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

An energy management strategy (EMS) is important in marine hybrid propulsion systems while the global optimal solutions are more significant for the EMS to improve the fuel economy of system. Aiming at finding the more accurate global optimal solutions for EMS, an improved dynamic programing (DP) algorithm was proposed to overcome the basic DP algorithms' weaknesses and improve the fuel economy. Then the model of gas-electric hybrid propulsion system was also established to verify the strategies. What's more, the improved DP algorithm was applied to the minimum natural gas consumption and charge-sustaining strategy. And a bi-level optimization scheme that decoupled the interaction between the EMS and components matching was also studied. The results showed that the improved DP algorithm not only avoided the shortcomings in the basic DP algorithm such as the weak effect of penalty functions and curse of dimensionality but also gave more accurate solutions. Compared to the basic DP, the improved DP algorithm can reduce the gas fuel consumption by 3.67% and the computational complexity decreases 100 times. [ABSTRACT FROM AUTHOR]

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

To improve the fuel economy of ships with multi-energy hybrid power systems, we propose an energy management strategy for hybrid power ships based on condition identification and prediction. By constructing a condition identification model based on a support vector machine (SVM), the characteristic parameters of current operation conditions of the ships were analyzed and assessed to determine operation condition types of the ships in real-time. The model predictive control (MPC) method is used to distribute the output power of the main engine, storage battery, and supercapacitor. Using multiple sets of multi-step Markov models with different types of operating conditions as the power prediction model, the optimal prediction time domain length and initial SOC are selected through simulation. Finally, based on the operating condition identification results, the operating condition prediction model for the corresponding type of operating condition is automatically selected to improve the strategy performance.The results show that the proposed strategy can save 4.55% of fuel consumption compared with the strategy using only model predictive control.Using this strategy can effectively improve the fuel economy and energy efficiency, and promote the development of green ships. [ABSTRACT FROM AUTHOR]

A parallel hybrid vehicle was studied to establish the demand power and power system model of the whole vehicle and proposed an energy management strategy based on improved deep reinforcement learning (DRL). The DRB-TD3 algorithm was proposed to improve the sampling efficiency of the original algorithm by improving the twin delayed deep deterministic policy gradient (TD3) algorithm in DRL and introduced dual replay buffers. A rule-based constraint controller was designed and embedded into the algorithm structure to eliminate unreasonable torque allocation. The performance of the dynamic planning (DP)-based energy management strategy was used as a benchmark for simulation experiment under UDDS driving conditions. The experimental results show that the DRB-TD3 algorithm has the best convergence performance compared with the deep deterministic policy gradient (DDPG) algorithm and the conventional TD3 algorithm, with 61.2$ \% $ and 31.6$ \% $ improvement in convergence efficiency, respectively, the proposed energy management strategy reduces the average fuel consumption by 3.3$ \% $ and 2.3$ \% $ compared with the DDPG- and TD3-based energy management strategies, respectively, the fuel performance reaches 95.2$ \% $ of DP-based, which with the best fuel economy, and the battery SOC can be maintained at a better level, which helps to extend the battery life. [ABSTRACT FROM AUTHOR]

Copyright of Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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.)

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.

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.

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

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

Summary: The design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures. [ABSTRACT FROM AUTHOR]

Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive. [ABSTRACT FROM AUTHOR]

This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an efficient frequency-separating based-energy management strategy (EMS) is designed using Harr wavelet transform (HWT), adaptive low-pass filter, and interval type–2 fuzzy controller (IT2FC) to determine the appropriate power distribution for different power sources. Second, the ultra-local model (ULM) is introduced to re-formulate the FCHEV system by the knowledge of the input and output signals. Then, a novel adaptive model-free integral terminal sliding mode control (AMFITSMC) based on nonlinear disturbance observer (NDO) is proposed to force the actual values of the DC-link bus voltage and the power source's currents track their obtained reference trajectories, wherein the NDO is used to approximate the unknown dynamics of the ULM. Moreover, the Lyapunov theorem is used to verify the stability of AMFITSMC via a closed-loop system. Finally, the FCHEV system with the presented method is modeled on a Matlab/Simulink environment, and different driving schedules like WLTP, UDDS, and HWFET driving cycles are utilized for investigation. The corresponding simulation results show that the proposed technique provides better results than the other methods, such as operational mode strategy and fuzzy logic control, in terms of the reduction of fuel consumption and fuel cell power fluctuations. [ABSTRACT FROM AUTHOR]

