Research on ECMS Based on Segmented Path Braking Energy Recovery in a Fuel Cell Vehicle

Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.

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.

Interpretation of GB/T 26779-2021,Hydrogen fuel cell electric vehicles refueling receptacle

With the development of fuel cell electric vehicle industry in China,the 70-MPa hydrogen storage cylinders have been widely applied on vehicles in recent years.The revised standard,GB/T 26779-2021,Hydrogen fuel cell electric vehicle refueling receptacle,was released on March 9,2021 with added stipulations for the 70-MPa hydrogen refuelling receptacle.The main technical contents of GB/T 26779-2021 and its similarities and differences with GB/T 26779-2011 are discussed in this paper.

Interpretation of GB/T 24549-2009 Fuel Cell Electric Vehicle—Safety Requirements

The national standard GB/T 24549—2009 Fuel Cell Electric Vehicle—Safety Requirements specifies the general safety requirements for whole vehicle and key parts of Fuel Cell Electric Vehicle (FCEV).It is of great significance for the development of FCEV in china.This paper discusses the main contents and the background of its development.

A Resonant Dual Full-Bridge Class-E Bidirectional DC-DC Converter for Fuel Cell Electric Vehicle

The interests on energy storage schemes, bidirectional dc-dc converter and uninterruptible power supplies have been increasing nowadays as there wide researches are undertaken in the area of electric vehicles. A modified bi directional class-E resonant dc-dc converter is introduced here in this proposed topology for the application in electric vehicles. The advantages of soft switching techniques have been utilized for making analysis simple. The main advantage here in this system is that it can operate in a wide range of frequencies with minimal switching loss in transistors. This paper elaborates a detailed analysis on converter design and the same has been simulated and verified in Matlab/Simulink.

A New Topology of a Variable Output-Voltage DC-DC Converter for Fuel Cell Vehicles

The aim of this paper is to present a new topology of a DC-DC power converter for conditioning the current and voltages behaviors of embarked energy sources used in electrical vehicles. The fuel cells in conjunction with ultra-capacitors have been chosen as the power supply. The originality of the proposed converter is to use a variable voltage of the DC bus of the vehicle. The goal is to allow a better energy management of the embedded sources onboard the vehicle by improving its energy efficiency. After presenting and explaining the topology of the converter, some simulation and experiments results are shown to highlight its different operation modes.

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.

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles

Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.

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.

An adaptive fuel cell hybrid vehicle propulsion sizing model

As we enter the age of electrochemical propulsion,there is an increasing tendency to discuss the viability or otherwise of different electrochemical propulsion systems in zero-sum terms.These discussions are often grounded in a specific use case;however,given the need to electrify the wider transport sector it is evident that we must consider systems in a holistic fashion.When designed adequately,the hybridisation of power sources within automotive applications has been demonstrated to positively impact fuel cell efficiency,durability,and cost,while having potential benefits for the safety of vehicles.In this paper,the impact of the fuel cell to battery hybridisation degree is explored through the key design parameter of system mass.Different fuel cell electric hybrid vehicle(FCHEV)scenarios of various hydridisation degrees,including light-duty vehicles(LDVs),Class 8 heavy goods vehicles(HGVs),and buses are modelled to enable the appropriate sizing of the proton exchange membrane(PEMFC)stack and lithium-ion battery(LiB)pack and additional balance of plant.The operating conditions of the modelled PEMFC stack and battery pack are then varied under a range of relevant drive cycles to identify the relative performance of the systems.By extending the model further and incorporating a feedback loop,we are able to remove the need to include estimated vehicle masses a priori enabling improving the speed and accuracy of the model as an analysis tool for vehicle mass and performance estimation.

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.

基于蓄热装置辅助的燃料电池电动车供暖系统动态仿真

为了辅助燃料电池车辆预热,提升燃料电池在低温下的冷启动速率以及降低电动车能耗,该文提出了一种基于热泵和蓄热装置联合的燃料电池电动车供暖方案,并开展了燃料电池车辆系统动态仿真研究,对比了有无蓄热装置辅助的系统对车辆运行过程的影响。结果表明:在寒冷运行环境下,蓄热装置可确保驾驶循环工况下循环冷却液热量来源的连续性,相较于无蓄热装置的热管理系统,热泵和蓄热联合供暖方案可使空调系统能耗下降26%;利用蓄热装置辅助的供暖系统能够改善燃料电池电动车的冬季冷启动特性,优化车辆电池系统的热管理。

GB/T 26990-2023《燃料电池电动汽车车载氢系统技术条件》标准分析

近年来随着燃料电池电动汽车技术不断升级,与35 MPa车载氢系统相比,公称压力为70 MPa的车载氢系统具有储氢体积密度大可提高整车续驶里程的优点,在乘用车上得到广泛应用。而在GB/T 26990—2011《燃料电池电动汽车车载氢系统技术条件》及GB/T 29126—2012《燃料电池电动汽车车载氢系统试验方法》中仅适用于公称工作压力不超过35 MPa的车载氢系统,而且该版标准已实施十余年,已不能完全适应现阶段燃料电池电动汽车车载氢系统技术现状,因此2023年11月,我国发布了GB/T 26990—2023《燃料电池电动汽车车载氢系统技术条件》,该标准修改和增加了部分技术内容以适应当前燃料电池电动汽车行业发展水平。本文将阐述GB/T 26990—2023的主要技术内容及其与上一版标准的异同,并对新标准中相关技术内容和测试方法进行解析,供行业内企业参考。

