An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system

Nowadays, the scheme of a stand-alone microgrid utilizing renewable energy is regarded as an effective approach to guarantee the power supply of an off-grid system. However, the intermittent nature of renewables brings new challenges to the determination of the optimal operation point for a hybrid energy system (HES). To address this issue, this paper proposes a subsection bi-objective optimization dynamic programming strategy for the HES consisting of photovoltaic, fuel cell, electrolyzer, hydrogen storage system, and battery bank. Within the proposed strategy, reasonable rule-based judgment is introduced to reduce the complexity of system control. Moreover, dynamic programming is selected to obtain the global optimal power distribution scheme. Meanwhile, a multi-objective genetic algorithm strategy is designed for comparative analysis. The results in two typical cases indicate the proposed strategy can improve photovoltaic utilization by 0.95% and 0.0003%, and fuel economy by nearly 50%.     

Optimized rule-based energy management for a polymer electrolyte membrane fuel cell/battery hybrid power system using a genetic algorithm

The polymer electrolyte membrane fuel cell (PEMFC) coupled with the battery is a promising hybrid power system for future energy supply application. Fuel cell durability, battery charge sustenance, and fuel consumption strongly rely on the energy management strategy (EMS). This paper puts forward an optimized rule-based EMS using genetic algorithm (GA) to optimally allocate the power between the fuel cell and the battery system. Control variables in real-time rule-based EMS are optimally adjusted with single objective of battery charge sustenance considering the fuel cell durability and efficiency. The proposed optimized rule-based EMS is simulated and experimentally verified via MATLAB/Simulink and LabVIEW-based experimental rig, respectively. The conventional rule-based EMS, fuzzy logic EMS, and dynamic programming (DP) EMS are also examined for comparison. The comparison results elucidate that the optimized rule-based EMS realizes a large performance improvement over the conventional rule-based and fuzzy logic EMSs. Near optimal performance is verified compared with DP EMS in terms of fuel economy, battery charge sustenance, fuel cell efficiency, and system durability. The combination of rule-based EMS and GA optimization algorithm has the advantage of having expert experience and global optimization properties, realizing optimal power allocation in real-time application with lower computation burden, which could be applied easily to other EMS system without loss of validity.     

A comparison of real-time energy management strategies of FC/SC hybrid power source: Statistical analysis using random cycles

This paper presents a comparative study of two promising real-time energy management strategies for fuel cell electric vehicle applications: adaptive equivalent consumption minimization strategy (A-ECMS), and stochastic dynamic programming (SDP). An off-line algorithm –classic dynamic programming - provides reference results. On-line and off-line strategies are tested both in simulation and using a dedicated test bench completely consistent with an electric scooter powertrain.The hybrid power source combines a fuel cell, a supercapacitor pack and two related power converters. The system model is carefully calibrated using experimental data. This allows meaningful identification of parameters of the various strategies. The model data is determined using a motorcycle certification driving cycle.The robustness of each strategy is then analyzed using a large number of random driving cycles. Experimental and simulation results show that a specific SDP approach, based on Markov chain modeling, has the best overall performance in real-world driving conditions. It achieves the minimum average hydrogen consumption while respecting the state-sustaining constraint. Conversely, the A-ECMS results lack robustness and show poor performance indexes when facing unknown real world power demand profile. In conclusion, the present results indicate SDP is an interesting approach for future hybrid source energy allocation.     

Development of new energy management strategy for a household fuel cell/battery hybrid system

This work aims to construct an efficient and robust fuel cell/battery hybrid operating system for a household application. The ability to dispatch the power demands, sustain the state of charge (SOC) of battery, optimize the power consumption, and more importantly, ensure the durability as well as extend the lifetime of a fuel cell system is the basic requirements of the hybrid operating system. New power management strategy based on fuzzy logical combined state machine control is developed, and its effectiveness is compared with various strategies such as dynamic programming (DP), state machine control, and fuzzy logical control with simulation. Experimental results are also presented, except for DP because of difficulties in achieving real‐time implementation and much faster response to load variation. The given current from the energy management system (EMS) as a reference of the fuel cell output current is determined by filtering out various harmful signals. The new power management strategy is applied to a 1‐kW stationary fuel cell/battery hybrid system. Results show that the fuel cell hybrid system can run much smoothly with prolonged lifetime.     