This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2) fuel gas supply system (FGSS), proton-exchange membrane fuel cell (PEMFC) and lithium-ion battery systems. This study analyzed the optimized performance of the DRL-EMS and the operational strategy of the LH2-HSPS. To train the proposed DRL-EMS, a reward function was defined based on fuel consumption and degradation of power sources during operation. Fuel consumption for ship propulsion was estimated with the power for balance of plant (BOP) of the LH2 FGSS and PEMFC system. DRL-EMS demonstrated superior global and real-time optimality compared to benchmark algorithms, namely dynamic programming (DP) and sequential quadratic programming (SQP)-based EMS. For various operation cases not used in training, DRL-EMS resulted in 0.7% to 9.2% higher operating expenditure compared to DP-EMS. Additionally, DRL-EMS was trained to operate 60% of the total operation time in the maximum efficiency range of the PEMFC system. Different hydrogen fuel costs did not affect the optimized operational strategy although the operating expenditure (OPEX) was dependent on the hydrogen fuel cost. Different capacities of the battery system did not considerably change the OPEX. [ABSTRACT FROM AUTHOR]

Hydraulic hybrid technology can further improve the economy of electric vehicles (EV). In this article, a novel electric‐hydraulic power coupling vehicle (EHPCV), which is endowed with multiple drive modes and energy regenerative braking modes, is investigated. The electric‐hydraulic power coupler (EHPC) is an innovative device for realizing power coupling and conversion. The design and optimization of the energy management strategy (EMS) are key to ensuring the efficient and stable operation of the hybrid electric vehicle. Based on the analysis of energy flow, a rule‐based EMS (RB‐EMS) is built. To achieve more reasonable energy management of the EHPCV in random driving environments, the article proposes an optimization framework for the EMS based on multiple driving cycles. More precisely, a multiobjective optimization mathematical model is established with the goal of maximizing the battery state of charge and minimizing velocity error. Moreover, the optimal Latin hypercube design is used to select the design variables that have a significant impact on the optimization objective. Definitively, the nondominated sorting genetic algorithm II algorithm combines with RB‐EMS to optimize the control parameters. The verification results show that the optimized EMS enables the EHPCV to have superior economic advantages. [ABSTRACT FROM AUTHOR]

Optimal control is a common control strategy to solve complex non-linear systems. Pontryagin's minimum principle, as a typical optimal control theory, is widely used in energy management systems. Due to the randomness of new energy generation and the diversity of load power consumption, microgrid energy management system is a complex non-linear stochastic system. The efficient operation of microgrid energy management strategy largely depends on the real-time performance of the control algorithm. Pontryagin's minimum principle is an offline control strategy, whose co-state variable λ depends on expert experience and needs to be manually adjusted according to different working conditions, so it is difficult to achieve real-time optimal control. First, the energy management system model of island-type microgrid is established in this paper. In order to improve the economy of power generation and prevent battery over-charge or over-discharge, the state of charge (SOC) change of battery pack is comprehensively considered, and the fuel cost and pollutant treatment cost are taken as the objective function. Second, an adaptive Pontryagin's minimum principle energy management strategy is proposed. By introducing the penalty mechanism and adjusting the parameters adaptively, the problem that the open-loop control of Pontryagin's minimum principle needs to adjust the co-state variables in real time is solved. Comparative experiments show that compared with Pontryagin's minimum principle and particle swarm optimization, adaptive Pontryagin's minimum principle can not only limit battery SOC within the ideal range but also effectively reduce the daily operation cost of microgrid. Experimental results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The hybrid electric train which operates without overhead wires or traditional power sources relies on hydrogen fuel cells and batteries for power. These fuel cell-based hybrid electric trains (FCHETs) are more efficient than those powered by diesel or electricity because they do not produce any tailpipe emissions making them an eco-friendly mode of transport. The target of this paper is to propose low-budget FCHETs that prioritize energy efficiency to reduce operating costs and minimize their impact on the environment. To this end, an energy management strategy [EMS] has been developed that optimizes the distribution of energy to reduce the amount of hydrogen required to power the train. The EMS achieves this by balancing battery charging and discharging. To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. DRL-based strategies are commonly used in energy management and they suffer from unstable convergence, slow learning speed, and insufficient constraint capability. To address these limitations, an action masking technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated is proposed. The DDPG+TL agent consumes up to 3.9% less energy than conventional rulebased EMS while maintaining the battery's charge level within a predetermined range. The results show that DDPG+TL can sustain battery charge at minimal hydrogen consumption with minimal training time for the agent. [ABSTRACT FROM AUTHOR]