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。

混合动力FCEV控制策略的仿真研究

目前国内外都在深入研究燃料电池混合动力电动汽车(FCEV),而FCEV的控制策略是更好的发挥FCEV的优势的一个关键。比较了FCEV与传统的汽车在整车性能上的不同,并从理论上分析了FCEV的能量分配和系统控制方式,用SIMULINK建立了一个FCEV的系统模型进行仿真,通过对仿真结果的分析验证了文中所述的控制策略。

基于工况识别的燃料电池电动汽车能量管理策略

为降低燃料电池电动汽车的氢气消耗、降低燃料电池输出功率波动率及维持电池系统的荷电状态,基于工况识别能量管理策略框架提出了一种新型转移过程工况识别方法,该方法相较于传统的工况识别方法具有识别率高、识别速度快等优点。与常见的学习向量量化神经网络工况识别方法进行对比,通过仿真分别验证了所提工况识别算法的优势,同时所提基于工况识别方法的能量管理策略在混合工况下具有更优的性能表现。

基于FTA-AHP的FCEV动力系统可靠性影响因子及其权重研究

FCEV动力系统是燃料电池汽车重要的子系统,一旦发生故障,轻则造成系统性能下降,重则造成人员和财产损失。利用故障树分析法(FTA)对FCEV动力系统的可靠性进行定性分析,建立了FCEV动力系统故障树,再运用层次分析法(AHP)对动力系统进行定量分析,得出FCEV动力系统影响因子的权重,结果表明对可靠性影响最大的是氢管理因子和动力蓄电池因子,燃料电池系统、DC/DC变换器因子和电机驱动因子次之,其中驱动电机CAN通讯故障对整车安全运行影响显著。最后根据FTA-AHP定性定量相结合分析的结果,提出了加长排氢气的时间、更改优化燃料电池吹扫策略等提高可靠性的途径和具体措施,为提高燃料电池汽车安全运行的可靠性、提前发现安全隐患等提供参考。

燃料电池混合动力汽车深度强化学习能量管理优化

针对配备有锂电池与超级电容的燃料电池混合动力汽车,为降低车辆总体运行成本,延长能量源寿命,本文提出一种基于深度强化学习的能量管理策略.首先,依据超级电容高功率密度特性,建立基于模糊自适应滤波的功率分层结构,并依据燃料电池与锂电池的经验退化模型,建立能量源退化的成本函数,采用等效消耗最小策略平衡氢耗成本与能量源退化成本,以最小化总体运行成本为目标来优化能量源功率分配;然后,引入优先经验回放与软更新以提高深度强化学习的离线训练效率;最后,在多种工况下进行仿真,结果表明,与未考虑退化的策略相比,本文所提出策略在全球统一轻型车辆测试循环下可使氢耗量降低11.8%,并可有效减缓燃料电池与锂电池的退化速率,降低燃料电池混合动力汽车的总体运行成本.

氢燃料电池混合动力汽车能量管理策略研究综述

氢燃料电池混合动力汽车的能量管理是整车控制的核心与关键,尤其是在提高燃料经济性和电池耐久性方面有重要作用。文中梳理了国内外基于规则、基于优化、基于学习和混合能量管理策略的研究进展,分析了各种策略的优势和局限。结果发现:规则型的策略易于实现,但适应性差;优化型的策略灵活性好,但计算复杂;学习型的策略自适应强,但需要大量数据支持;而国内外学者所提出的混合策略在氢燃料电池汽车氢耗量和电池寿命等方面更有优势。未来的研究会聚焦于结合不同策略的优势,将优化算法和机器算法引入基于规则和基于优化的策略,以及优化强化学习中的智能算法以提升整车性能。

氢燃料电池发展现状及其在电动飞行器中的应用分析

随着以氢燃料电池为代表的新型电池技术的发展,其在大尺寸长航程电动飞行器中的应用逐渐成为研究热点。介绍5种主要的燃料电池的工作原理和技术发展现状,并以两个概念方案为例,重点对质子交换膜燃料电池的适用性进行分析和评估。研究发现,5种燃料电池特点明显,从电池成本和电池效率方面考虑,碱性燃料电池非常适合在航空器上使用,尤其是在民航运输等对经济性要求较高的飞机;而质子交换膜燃料电池效率高,对负载响应速度快,输出功率稳定,相对于其他种类燃料电池优势明显;磷酸燃料电池对杂质的容忍度更高,不需要二氧化碳处理设备,且技术相对成熟;熔融碳酸盐燃料电池和固体氧化物燃料电池由于存在技术上的问题现阶段不适合在飞行器上应用。