Hierarchical predictive energy management strategy for fuel cell buses entering bus stops scenario

This paper aims to answer how to use traffic information to design energy management strategies for fuel cell buses in a networked environment.For the buses entering the bus stops scenario,this paper proposes a hierarchical energy management strategy for fuel cell buses,which considers the traffic information near the bus stops.In the upper-level trajectory planning stage,the optimal SOC trajectory under various historical traffic conditions is solved through dynamic planning.The traffic information and the best SOC trajectory are mapped through BiLSTM,which can achieve fast,real-time long-term SOC reference.In the lower-level real-time predictive energy management strategy,the optimal SOC is used as the state reference to guide the predictive energy management of fuel cell buses when entering the bus stops.Simulation results show that compared with the strategy without SOC trajectory reference,the life cost of the proposed strategy is reduced by 13.8%,and the total cost is reduced by 3.61%.The SOC of the proposed strategy is closer to the DP optimal solution.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Half‑Power Prediction and Its Application on the Energy Management Strategy for Fuel Cell City Bus

The fuel cell hybrid powertrain is a potential power supply system for fuel cell vehicles.The underlying problem is that the fuel cell vehicles encounter exhaustive hydrogen consumption.To effectively manage hydrogen consumption,the aim is to propose fuel cell city bus power and control system.The underlying idea is to determine the target power of fuel cell through simulation study on fuel cell and battery energy management strategy and road test verifications.A half-power prediction energy management strategy is implemented to predict the target power of the fuel cell in the current time step based on the demand power of the vehicle and the state of charge(SOC)of the battery in the previous time steps.This offers better understanding of the correlation between fuel cell power and vehicle drive cycle for enabling effective power supply management.The research results show that the half-power prediction energy management strategy effectively reduces the hydrogen consumption of the vehicle by 7.1%and the number of battery cycle by 6.0%,compared to the stepped manage-ment strategy of battery SOC.When applied to a 12-m fuel cell city bus—F12,specially designed and manufactured for the Winter Olympic Games in 2022—the fuel economy of 3.7 kg/100 km is achieved in urban road conditions.This study lays a foundation for providing the powertrain configuration and energy management strategy of fuel cell city bus.

Function approximation reinforcement learning of energy management with the fuzzy REINFORCE for fuel cell hybrid electric vehicles

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

Deep reinforcement learning based energy management strategy for fuel cell/battery/supercapacitor powered electric vehicle

Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy.Therefore,the current automobile market is mainly dominated by fuel cell hybrid vehicles.In this study,the fuel cell hybrid commercial vehicle is taken as the research object,and a fuel cell/battery/supercapacitor energy topology is proposed,and an energy management strategy based on a double-delay deep deterministic policy gradient is designed for this topological structure.This strategy takes fuel cell hydrogen consumption,fuel cell life loss,and battery life loss as the optimization goals,in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery,providing more optimization ranges for the optimization of fuel cells and batteries.Compared with the deep deterministic policy gradient strategy(DDPG)and the nonlinear programming algorithm strategy,this strategy has reduced hydrogen consumption level,fuel cell loss level,and battery loss level,which greatly improves the economy and service life of the power system.The proposed EMS is based on the TD3 algorithm in deep reinforcement learning,and simultaneously optimizes a number of indicators,which is beneficial to prolong the service life of the power system.

A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

Optimal Energy Management for a Complex Hybrid Electric Vehicle:Tolerating Power-loss of Motor

The energy management may perform well under normal conditions, but may lead to poor behavior under abnormal situations. To tackle this problem, an optimal control strategy called rule-based equivalent fuel consumption minimization strategy (RECMS) is developed for a new complex hybrid electric vehicle (CHEV). It optimizes the energy effciency and drive performance to cater for normal and power-loss operations of the tractive motor. Firstly, the strategy formulates a novel objective function based on the equivalent fuel concept. By accounting for the actual fuel cost, the equivalent fuel cost for the electric machines and virtual fuel cost for the drivability, the cost function is obtained. Furthermore, some penalty factors are presented to optimize the performance target. Finally, experiments for a practical CHEV are performed to validate a simulation model. Then simulations are carried out for both rule-based and RECMS. The results show that the optimal energy management is working well.

A Novel Method for the Application of the ECMS(Equivalent Consumption Minimization Strategy)to Reduce Hydrogen Consumption in Fuel Cell Hybrid Electric Vehicles

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society.In this context,this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor(determined in the framework of the equivalent consumption minimum strategy-ECMS)on the working conditions.The simulation results show that under typical conditions(some representative cities being considered),the proposed strategy can maintain the power balance;for different initial battery’s states of charge(SOC),after the SOC stabilizes,the fuel consumption is 5.25 L/100 km.

融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略

为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.