Cost-optimal design and energy management of fuel cell electric trucks

Road freight transport on hilly routes represents a significant challenge for the advancement of fuel cell electric trucks because of the high-performance requirements for fuel consumption, vehicle lifetime, and battery charge control. Therefore, it is essential to optimize the vehicle design and energy management, which greatly influence the driving performance and total cost of ownership. This paper focuses on the cost-optimal design and energy management of fuel cell electric trucks, considering five key influencing factors: powertrain component sizing, driving cycle, vehicle weight, component degradation, and market prices. The cost optimization relies on a novel predictive energy management scheme based on dynamic programming and the systematic calibration of control parameters. The paper analyzes the simulation results to highlight three main findings for fuel cell electric trucks: 1) cost-optimal energy management is essential to define the best trade-off between fuel consumption and component degradation; 2) the total cost of ownership is significantly influenced by component sizing, driving cycles, vehicle weight, and market prices; 3) predictive energy management is highly beneficial in challenging road topographies for substantial cost-saving and lower component size requirements.     

Real-time optimization strategy by using sequence quadratic programming with multivariate nonlinear regression for a fuel cell electric vehicle

The energy management of a plug-in FCEV (Fuel Cell Electric Vehicle) strictly depends on the control of SOC (State of Charge) over a given trip distance. The SOC may be varied with the trip distance by updating an EF (Equivalent Factor), which is derived from ECMS (Equivalent Consumption Minimization Strategy). However, the EF is too complicated to estimate accurately in real-time with traditional method. A real-time optimization strategy by using SQP (Sequence Quadratic Programming) with MNLR (Multivariate Nonlinear Regression) is proposed for a plug-in FCEV. First, the real-time hydrogen consumption optimization problem for SOC trip distance adaptive is formulated by using ECMS. The EF is adjusted according to the trip distances and predefined SOC. Then, in order to improve the accuracy of EF, SQP method is utilized to optimize the fuel cell and battery efficiency. Thus, the MNLR is applied to construct the fuel cell and battery efficiency response surface models for real-time optimization application. Finally, numerical verification and hardware in loop experiments are conducted to validate the proposed strategy. The results indicate that the combination of SQP with MNLR made it possible to develop the proposed strategy capable of significantly improving the hydrogen economic performance of this FCEV.     

Optimal power allocation for a FCHV based on linear programming and PID controller

Hybrid electric vehicles positively influence the transportation industry with regards to reducing the use of fossil fuels and minimizing polluting emissions. A class of such vehicles incorporates fuel cells and energy storage systems as alternatives to internal combustion engines. This paper develops a dynamically efficient energy management system for fuel cell hybrid vehicles for the purpose of achieving an optimal power allocation between the energy sources while adhering to component requirements and maintaining the essential operational performance. The paper addresses a two stage control methodologies, pre-driving optimization using linear programming algorithms and on-line optimization using PID controllers and component mechanisms. The performance criteria are based on the overall operational cost as well as the hydrogen consumption per trip. Comparison against a state control algorithm shows improvements in hydrogen consumption.     

Parameter sizing and stability analysis of a highway fuel cell electric bus power system using a multi-objective optimization approach

Although FC based electric buses are currently popular on urban streets or in short transit routes within large facilities, the version that is designed to operate on a highway, which has much higher dynamic requirements, is yet to be well developed. This research proposes to adopt the NSGA-II based multi-objective optimization scheme to optimize a fuel cell-battery-supercapacitor (SC) based FC power system (FCPS) that is specifically for a FC electric bus operating on the highway fuel economy cycle (HWFET). The optimization objectives are to minimize the FC's fuel consumption, the required battery and SC size and the battery degradation rate. More importantly, the optimization scheme is based on a combined energy management strategy (EMS) software parameter and hardware component sizing approach which is important for guaranteeing dynamically stable responses. This characteristic is achieved by imposing constraints that limit the transient time responses the DC-Bus capacitor voltage electrical parameters upon a generic step change in load power. Results demonstrate that dynamic stability can be guaranteed with proper software parameter and hardware components combinations without any trade-off requirements with the optimizer objectives. Moreover, the system mass and the battery degradation objectives are in trade-off but don't have any dependence to hydrogen consumption.     

基于价值损耗的燃料电池混合动力能量管理策略评价

为了评价燃料电池混合动力系统能量管理策略的经济性,对基于状态机和模糊逻辑2种能量管理策略的燃料电池混合动力叉车的价值损耗进行分析。首先,通过分析燃料电池和锂电池的工作特性,分别构建依赖实际工况的燃料电池单体电压衰减率模型和锂电池容量衰减率模型;同时定义计及燃料电池氢耗量的燃料电池混合动力系统的综合价值损耗指标。其次,通过测试叉车极限工况,计算燃料电池功率和锂电池容量,并根据母线电压确定锂电池SOC范围。最后,设计基于状态机和模糊逻辑的2种燃料电池混合动力叉车能量管理策略,并通过仿真分析在叉车一次循环工况下2种能量管理的价值损耗。研究结果表明:相较于模糊逻辑策略,采用状态机策略造成燃料电池寿命损耗提高7.81%,氢耗量提高1.89倍,锂电池寿命损耗减小21.33%。     

