Existing Energy Management Strategies Research Articles

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

An enhanced energy management system for coordinated energy storage and exchange in grid-connected photovoltaic-based community microgrids

This paper introduces an enhanced coordinated community energy management system (CEMS) for a community microgrid. It is designed to optimize residential energy consumption and facilitate energy sharing among smart homes. The proposed solution integrates constrained mixed-integer programming (CMIP) approach along with advanced optimization technique and cooperative game theory-based pricing mechanism to address critical challenges in existing energy management strategies, including load scheduling and efficient peer-to-peer energy trading. The CEMS approach is adopted for grid-connected community power systems that incorporate local energy sources such as photovoltaic and battery storage systems. This solution is implemented under a time-of-use dynamic pricing model for energy transfer from the main grid to the community peers, a modified mid-market pricing mechanism for the P2P energy exchange, and feed-in tariff for energy transfer from the community peers to the main grid. Besides, seagull optimization algorithm is applied to relax the CMIP problem to get the optimal rational solution of the scheduling problem which is subjected to a rounding process to obtain the best feasible solution. The obtained results indicate a substantial reduction in electricity costs, achieving daily savings of 72.21 % and reducing grid dependency by 41.95 %. Moreover, 82.2 % of the community's energy demand was met through locally generated resources with 18.39 % enhancement of the overall CMG self-consumption ratio comparing to the reference operating scenario. Ultimately, the study demonstrates the effectiveness of the management system in improving load profiles, maximizing the use of local renewable energy sources, and reducing electricity expenses, thereby providing a sustainable and economically viable model for future smart grids.

An energy management strategy with considering ultracapacitor ideal state of charge for fuel cell/battery/ultracapacitor vehicle

The hybridization of energy storage systems consisting of fuel cells, batteries, and ultracapacitors has tremendous potential in fuel cell hybrid electric vehicles. However, the utilization of ultracapacitors in existing energy management strategies is insufficient, preventing complete exploitation of the energy storage system's benefits. To strengthen the utilization of ultracapacitors, this paper proposes an energy management strategy for fuel cell vehicles with three energy sources, considering the ideal state of charge (SOC) of ultracapacitors. The proposed method disassembles the motor power demand, with the ultracapacitor primarily supplying the acceleration portion. It calculates the ideal SOC of the ultracapacitor, and controlling the actual SOC of the ultracapacitor to match the ideal SOC. Furthermore, a fuzzy logic controller is designed to modify the fuel cell's output power, reducing power fluctuation. Additionally, according to the variation of the SOC of the battery, the fuel cell output power is modified to enhance the overall performance of the composite energy storage system. Simulation and hardware-in-loop results demonstrate that, compared to the adaptive filtering strategy with fuel cell power constraints and dynamic programming strategy, the strategy proposed in this paper reduces operational costs by 14.56 % and 4.99 %, respectively.

A proactive energy management strategy for battery-powered autonomous systems

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies.

Parallel-Reinforcement-Learning-Based Online Energy Management Strategy for Energy Storage Traction Substations in Electrified Railroad

The traction power supply system is the only source of power for electric locomotives. The huge power fluctuations and complex operating conditions of the traction power supply system pose a challenge to the efficient operation of energy storage traction substations. The existing energy management strategies are difficult to achieve accurate charging and discharging, difficult to modify the control rules in real-time, and have poor migration capability. For comparison, the reinforcement learning algorithms can address the shortcomings of rule-based energy management strategies due to their model-free feature. Therefore, this paper proposes an energy management strategy based on parallel reinforcement learning to improve the efficiency of energy utilization while speeding up the convergence of the algorithm. More specifically, a Markov decision framework is established for capturing the energy management process. The Monte Carlo sampling process is also improved to achieve offline optimization by parallel reinforcement learning algorithms and reduce the impact of low-value power fragments on iteration speed. Meanwhile, the algorithm is modified to enable online updates. The case study shows that compared with other energy management strategies, the parallel reinforcement learning-based energy management strategy has faster convergence speed, higher energy exchange efficiency, better migration capability, and can adapt to various complex working conditions.