基于优化功率跟随控制的E-REV能量管理策略研究

基于功率跟随控制的增程式电动汽车能量管理策略具有减缓电池寿命衰减与提高车辆NVH性能等优势,但存在阈值参数依赖性强、增程器启停频繁等问题,为此提出了一种基于优化功率跟随控制的E-REV能量管理策略。依据车速、SOC状态与驾驶员的加速踏板力度等信息特征,制定基于功率跟随控制的能量管理策略。在此基础上,针对固定规则参数的局限性,以车辆行驶总成本与SOC变化梯度为目标函数,结合灰狼优化算法对增程器启停功率阈值参数进行优化,减少发动机频繁启停现象。运用Matlab/Simulink搭建控制策略模型,并联合基于Simcenter/AMESIM搭建的整车物理模型进行仿真试验,结果表明:CHTC-LT循环工况下,优化功率跟随控制策略与功率跟随控制策略相比,SOC最大波动值降低了28%,增程器启停次数减少了28.5%,整车燃油经济性提升了6.89%。

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determined,the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources.The paper simulates the assistance of the intelligent transport system(ITS)and carries out the eco-velocity planning using the traffic signal light.On this basis,in order to further improve the energy efficiency of FCT,a model predictive control(MPC)-based energy source optimization management strategy is innovatively developed,which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption.Under the scenarios of signalized intersections,based on the planned eco-velocity,the off-line simulation results show that the proposed MPC-based energy source management strategy(ESMS)can reduce hydrogen consumption of fuel cell up to 7%compared with the existing rule-based ESMS.Finally,the Hardware-in-the-Loop(HiL)simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.

燃料电池汽车动力系统匹配及能量管理

为了分析燃料电池汽车动力系统参数匹配设计以及能量管理方案对整车性能的影响,以某款燃料电池汽车为研究对象,对其主要动力系统部件进行了参数匹配.以提升整车经济性能为目标,提出了功率跟随式能量管理策略和基于模糊控制的能量管理策略,并采用滑动平均滤波算法对模糊控制策略输出量进行优化.基于AVL-Cruise和Simulink建立了联合仿真平台,搭建整车及能量管理系统模型,验证了整车动力性能,对比分析了3种控制策略下的整车经济性能.结果表明:经过优化后的能量管理策略燃料电池输出功率曲线更加平滑,且一直处于高效率输出区间内,基于燃料电池优先保护原则,经过优化后的能量管理策略性能最佳.

基于模糊控制的燃料电池电动轻卡能量管理策略研究

以某款燃料电池电动轻型卡车作为研究对象,以提高燃料电池系统的经济性和耐久性为目的,利用Matlab/Simulink搭建了燃料电池电动轻卡动力系统仿真模型,对其能量管理策略进行优化。在原模糊控制策略的基础上进行改进,对燃料电池输出功率变化率进行约束,制定了一种改进后的模糊控制策略。将改进后的模糊控制策略与有限状态机控制策略和原模糊控制策略进行对比仿真验证。结果表明,在NEDC和UDDS循环工况下,改进后的模糊控制策略比原模糊控制策略氢耗量分别减少5.65%和8.29%;与有限状态机控制策略相比,改进后的模糊控制策略的氢耗量分别减少16.63%和10.64%,并且改进后的模糊控制策略的燃料电池输出功率波动幅度更小,变化更加稳定,提高了燃料电池系统的经济性和耐久性。

燃料电池汽车动力系统及能量管理策略研究进展

动力系统是燃料电池汽车(FCV)的核心,可分为单一式系统与混合动力系统两大类,其中,将燃料电池与辅助电源相结合组成“电-电”混合动力系统,已成为业界主流。本文根据辅助电源类型的不同,提出3类FCV混动系统的构建方案,分别为燃料电池+动力电池方案、燃料电池+超级电容方案、燃料电池+动力电池+超级电容方案,并对各方案的优势和劣势进行比较。同时,本文综述了近年来国内外学者提出的面向FCV的代表性能量管理策略,从理论基础与求解方法的差异出发,将现有燃料电池汽车的能量管理策略分为3类:基于规则定义的策略、基于最优化方法的策略以及基于机器学习的策略,并总结了各类策略在最优性与实时性等方面的优势和劣势。其中,基于规则定义的策略最易实现,在工程应用中最为普遍,但无法实现性能最优;基于最优化方法的策略能够接近甚至达到理论最优,但存在计算量过大、计算耗时过长、实时性差等问题;以强化学习为代表的基于机器学习的策略有望在最优性与实时性之间实现理想的平衡,但目前还存在模型训练耗时长、试错代价高等困难,在实车应用层面还存在一定挑战。基于文献研究与分析,本文提出以下观点:1)以大功率燃料电池为核心的功率混合型系统是FCV混动系统的未来发展方向;2)必须进一步提升智能化程度,根据实际使用场景开发具有个性化的能量管理策略;3)亟需建立关于燃料电池汽车能量管理策略的综合评价体系。