Real time optimal energy management strategy targeting at minimizing daily operation cost for a plug-in fuel cell city bus

This paper proposes an optimal real-time energy management strategy targeting at daily operation optimization for a plug in proton exchange membrane fuel cell electric vehicle (PFCEV) for public transportations. A novel real-time optimal energy management strategy based on the determined dynamic programming (DDP) strategy is proposed, namely the DBSD (charge Depleting – Blended – Sustaining – Depleting) strategy. A simulation model is set up to compare the DDP strategy, the DBSD strategy and the CDCS (Charge Depleting and Charge Sustaining) strategies. Compared to the CDCS strategy, the daily operating cost can be reduced by 6.4% with the DBSD strategy, and it can be reduced by 9.5% with the DDP strategy. On-road testing with the DBSD strategy shows that, the daily operation cost is 510.2 Sig. $ (100 km)⁻¹. The electric energy consumption in pure battery driven mode is about 1.68 kWh km⁻¹, and the equivalent hydrogen consumption in hybrid driven mode is about 0.14 kg km⁻¹.     

Experimental investigation on the online fuzzy energy management of hybrid fuel cell/battery power system for UAVs

The hybrid powerplant combining a fuel cell and a battery has become one of the most promising alternative power systems for electric unmanned aerial vehicles (UAVs). To enhance the fuel efficiency and battery service life, highly effective and robust online energy management strategies are needed in real applications.In this work, an energy management system is designed to control the hybrid fuel cell and battery power system for electric UAVs. To reduce the weight, only one programmable direct-current to direct-current (dcdc) converter is used as the critical power split component to implement the power management strategy. The output voltage and current of the dcdc is controlled by an independent energy management controller. An executable process of online fuzzy energy management strategy is proposed and established. According to the demand power and battery state of charge, the online fuzzy energy management strategy produces the current command for the dcdc to directly control the output current of the fuel cell and to indirectly control the charge/discharge current of the battery based on the power balance principle.Another two online strategies, the passive control strategy and the state machine strategy, are also employed to compare with the proposed online fuzzy strategy in terms of the battery management and fuel efficiency. To evaluate and compare the feasibility of the online energy management strategies in application, experiments with three types of missions are carried out using the hybrid power system test-bench, which consists of a commercial fuel cell EOS600, a Lipo battery, a programmable dcdc converter, an energy management controller, and an electric load. The experimental investigation shows that the proposed online fuzzy strategy prefers to use the most power from the battery and consumes the least amount of hydrogen fuel compared with the other two online energy management strategies.     

Comparison among various energy management strategies for reducing hydrogen consumption in a hybrid fuel cell/supercapacitor/battery system

The aim of this study is to introduce a comprehensive comparison of various energy management strategies of fuel cell/supercapacitor/battery storage systems. These strategies are utilized to manage the energy demand response of hybrid systems, in an optimal way, under highly fluctuating load condition. Two novel strategies based on salp swarm algorithm (SSA) and mine-blast optimization are proposed. The outcomes of these strategies are compared with commonly used strategies like fuzzy logic control, classical proportional integral control, the state machine, equivalent fuel consumption minimization, maximization, external energy maximization, and equivalent consumption minimization. Hydrogen fuel economy and overall efficiency are used for the comparison of these different strategies. Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency. The minimum consumed hydrogen and maximum efficiency are found 19.4 gm and 85.61%, respectively.     

A prediction-based optimization strategy to balance the use of diesel generator and emergency battery in the microgrid

This paper presents a prediction-based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by combing two operating strategies, the “predictive analysis” and “optimal operation” in each scheduling period for the microgrid. Based on the predicted microgrid state and energy demand, a multi-objective mixed-integer nonlinear programming model (MOMINP) is constructed to minimize the fuel consumption and the regularization of battery charge/discharge subject to the practical constraints in the microgrid. This paper proposes a detailed scheme to deal with the multiple objectives and nonlinear constraints in the MOMINP, then the MOMINP is successfully converted into a mixed-integer linear programming model (MILP). And an adjustment strategy is designed to obtain the near-optimal solution of the MOMINP based on the optimal solution of the MILP solved by using the CPLEX Optimizer. Experimental results show that in a basic scheduling period, the working time of DG in the POS-softmax regression strategy is shorter than the current operation, and the fuel consumption reduction ratio is about 15.3% with the same battery SoC value at the end of the scheduling. At the same time, the fuel consumption in the POS-accurate prediction strategy can be reduced by up to 54.9% compared with the POS-softmax regression strategy and can be reduced by 61.8% compared to the current operation. Based on the comparative analysis of the actual case data of a micro-grid in 6 months, it can be seen that on average the POS works better than the current operation, with an approximately 23.6% decrease in the objective function and an additional 16.2% decrease with an accurate prediction.     