Driving-Behavior-Aware Optimal Energy Management Strategy for Multi-Source Fuel Cell Hybrid Electric Vehicles Based on Adaptive Soft Deep-Reinforcement Learning

The majority of existing energy management strategies (EMSs), merely considering external driving conditions, often allocate demand power in an irrational way, resulting in a waste of energy and a short service life of power sources. Therefore, it is necessary to integrate driving behavior in EMS to reduce the fuel consumption and improve the lifespan of power sources. In this paper, a driving-behavior-aware adaptive deep-reinforcement-learning (DRL) based EMS is proposed for a three-power-source fuel cell hybrid electric vehicle (FCHEV). To fully utilize each power source, a hierarchical power splitting method is adopted by an adaptive fuzzy filter. Then, a high-performance driving behavior recognizer is employed, and Pontryagin’s minimum principle (PMP) method is used to compute the optimal equivalent factor (EF) of each driving behavior. To realize a trade-off between global learning and real-time implementation, an improved multi-learning-space DRL-based algorithm, applying driving-behavior-aware adaptive equivalent consumption minimization strategy (A-ECMS) and soft learning mechanism, is proposed and verified by a series of simulations. Simulation results show that, compared with the benchmark method ECMS, the proposed P-DQL method can reduce the hydrogen consumption by 49.9% on average, and the total cost to use by 31.4%, showing a promising ability to increase fuel economy and reduce hydrogen consumption and the total cost to use of FCHEV.

Real-Time Model Predictive Control for Battery-Supercapacitor Hybrid Energy Storage Systems Using Linear Parameter-Varying Models

This article proposes a new model predictive control (MPC) strategy for the energy management of a battery-supercapacitor (SC) hybrid energy storage system (HESS) for electric vehicle (EV) applications. First, linear parameter-varying (LPV) models of the HESS are developed, which account for battery parameter variations along its state of charge (SOC). Compared with the conventional linear time invariant (LTI) models reported in the literature, the LPV models of the HESS offer higher modeling and prediction accuracy. Based on the LPV prediction model, a real-time LPV-MPC strategy is then proposed to optimally allocate the load current of the HESS between battery and SC to achieve three goals: minimizing the power loss of the HESS, mitigating the battery degradation, and regulating the SOC of the SC. The optimization problem of the LPV-MPC strategy is transformed into a quadratic programming problem, which can be efficiently solved in real time. Simulation studies verify the superiority of the proposed LPV-MPC strategy over the existing energy management strategies in reducing the energy losses and battery degradation of the HESS. Finally, a real-time experiment is carried out to further validate the proposed LPV-MPC strategy for EV applications.

Energy management strategies for hybrid ships and Unmanned Underwater Vehicles

Both ships and unmanned underwater vehicles (UUVs) have played a great role in the human exploration of the ocean and the investigation of marine resources. Their endurance of them is limited by the onboard energy storage, and the benefits of hybrid ships and UUVs in terms of energy conservation and emission reduction technologies are increasingly clear. The secret to attaining energy conservation while maintaining power performance is energy management. The management of energy for hybrid ships and UUVs are summarized in this work. First, this article includes the state of the art in energy management techniques for autonomous maritime vehicles and hybrid ships. and summarizes the hybrid energy management objectives for different purposes. Second, it analyses the latest progress of hybrid energy management strategies at the present stage and compares the benefits and drawbacks of existing energy management strategies in a comprehensive and in-depth manner. Finally, the paper summarizes their shortcomings and provides an outlook on the future development of hybrid energy management strategies.

Real-time energy management strategy for a plug-in hybrid electric bus considering the battery degradation

The energy conservation remains the key issue for the hybrid electric vehicles (HEVs) today. However, most existing energy management strategies (EMS) only focus on the fuel consumption or battery preservation, and little considers the battery health. Besides, the global energy optimality and real-time execution are two trade-off counterparts for the EMS application. To this end, this paper proposed a real time EMS of the HEVs considering the battery health. Explicitly, the battery health status and SOC values are predicted with the space vector machine algorithm and adaptive Kalman filter algorithm, respectively. Then, the offline energy optimization is realized with the Pontryagin's minimum principle; thereby, the offline energy conversion coefficient can be further extracted into the simple rules. On this basis, the online equivalent consumption minimization strategy (ECMS) is adopted to output the optimal control variables including the motor torque, engine torque, clutch states and gear information. Besides, to apply the proposed EMS in the practical vehicles, the energy conversion coefficients are further modified according to the estimated battery state of the charge (SOC). The simulation and experimental tests have validated the superiority of the proposed EMS in terms of energy economy and maneuverability. Explicitly, the proposed EMS considering the battery degradation has effectively prevented the SOC trajectory into charge-sustaining stage earlier and meanwhile the fuel utilization is improved by 4.1 % than that without considering the battery degradation. Besides, compared with the existing ECMS method, the proposed EMS can reduce the fuel consumption by about 8–14 % and meanwhile enhance the handling adaptiveness by about 15–20 %. In general, the proposed EMS is of significance to the vehicular energy conservation and the real-time executability for the HEVs.