锂电池与燃料电池混合观光车动力系统的优化设计

为了解决传统电动车续航焦虑的问题,对燃料电池观光车进行了功率匹配设计与仿真验证,提出了一种功率跟随型能量管理策略。对比了纯锂电池观光车(LI)、纯燃料电池观光车(FC)、LI与FC的混合动力观光车(LI-FC)的输出特性,并仿真试验。结果表明:LI-FC的爬坡度与纯锂电池观光车不相上下,续航里程较纯锂电池观光车提升了15%,等效氢耗较纯锂电池观光车减少了53%,动力电池荷电状态(SOC)能保持在0.5~0.9之间稳定工作,燃料电池也会在额定工况与怠速工况下稳定切换。因此,燃料电池锂电池混合动力观光车具有这3种观光车中最优续航能力、较好的使用经济性、最好的使用稳定性,燃料电池与动力电池都能在期望工况下稳定工作,有较好的竞争力。

基于Stackelberg博弈的燃料电池混合动力汽车跟车能量管理

跟车场景下燃料电池混合动力汽车(FCHEV)的速度与能量管理协同优化是实现车辆节能的重要有效手段,针对现有策略中双能量源退化与能耗耦合关系不明,且难以兼顾全局优化与实时性能的问题,提出一种基于Stackelberg博弈的FCHEV跟车能量管理策略。首先,建立了燃料电池/锂电池的能耗与性能退化模型,并纳入到统一量纲的整车综合使用成本函数中;其次,提出了基于分层解耦的跟车能量管理策略,实现跟车速度与功率分配的解耦控制;最后,综合考虑跟车安全性、舒适性、燃料经济性和能源耐久性,建立跟车控制层与能量管理层对应的双层规划模型,并基于Stackelberg博弈思想设计了双层差分遗传算法对策略核心参数进行离线优化。仿真和实验结果表明:相较于模型预测控制方法,该方法可降低平均车间距误差37.7%、平均冲击度2.4%、等效氢气消耗9.3%和能源退化成本13.9%,实现了优化性能与实时性的兼顾。

基于强化学习的车队速度规划与能量管理联合优化

近年来,随着智能交通系统的快速发展,包括车车通信、车路通信等短距离实时无线通信信息,以及道路交通通行信息等远距离交通信息,使得车辆能够实时获知周围车辆运动情况以及前方交通环境情况,有利于提高车辆对周围交通环境的感知能力,以实现合理的出行安排与行驶控制,从而提高车辆的使用性能。为实现车队在多信号灯场景下的节能驾驶,提出了一种基于强化学习的车队速度规划与能量管理联合优化方法。通过SUMO平台,建立了包括5辆汽车的车队通过多个信号灯的场景,结果表明,提出的方法在舒适性、经济性和效率方面均优于传统的驾驶模型(Intelligent Driver Model,IDM)策略。

基于时间卷积的燃料电池汽车能量管理策略

为了适应智慧交通与智能网联汽车的快速发展,依据北京部分地图建立了智慧交通模拟数据集,结合因果膨胀卷积和残差连接的网络结构,设计并验证了新型基于时间卷积的数据驱动速度预测模型。为了提升燃料电池汽车的节能性,建立了等效氢耗的多目标优化函数及动力源寿命的约束条件。为了提高策略的实时性,建立了能量管理策略凸优化数学模型,使用了OSQP(operator splitting quadratic program)求解算法,在满足需求功率和计算实时性的前提下,通过合理分配动力源的输出功率,减少了动力源的寿命衰减。结果表明,提出的基于智慧交通的凸优化燃料电池汽车能量管理策略,相比于动态规划计算时间减少了90%以上,并保持等效氢耗基本一致。

基于模糊控制的增程式电动汽车能量管理控制研究

为了提升增程式电动汽车的燃油经济性并使其能够适应更加复杂的行驶工况,在保证汽车动力性的前提下,以控制电池SOC在工作范围内和等效百公里燃油消耗量较小为目标,以某增程式电动汽车为参考对象进行动力参数匹配,分别建立功率跟随控制策略模型、模糊功率跟随控制策略模型和加速度模糊功率跟随控制策略模型。在AVL_Cruise软件上完成整车模型的搭建并验证其动力性,运用Matlab/Simulink软件搭建控制策略模型,在WLTC和CLTC工况下进行联合仿真分析。试验表明:在满足动力性要求的基础上,功率跟随控制策略在WLTC和CLTC工况下的等效百公里燃油消耗量较其他两种控制策略较低,但是对CLTC较复杂的工况适应性差,具体表现在电池SOC的范围低于给定的电池工作范围,影响电池寿命;模糊功率跟随控制策略引入电池SOC状态,有效地改进了功率跟随对复杂工况适应性差的缺陷,但存在等效百公里燃油消耗量较大问题,且自身对复杂工况的适应性依旧存在缺陷;加速度模糊功率跟随控制策略综合考虑前二者控制策略的优劣性,引入加速度这一影响因素,解决了前二者对复杂工况适应性差的缺陷,与模糊功率跟随控制策略相比燃油经济性得到了提升。