An experimental and analytical comparison study of power management methodologies of fuel cell-battery hybrid vehicles

The implementation of fuel cell vehicles requires a supervisory control strategy that manages the power distribution between the fuel cell and the energy storage device. Some of the current problems with power management strategies are: fuel efficiency optimization methods require prior knowledge of the driving cycle before they can be implemented, the impact on the fuel cell and battery life cycle are not considered and finally, there are no standardized measures to evaluate the performance of different control methods. In addition to that, the performances of different control methods for power management have not been directly compared using the same mathematical models. The proposed work will present a different optimization approach that uses fuel mass flow rate instead of fuel mass consumption as the cost function and thus, it can be done instantaneously and does not require knowledge of the driving cycle ahead of time. Also this study presents an experimental approach to validate the mathematical simulation results.     

A control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid unmanned aerial vehicles

Fuel cell/battery hybrid energy storage system (HESS) powered unmanned aerial vehicle (UAV) has the outstanding advantage of long endurance time. Trajectory tracking motion is a commonly used task execution mode of UAVs, especially in autonomous UAVs. This study aims at developing a control architecture to coordinate energy management with trajectory tracking control for fuel cell/battery hybrid UAVs. Its position tracking control adopts model predictive control (MPC) and an extended state observer to eliminate the modeling errors and effect of interference. The attitude tracking control adopts an auto-disturbance rejection controller having a quick response. The obtained control parameters are given as an input to the energy management block. Energy management strategies (EMSs) based on online dynamic programming and hierarchical MPC have been proposed. The results obtained from a simulation show that the proposed trajectory tracking control architecture can track the target trajectory stably with a small tracking error. The tracking performance is stable under interference. Experimental results show that dynamic programming is solved online with good control performance. Compared to ordinary EMSs, dynamic programming and hierarchical MPC can increase endurance time by 2.69% and 1.27%, respectively. The proposed control architecture verifies the coordination of energy management and trajectory tracking control, and prospected the advantages of the combination of fuel cell and autonomous driving for long endurance UAVs in the future.     

Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship

An improved fuzzy-based energy management strategy (EMS) is proposed for a tourist ship used hybrid power system with multiple power sources consisting of fuel cell(FC)/photovoltaic cell(PV)/battery(BAT)/super-capacitor(SC). The power demand from propeller and user terminal is afforded by the power sources connecting to power converters. To obtain more superior performance of the power system, the maximum power point tracking (MPPT) algorithm is employed to optimize the PV. Meanwhile, the improved fuzzy logic control based on dynamic programming (DP) associated with wavelet analysis and PI control are employed to achieve the output power optimal distribution and online control. In particular, the MPPT algorithm can improve the utilization of solar energy, and the SC can well absorb the high frequency power and reduce the fluctuation of the battery and FC that exhibits the potential of their lifetime extension. The FC outputs the high and stable power satisfying the ship's power demand even under the extreme work conditions. The developed model is able to illustrate well in the operation process of the hybrid power system governed by the proposed EMS. In addition, compared with the rule-based strategy, the improved fuzzy-based EMS can reduce 14.39% hydrogen consumption and keep the consistency of battery SOC.     

Economic energy management strategy design and simulation for a dual-stack fuel cell electric vehicle

This paper presents the design and simulation validation of two energy management strategies for dual-stack fuel cell electric vehicles. With growing concerns about environmental issues and the fossil energy crisis, finding alternative methods for vehicle propulsion is necessary. Proton exchange membrane (PEM) fuel cell systems are now considered to be one of the most promising alternative energy sources. In this work, the challenge of further improving the fuel economy and extending the driving range of a fuel cell vehicle is addressed by a dual-stack fuel cell system with specific energy management strategies. An efficiency optimization strategy and an instantaneous optimization strategy are proposed. Simulation validation for each strategy is conducted based on a dual-stack fuel cell electric vehicle model which follows the new European driving cycle (NEDC). Simulation results show that a dual-stack fuel cell system with proposed energy management strategies can significantly improve the fuel economy of a fuel cell vehicle and thus lengthen the driving range while being able to keep the start-stop frequency of the fuel cell stack within a reasonable range.     

Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.     

A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.     

纯电动船复合储能系统的设计与优化

针对纯电动船中的动力电池组易受瞬态大电流的冲击、使用寿命短等问题,提出以磷酸铁锂电池和超级电容为核心的复合储能系统方案,进行理论分析,并采用DC/DC变换器以更好地发挥超级电容性能。采用带精英策略的非支配排序遗传算法进行多目标优化选型,结合能量管理策略,实现储能系统的优化配置。仿真结果表明,得到的选型方案结合以模糊控制为核心的能量管理策略能够很好地应对波动性负载。