Review on Energy Management Strategies of PHEV

The problems of air pollution and the shortage of energy sources are increasingly significant, and the traditional automobile industry plays a vital role in it. Consequently, Energy management strategy can adjust the energy allocation between the engine and the motor, achieving the purpose of improving fuel economy. Meanwhile, it can optimize the battery balance so that the comprehensive cost of the hybrid vehicle can be reduced, apart from the limited emissions and conserved energy. This paper first introduces the machinery configuration of the parallel hybrid electric vehicles, and then classifies their working patterns under different working conditions. After analysing the existing energy management strategies, we introduce and analyse the rule-based ones by two frequently used methods: logic threshold method and fuzzy logic control. Additionally, strategies based on optimization, including instantaneous and global optimization strategy, are also introduced and analysed, with each advantages, limitations and potential innovations. Finally, the contradiction between practicability and control effect is identified, which is one of the crucial problems to be resolved.

An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario

Nowadays, the comprehensive performance of plug-in hybrid electric vehicle (PHEV) is expected to be further improved with development of connected vehicle technology. However, the strong coupling and traffic flow uncertainty characteristics of connected scenario pose formidable challenge to existing energy management strategies (EMSs) in terms of optimization effect and computational efficiency. For comprehensively improving connected PHEV performances including energy saving, safety, traffic efficiency and computational efficiency, a multi-objective hierarchical EMS with less computational burden is proposed by incorporating resistance network (RN) triggered motion planning and alternating direction method of multipliers (ADMM) based convex torque optimization. Specifically, the RN method is employed to characterize and decouple the complex interaction relationship within connected scenario from internal mechanism perspective, enabling the velocity profile optimization issue that with fixed end time constraint and online correction mechanism for traffic flow uncertainty. According to the velocity profile optimized in cloud level, the convex formulation of model predictive control (MPC) based torque distribution problem is formulated in vehicle level, and an efficient ADMM algorithm is used for its solution, with the aim of satisfying energy saving and practical application requirements simultaneously. Based on real connected information, the superiorities of proposed EMS are verified by both simulation and hardware-in-loop (HIL) experiment.

Practical Application-Oriented Energy Management for a Plug-In Hybrid Electric Bus Using a Dynamic SOC Design Zone Plan Method

The main problem in current energy management is the ability of practical application. To address the problem, this paper proposes a reinforcement learning (RL)-based energy management by combining Tubule Q-learning and Pontryagin’s Minimum Principle (PMP) algorithms for a plug-in hybrid electric bus (PHEB). The main innovation distinguished from the existing energy management strategies is that a dynamic SOC design zone plan method is proposed. It is characterized by two aspects: ① a series of fixed locations are defined in the city bus route and a linear SOC reference trajectory is re-planned at fixed locations; ② a triangle zone will be re-planned based on the linear SOC reference trajectory. Additionally, a one-dimensional state space is also designed to ensure the real-time control. The off-line trainings demonstrate that the agent of the RL-based energy management can be well trained and has good generalization performance. The results of hardware in loop simulation (HIL) demonstrate that the trained energy management has good real-time performance, and its fuel consumption can be decreased by 12.92%, compared to a rule-based control strategy.

Optimized EMS and a Comparative Study of Hybrid Hydrogen Fuel Cell/Battery Vehicles

This paper presents a new Fuel Cell Fuel Consumption Minimization Strategy (FCFCMS) for Hybrid Electric Vehicles (HEVs) powered by a fuel cell and an energy storage system, in order to minimize as much as possible the consumption of hydrogen while maintaining the State Of Charge (SOC) of the battery. Compared to existing Energy Management Strategies (EMSs) (such as the well-known State Machine Strategy (SMC), Fuzzy Logic Control (FLC), Frequency Decoupling and FLC (FDFLC), and the Equivalent Consumption Minimization Strategy (ECMS)), the proposed strategy increases the overall vehicle energy efficiency and, therefore, minimizes the total hydrogen consumption while respecting the constraints of each energy and power element. A model of a hybrid vehicle has been built using the TruckMaker/MATLAB software. Using the Urban Dynamometer Driving Schedule (UDDS), which includes several stops and accelerations, the performance of the proposed strategy has been compared with these different approaches (SMC, FLC, FDFLC, and ECMS) through several simulations.