With the development of intelligent transportation systems, access to diverse transportation information has become possible. Integrating this information into an energy management strategy will make the energy allocation prospective and thus improve the overall performance of the energy management program. For this reason, this paper proposes a hierarchical model predictive control (MPC) energy management strategy that incorporates traffic information, where the upper layer plans the vehicle's velocity based on the traffic information and the lower layer optimizes the energy distribution of the vehicle based on the planned velocity. In order to improve the accuracy of the planning speed of the upper strategy, a dung beetle optimization-radial basis function (DBO-RBF) prediction model is constructed, artfully optimizing the RBF neural network using the dung beetle optimization algorithm. The results show that the prediction accuracy is improved by 13.96% at a prediction length of 5 s. Further, when the vehicle passes through a traffic light intersection, the traffic light information is also considered in the upper strategy to plan a more economical speed and improve the traffic efficiency of the vehicle and traffic utilization. Finally, a dynamic programming (DP)-based solver is designed in the lower layer of the strategy, which optimizes the energy distribution of the vehicle according to the velocity planned by the upper layer to improve the economy of the vehicle. The results demonstrate achieving a noteworthy 3.97% improvement in fuel economy compared to the conventional rule-based energy management strategy and allowing drivers to proceed through red light intersections without stopping. This proves a substantial performance enhancement in energy management strategies resulting from the integration of transportation information. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology 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.)

Utilizing solar energy is an efficient method to provide hybrid renewable energy system with sufficient thermal/electrical energy. Meanwhile, the rapid development of electrical vehicles leads to an excess of retired electric vehicles. As a combination of the abovementioned two conceptions, this study proposed and examined a hybrid solar-hydrogen-retired electrical vehicle battery energy system to meet thermal and electrical loads for small-scale usage. The novelty of this research is delivered as follows: first of all, the proposed hybrid energy system supplies both thermal and electrical energy to small-scale end users; secondly, the retired electrical vehicle batteries are recycling to relieve the pressure of battery demand; thirdly, an energy management strategy to regulate the complicated hybrid energy system is designed. The results show that with assistance of fuel cell as an energy storage unit, solar energy can basically satisfy the annual thermal/electrical load with maximum monthly energy supplement of 1220.43 MJ and 1572.75 kWh, respectively. However, the solar radiation serving as single energy source is not very reliable for large-scale utilization. Although the state of charge does not fluctuate greatly, the small range charge/discharge between 59% and 63% can still guarantee the normal operation of the proposed hybrid energy system. • The proposed hybrid energy system supplies both thermal and electrical energy to small-scale end uses. • Retired electrical vehicle batteries are recycled to relieve the pressure from battery demand. • An energy management strategy to regulate the complicated hybrid energy system is designed. [ABSTRACT FROM AUTHOR]

In the vehicle-to-everything scenario, the fuel cell bus can accurately obtain the surrounding traffic information, and quickly optimize the energy management problem while controlling its own safe and efficient driving. This paper proposes an energy management strategy (EMS) that considers speed control based on deep reinforcement learning (DRL) in complex traffic scenarios. Using SUMO simulation software (Version 1.15.0), a two-lane urban expressway is designed as a traffic scenario, and a hydrogen fuel cell bus speed control and energy management system is designed through the soft actor–critic (SAC) algorithm to effectively reduce the equivalent hydrogen consumption and fuel cell output power fluctuation while ensuring the safe, efficient and smooth driving of the vehicle. Compared with the SUMO–IDM car-following model, the average speed of vehicles is kept the same, and the average acceleration and acceleration change value decrease by 10.22% and 11.57% respectively. Compared with deep deterministic policy gradient (DDPG), the average speed is increased by 1.18%, and the average acceleration and acceleration change value are decreased by 4.82% and 5.31% respectively. In terms of energy management, the hydrogen consumption of SAC–OPT-based energy management strategy reaches 95.52% of that of the DP algorithm, and the fluctuation range is reduced by 32.65%. Compared with SAC strategy, the fluctuation amplitude is reduced by 15.29%, which effectively improves the durability of fuel cells. [ABSTRACT FROM AUTHOR]

