PEMFC integrated propulsion system during the flight are measured and analyzed. The flight test continued for 18 min, with an average cruising altitude and an average cruising speed of 101 m above sea level and 23 m/s, respectively. During the cruise stage, the average PEMFC power is 1688 W, with an average single-cell voltage of 0.65 V. The average charging power of PEMFC for the lithium battery is 41 W. The successful flight test verifies the feasibility of the design of the PEMFC integrated propulsion system, provides valuable data for the following optimal design and flight test of the PEMFC integrated propulsion systems, and opens up a new direction for the development and application of PEMFC-powered electrified propulsion systems. • A 1.5 kW PEMFC integrated propulsion system with direct airflow intake design is developed. • The "dynamic distribution" energy management strategy is proposed for the PEMFC integrated propulsion system. • The first flight verification of the PEMFC integrated propulsion system is conducted based on a UAV. • The average PEMFC power is 1688 W with an average charging power of 41 W for the battery in the cruise stage of the flight test. • The PEMFC integrated propulsion system shows excellent dynamic response ability and energy conversion efficiency. [ABSTRACT FROM AUTHOR]

Generalization to unseen environments is still a challenge for deep reinforcement learning (DRL)-based energy management strategies (EMSs). This paper proposes a real-time EMS with high generalization for a light-duty hybrid electric vehicle (HEV) from two perspectives: enhancing the generalization of the DRL algorithm and improving the accuracy of application scenario representation in the training environment. The enhanced DRL algorithm named ATSAC can adjust the update frequency and learning rate of SAC automatically to improve the generalization. With the advancement of naturalistic driving big data (NDBD) and machine learning, a specific training cycle is synthesized based on NDBD to reflect an urban-suburban real-world driving scenario more accurately. By the comprehensive comparison with SAC and TD3 based EMSs applied to unseen driving scenarios, the proposed algorithm achieves significant improvement in computational efficiency, optimality, and generalization. The results show that the computational efficiency of ATSAC is increased by 52.32% compared to SAC. The negative total reward (NTR) of ATSAC is decreased by 18.22% and 69.81% compared to SAC and TD3, respectively. Further analysis shows that the EMS trained through the synthetic driving cycle obtains 18.37% lower NTR than WLTC which demonstrates that the synthetic method can reflect the state transition probability of real-world driving scenarios better than WLTC. • A novel DRL algorithm with high generalization is researched for energy management. • An SODC is synthesized through big data and machine learning as the training cycle. • Iteration dropout can avoid overfitting to improve the generalization of DRL. • Adaptive learning rate can balance the exploration and exploitation. [ABSTRACT FROM AUTHOR]

As the mobile energy storage and load-side demand response device, energy dispatch potential of electric vehicles (EVs) in energy supply system is yet to be fully explored. This study presented a multi-energy incorporating integrated energy system (IES) with carbon capture system and power-to-gas for residential building and EVs, five optimal scheduling strategies considering EVs charging/discharging, co-dispatch of EVs and IES, and stationary battery characteristic are proposed. Optimization objectives, such as annual total cost (ATC), annual carbon emission (ACE) and total grid interaction (TGI), considering configuration and operation scheduling for IES under five scenarios can be determined through augmented ε-constraint and entropy weight-TOPSIS method, furthermore the impacts of carbon tax, battery degradation cost and the number of EVs on system performances are also studied. The results indicated that optimal ATC of IES is 6361.8 thousand yuan in Case 4 considering EVs ordered charging/discharging, optimum ACE and TGI are 4711.5 t and 2850.7 MWh in Case 5 with EVs ordered charging/discharging and state of fixed battery. Peak values and durations for charging/discharging electricity of fixed battery decrease in Case 4 and 5. This research offers a innovative idea for optimal design and operational management of integrated energy system with EVs orderly charging/discharging. [ABSTRACT FROM AUTHOR]