Effects of temperature on the performance of fuel cell hybrid electric vehicles: A review

Fuel cell hybrid electric vehicles powered by fuel cell and lithium-ion battery have been considered as an attractive and potential candidate in place of internal combustion vehicles with the advantage of high energy density and energy conversion efficiency, and have become a configuration of interest of many researchers in recent years. Energy management and thermal management are critical issues for steady output power of fuel cell hybrid power system. However, most of the research on energy management and temperature control of fuel cell hybrid electric vehicles is mutually independent. From the literatures, it is observed that existing energy management strategies without integration of fuel cell or battery’s temperature dimension are more or less incapable to perform very well. This paper focuses on reviewing the temperature effects on energy management strategies for fuel cell hybrid electric vehicles. To do this, temperature effects on the performance of lithium-ion battery and proton exchange membrane fuel cell are summarized respectively at first. Then, energy management in combination with thermal management of hybrid electric vehicles and fuel cell hybrid electric vehicles are analyzed and summarized in detail. It is indicated that the solution of power distribution considering temperature effects is a significant way to improve the efficiency and service life of fuel cell hybrid electric vehicles compared to the energy management strategy without considering temperature effects. Based on the above analysis, future suggestions of EMS designing are prospected in order to achieve better performance for fuel cell hybrid electric vehicles.

Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles

In this era of vehicle electrification from mild hybrids to fully electric cars, the importance of fuel economy improvements has led to technological advancements in energy management strategies. The control algorithm is pivotal to the increase in the energy efficiency of a plug-in hybrid system. The existing energy management strategies lack the adaptiveness and utilization of advancements of vehicle-to-vehicle technology. This paper proposes a Cost Optimization for Finite Horizon strategy and also an adaptive version of Equivalent Consumption Minimization Strategy (A-ECMS). The Adaptive-ECMS adds a battery State of Charge (SOC) based reference to ensure the most efficient blended operation for a charge-discharge cycle. The Cost Optimization for Finite Horizon strategy utilizes future driving condition information from vehicle-to-vehicle technology to assist fuel consumption. A forward-looking vehicle propulsion system simulator is developed in Simulink®. A battery model is developed with parameters from the Nickel Cobalt Aluminum chemistry cell. To understand the extent of improvement, the proposed strategies are then compared with the prevalent Finite State Machine strategy (FSM) in three representative driving cycles. The results show an average fuel economy improvement of 5% when compared to the baseline strategy. Among the three strategies, Cost Optimization for Finite Horizon strategy is best suitable for urban driving conditions and Adaptive-ECMS is best suitable for highway driving conditions. For a conventional series-hybrid vehicle, implementing the proposed energy management strategies can help save approximately 8.5 gallons of fuel per year.

Dynamic Energy Management Strategy of Hybrid Electric Vehicles based on Velocity Prediction

Abstract Energy management strategy is used to realize dynamic mode switching of hybrid electric vehicles, which can achieve good energy saving effect. However the vehicle future speed, road traffic flow information and other factors are unknown, excellent energy saving effect can’t be realised by the existing energy management strategy. Owe to the development of V2X, more information will be provided, a dynamic energy management strategy of hybrid electric vehicle (HEV) combining velocity prediction with dynamic optimization is proposed to deal with the complex and changeable problems of vehicle running process. The speed prediction layer, threshold optimization layer and strategy execution layer are included in this strategy. The LSTM is used to predict the vehicle speed based on V2X information in the speed prediction layer, so as to predict the future short-term torque demand of the vehicle. Based on the predicted vehicle speed curve, the LS-PSO algorithm is used in the threshold optimization layer to optimize the mode switching thresholds of the energy management strategy and the thresholds are transmited to the executive layer for vehicle energy allocation. After simulation verification, the results show that compared with the strategy of fixed rule, energy consumption of the dynamic energy management strategy is reduced. The average energy consumption in the ideal scenario can be reduced averagely by 22.1%.