Over the last two decades, there has been a significant interest in developing solutions to improve the efficiency of power systems, such as optimal energy consumption and management. The demand response programs (DRP) have long been one of the most effective strategies to encourage consumers to change their consumption behavior. In this paper, a novel hybrid DRP has been implemented by combining the time of use (TOU) and incentive-based (IB) DRP methods. To meet the load demand, a PV-WT-BESS hybrid renewable energy system (HRES) has been proposed in this work. Further, to analyze the impact of hybrid DRP on the capacity allocation of different system components, optimal sizing has been performed in two scenarios, one with the actual load and the second with the load after considering the hybrid DRP. The optimization has been carried out by using an improved search space reduction (ISSR) algorithm. To demonstrate the effectiveness of the ISSR algorithm, the obtained results are compared with other algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning based algorithm (TLBO), seagull optimization algorithm (SOA), and sine cosine algorithm (SCA). Finally, a novel energy management strategy (EMS) is also proposed in this work on the supply side of the HRES, which intelligently uses the variations in the grid tariff to charge the BESS when the tariff is low. To demonstrate the proposed DRP and EMS-based sizing, a case study has been performed for a grid-connected PV-WT-BESS for the location of Kanyakumari, India. From the results obtained, it has been concluded that the hybrid DRP reduces the peak demand up to 5%, which reduces the levelized cost of energy (LCE) by 22%. Furthermore, the proposed novel EMS reduces the LCE further by 5%. The overall cost reduction of up to 25% has been achieved by using both the proposed hybrid DRP and EMS, while satisfying all necessary constraints. [Display omitted] [ABSTRACT FROM AUTHOR]

High-speed railways generate a large amount of regenerative braking energy during operation but this energy is not utilized efficiently. In order to realize the recycling of regenerative braking energy of high-speed railways, the hybrid energy storage type railway power conditioner (RPC) system is proposed. The working principle and the control strategy of the system are studied. The energy management strategy consisting of a hybrid energy storage system charging and discharging strategy and variational modal decomposition (VMD) power allocation strategy is proposed. Three system operation modes are proposed: the power of the hybrid energy storage system is decomposed by VMD and an interrelationship number is proposed to determine the lithium battery and supercapacitor power. The hardware-in-the-loop test experiments are conducted by the StarSim power electronics small-step real-time simulator from Modeling Tech and the validation analysis is carried out on MATLAB/Simulink with the actual measurement data of a traction substation on the Lanzhou–Xinjiang line. The results verify that the proposed strategy can effectively recycle the regenerative braking energy, realize the peak-shaving effect on the load, and reduce the energy consumption of the train. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) utilizing hybrid energy sources is a significant step toward a sustainable future in the transportation industry. The electric three‐wheeler (3W) considered in the proposed work includes three sources—battery, supercapacitor (SC), and photo‐voltaic (PV) panels. In battery electric vehicles (BEV), battery life cycle, energy efficiency, and performance are affected by variations in driving conditions that inhibit their wider adoption. The main focus of the proposed intelligent hybrid energy management strategy (IHEMS) is to enable the vehicle to adaptively manage and diminish the effects of load fluctuations due to varying conditions. IHEMS diverts the load fluctuations to the SC bank by ensuring an effective absolute energy sharing among the sources with a fuzzy logic control algorithm. PV energy is utilized to assist the battery during sunny days. Performance of the EMS in hybrid source EV is analyzed in MATLAB/SIMULINK environment with a combination of three different standard real‐time driving profiles (NYCC, Artemis Urban, WLTP class‐1). Proposed EMS reduces peak battery power by 20% and 14.35% and improves battery life by 16.4% and 11.4% compared to BEV and conventional EMS, respectively. This proves that the proposed EMS exhibits adaptive energy management irrespective of the driving conditions and ensures improved battery performance and longevity. [ABSTRACT FROM AUTHOR]

Environmental concerns and higher energy demand require more energy. Solar and wind are renewable energies. One of the most important and available renewable energy sources is the use of wind and solar sources. This study examines energy generation and battery storage systems for lowering peak load and smoothing a residential substation's load curve. This study aims to present a useful and effective mechanism for improving the design of a hybrid system using solar panels and wind turbines to provide the common peak load and as much actual load demand as possible at the desired location. The proposed method provides the optimal solution after obtaining light radiation, wind speed, and load demand. Training and learning-based algorithms optimize. This study focuses on reducing lifetime costs. Prices and equipment are accurate, and power plant costs include initial and ongoing costs. PSO optimizes Karachi's anemometer and radiation data. The results showed that the network's summer, fall, and winter peak outputs are 12368 kW, 14865 kW, and 77 147 kW; the systems are 68.31 kW, 29.38 kW, and 2337 kW. Using the seasonal average rather than the annual average improves the system's dependability and provides a more accurate response to the desired peak load. Wind and solar hybrid systems connected to the grid can reduce the grid's peak load and total cost over time. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications 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.)