All-Wheel-Drive Torque Distribution Strategy for Electric Vehicle Optimal Efficiency Considering Tire Slip

The existing energy management strategies for four-wheel-drive electric vehicles only take into account the vehicle energy consumption under static adhesion constraints. However, the front and rear axle loads transfer under dynamic conditions lead to the variations of vehicle adhesion characteristics, which results in the changes of vehicle energy consumptions. In this paper, a multi-objective optimal torque distribution strategy is proposed, taking into account the front and rear axle load transfer and the variations of adhesion characteristics. The advantages of the proposed strategy are verified through simulation studies in terms of vehicle energy consumption and wheel slip ratio, in comparison with the average torque distribution strategy and the optimal torque distribution strategy based on Sequential Quadratic Programming Algorithm. The simulation results show that the economy performance of the proposed strategy is superior to those of the competing methods. Furthermore, the proposed strategy provides good power performance and eliminates excessive wheel slip, which in turn ensures vehicle longitudinal stability and avoids energy loss resulting from frequent ASR interventions.

Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization

Plug-in hybrid electric buses (PHEBs) have the potential to satisfy both the fuel efficiency and the driving-mileage under complex urban traffic conditions. However, the optimal charge and discharge management is still a pivotal challenge of energy management for the inherent uncertainty in driving conditions. The common reference state-of-charge (SOC) profile based methods are limited by the adaptiveness which restricts the economic performance of on-line energy management systems. Promisingly, reinforcement learning based energy management strategies exhibited the significant self-learning ability. However, for PHEBs, the sparse rewards by the long-term SOC shortage make the strategies easily trick into the local optimal solution. The work presented in this paper concentrates on combining battery power reduction in the form of conditional entropy into reinforcement learning based energy management strategy. The proposed method named adaptive policy optimization (APO) introduces a novel advantage function to evaluate energy-saving performance considering long-term SOC dynamic, and a Bayesian neural network based SOC shortage probability estimator is utilized to optimize the energy management strategy parameterized by a deep neural network. Several experiments in a standard driving cycle demonstrate the optimality, self-learning ability and convergence of the APO. Moreover, the adaptability and robust performance get validated over the real bus trajectories data. With the comprehensive experiments in this paper, the proposed model exhibits enhanced fuel economy and more suitable SOC planning in comparison with the existing energy management strategies. The results indicate that APO respectively outperforms the compared online strategies by 9.8% and 2.6% and reaches 98% energy-saving rate of the offline global optimum.

A cascaded energy management optimization method of multimode power-split hybrid electric vehicles

A novel multimode power-split hybrid electric vehicle demonstrates significant advantages in energy conservation. The complicated structure and multiple operation modes, however, have brought significant challenges to energy management. The problems of operation mode selection, power allocation, and operating point selection need to be solved simultaneously. Traditionally, existing energy management strategies first have determined operation mode and then have solved power allocation and operating points. Moreover, traditional dynamic programming usually has not considered electric energy consumption and has deviated from the optimal solution. To solve these problems, a cascaded energy management strategy that integrates dynamic programming and equivalent consumption minimization strategy (DP + ECMS) is proposed. An iterative method is used to optimize the equivalence factor. A vehicle model based on actual control strategy is established and validated by test results. Then fuel economy simulations are conducted. Results showed that DP + ECMS could achieve 19.9% fuel economy improvement compared with the rule-based strategy for a new European driving cycle. In addition, simulation results of a Worldwide Harmonized Light-Duty Test Cycle demonstrated that DP + ECMS performed well in real road driving conditions. This study introduces a novel multimode power-split hybrid system and provides a global energy management optimization method.

Effect of Uncertainty in SOC Estimation on the Performance of Energy Management for HEVs

Existing energy management strategies of HEVs do not consider the inaccuracy of SOC estimation during optimal control formulation. In this paper, the importance and effect of considering this discrepancy in SOC values are analyzed and a mathematical relationship has been established. A sensitivity-based approach is adopted to analyze the problem. Finally, it is demonstrated through theoretical justifications and realistic simulation results that without incorporation of this discrepancy, not only does this lead to exceeding safe boundary conditions for battery but it also substantially affects fuel economy/energy consumption.