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.

To increase the transient power limit, and reduce the impact of operating condition changes on the electric vehicles’ powerful battery, it is essential to stabilize the working condition, enhance the vehicle’s dynamic performance and energy utilization. In this paper, a novel mechanical–electro–hydraulic power coupling system for electric vehicles is proposed. It integrates a planetary gear mechanism as a power coupling component with an accumulator, a hydraulic pump/motor, which efficiently converts electrical, mechanical, and hydraulic energy. A rule-based dynamic energy management strategy is established to control the energy distribution and the dynamic switching of working conditions in real time. By analyzing the system’s operating mode, a new vehicle model is established. First, the feasibility and superiority of the new model are verified compared with the pure electric vehicle model. Simultaneously, the vehicle speed jitter problem during electro–hydraulic power switching is eliminated by altering the inclination angle of the secondary element. Ultimately, the verification results illustrate that the battery power consumption is reduced by 14.7%, and the energy recovery rate of the accumulator is as high as 94.3%. Furthermore, reasonable distribution of the torque in two motors through fuzzy control strategy significantly stabilizes the main motor’s working state and improves its overall efficiency.

A fuel cell (FC) is one of the most viable solutions to the current energy crisis and environmental pollution problem. It can be applied in power generation, portable power supply, as well as in hybrid vehicles. Due to the versatile nature of this energy source and non-dependency over the environmental conditions, FCs can be a significant source of alternative energy source in the future. Researchers are working on power conversion topologies and control strategies development for FC application for the past decade. However, the latest summary of the progress in this sector is not available in the literature. This article is aimed at discussing the various control techniques and energy management systems reported in the literature. The configurations are categorized according to their nature, application area, and performance. Different energy management strategies used in fuel cell-based systems are considered here for the qualitative analysis. The essential characteristics such as the advantages, disadvantages, and improvement opportunities of various energy management strategies of fuel cell vehicles are discussed from the control system point of view. Finally, the recent trends in the development of fuel cell hybrid energy management systems are presented to improve the performance based on the above qualitative analysis. This article will help the researchers in this field to know the current status, future trend and the future research directions in fuel cell research for power generation.

Abstract 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.

As environmental problems become increasingly severe, achieving qualitative breakthroughs in the energy consumption and emissions of traditional internal combustion engine vehicles is difficult. In contrast, new energy vehicles are environmentally friendly and have low fuel consumption, which is important for the future development of vehicles. A plug-in hybrid electric vehicle (PHEV) is widely regarded as the most promising alternative solution for improving energy efficiency and reducing emissions. The optimization of the energy management strategy (EMS) mainly focuses on reducing fuel consumption and improving the economy. However, the durability of the power battery also needs attention, as the lack of life remains a major obstacle to the large-scale commercialization of PHEVs. Because PHEV batteries can obtain relatively cheap power through the grid, the traditional control strategy only considers the full use of the battery power but ignores its excessive use, which will accelerate the decline of the power battery capacity. Therefore, determining how to make full use of the battery power and control the decline of the battery capacity is a new research focus. Based on the semiempirical decay model of the battery, the energy management strategy of balancing the degradation of the battery capacity was established by introducing the battery utilization degree factor. The multiobjective optimization problem was transformed into a single-objective problem by selecting the appropriate weight factor through the Pareto noninferior target domain. A dynamic programming algorithm was used to obtain the global optimal solution of the weight coefficient. The optimal weight coefficient was selected by weighing the fuel consumption and battery capacity decline degree under different weights. In the case of equivalent fuel consumption, the decay rate of battery life can be effectively inhibited when the weight coefficient is 0.9. Finally, the validity of the proposed solution is verified by comparing the online equivalent consumption minimization strategy (ECMS) simulation with the dynamic programming solution under the same weight.

The integration of PV and energy storage systems (ESS) into buildings is a recent trend. By optimizing the component sizes and operation modes of PV-ESS systems, the system can better mitigate the intermittent nature of PV output. Although various methods have been proposed to optimize component size and achieve online energy management in PV-ESS systems, the optimal interconnection between them has received less attention. In order to maximize the effectiveness of systems with limited component sizes and address the impact of uncertainty on the system, an optimization framework is proposed for determining the optimal size of the PV-ESS system. The proposed framework consists of five parts: determination of optimal size, analysis of component output characteristics, system state prediction, parameter calibration of energy management strategies, and update of system components output features, and it considers uncertain factors, including climate, different components, and battery degradation caused by irregular charging and discharging, to establish the model for energy saving. To validate the results, four different climates in a year were considered. The obtained results indicate that the proposed framework can effectively achieve the optimal working state of the system, realizing a matching degree of 94.55% between the offline size optimization and online management strategy. The proposed framework's universality and effectiveness were demonstrated through simulation analysis across four cities with different climates in China. [ABSTRACT FROM AUTHOR]

This article suggests a hybrid DC microgrid (HDCMG) with different levels of DC bus voltages to use for various types of loads. The available sources in the HDCMG are wind generating systems (WGSs), photovoltaic (PV) systems, battery banks, and the AC grid for emergencies. The various levels of the DC bus voltages are 760 V, 380 V, and 48 V for different application uses such as electric vehicles and home applications. In addition, the controller plays an important role in the proposed system to achieve the desired DC bus voltage levels and extract the maximum power point (MPP) from the WGS and PV systems. In order to check the power continuity for the critical loads and improve the overall system performance, a suggested energy management strategy (SEMS) is developed. The SEMS is based on the optimum generated power and the state-of-charge (SOC) of the battery banks. Further, the SEMS is developed as a way to prevent battery storage from overcharging and deep discharging. The mathematical relations of the proposed HDCMG and MPP tracking are described. The bidirectional 3-Φ inverter connects the 760 V bus voltage to the AC grid for regulating this DC bus by absorbing the excess power or supplying the required power during the shortage in the generation and the low SOC of the battery storage. Buck converters with controlled duty cycles rather than constant duty cycles are used to obtain 380 V and 48 V from 760 V to achieve better dynamic responses. The overall HDCMG is evaluated using the MATLAB/Simulink package under different working cases to verify the capability of the control system and the PEMS. The obtained results are discussed and show the good performance and the capability of the overall system under the different scenarios, including (i) a comparison between variable duty and constant duty; (ii) high/low generated power and the SOC of the battery in the acceptable region; (iii) high/low generated power and the SOC of the battery in the critical region; and (iv) high/low generated power and the SOC of the battery in the overcharging region. [ABSTRACT FROM AUTHOR]

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives. [ABSTRACT FROM AUTHOR]

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The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance. [ABSTRACT FROM AUTHOR]

Passivity-based control is a well-known strategy for synthesizing stable controllers because it models and manages systems based on their g lobal state space e quations. I t also permits proving the system's stability by m aking it passive, which is an undeniable advantage over traditional controllers that have limitations in terms of applications, saturation, and the inability to verify stability. Previous research used nonlinear control based on passivity method to control the energy distribution between the embedded sources, they did not investigate the impact of the dumping matrix parameter, which was typically considered to be a positive random value. In contrast, the results of this investigation show that it significantly affects system behaviour, especially in the transient-state domain. The primary objective of this study is to develop and evaluate an energy management strategy for hybrid electric vehicles powered by Fuel Cells as the primary source and Supercapacitors as the secondary source. The fast response with less overshoot is ensured by optimizing the damping matrix parameter using a well-known optimization technique named Novel Equilibrium Optimizer. The proposed technique is successfully demonstrated in a Matlab® simulation environment, and the control system exhibits robust dynamic behaviour. Finally, despite the complexity of this technique's mathematical proof, it provides a simple, efficient feedback control law for a real electrical vehicle application. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) have gradually become an important member of new energy vehicles because of the advantages of both electric and hybrid electric vehicles. A fast and effective energy management strategy can significantly improve the fuel-saving performance of vehicles. By observing the dynamic programming (DP) simulation results, it was found that the vehicle is in the charge-depleting mode, the state of charge (SOC) drops to the minimum at the end of the journey, and the SOC decreases linearly with the mileage. As such, this study proposed an improved rule-based (IRB) strategy enlightened by the DP strategy, which is different from previous rule-based (RB) strategies. Introducing the reference SOC curve and SOC adaptive adjustment, the IRB strategy ensures that the SOC decreases linearly with the driving distance, and the SOC drops to the minimum at the end of the journal, similar to the result of the DP strategy. The fuel economy of PHEV in the RB and DP energy management strategies can be considered as their worst-case and best-case scenarios, respectively. The simulation results show that the fuel consumption of the IRB strategy under the China Light-duty Vehicle Test Cycle is 3.16 L/100 km, which is 7.87% less than that of the RB strategy (3.43 L/100 km), and has reached 44.41% of the fuel-saving effect of the DP strategy (2.84 L/100 km). [ABSTRACT FROM AUTHOR]