原文信息:
Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles
原文链接:
https://doi.org/10.1016/j.apenergy.2024.124198
Highlights
(1) 制定燃料电池梯度下降功率策略抑制功率瞬变。
(2) 提出了一种自学习马尔可夫预测器用于实时预测。
(3) 平衡多目标成本函数以最小化全局成本。
(4) 进行数值验证和硬件在环测试以验证所提出的策略。
Research gap
针对燃料电池在车辆制动和激烈驾驶场景下的寿命衰减特性,本研究指出了一个重要研究空白:目前的研究未考虑应用梯度下降功率控制策略来抑制模式切换时的功率瞬态,从而优化降解成本。同时,传统离线速度预测方法在实时性和鲁棒性上不足,为此,文章提出了一种自学习马尔可夫预测器,以实现更为准确且具有强大实时性和鲁棒性的实时预测。
Abstract
The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy.
Keywords:
Fuel cell hybrid electric vehicle 燃料电池混合动力汽车
Battery life degradation 电池寿命衰退
Gradient drop power strategy 梯度下降功率策略
Markov prediction 马尔科夫预测
Energy management strategy 能量管理策略
Graphics
图1 自学习马尔可夫预测器示意图
图2 观测和预测时间域中变量的定义关系
图3 不同预测步长的状态转移矩阵
图4 FGDP策略的流程示意图
图5 FGDP策略下的最优步长
图6 FGDP策略改善燃料电池经济性的对比分析
团队介绍
团队介绍:
本研究由中国福州大学机械工程及自动化学院:林歆悠,周强,涂佳怡,徐心淏,谢丽萍共同完成。
作者简介:
林歆悠,博士,福州大学教授,博士生导师,主持和参与国家自然科学基金4项,其他省部级和横向课题10余项,在国内外发表论文60余篇,其中一篇入选“领跑者5000”中国精品科技期刊顶尖学术论文。EI期刊论文30余篇、SCI期刊论文20余篇分别发表于IEEE Transactions on Industrial Electronics、IEEE Intelligent Transportation Systems Transactions、Applied Energy、Energy、eTransportation等期刊。并以第一发明人申请发明专利23项。现为福州大学车辆工程新能源与智能控制团队负责人,带领团队围绕新能源电动汽车的动力电池管理、燃料电池系统控制、FCHEV(PHEV)整车驱动控制与自动驾驶的关键技术进行研究。
谢丽萍,博士、福州大学副研究员,硕士生导师。近年来,以第一作者或通讯作者在Mechanical Systems and Signal Processing、Energy、Applied Acoustics、Journal of Bionic Engineering等国际权威期刊和会议上发表SCI/EI论文20余篇,授权发明专利10余项,软件著作权2项。参与多项国家自然科学基金与企业委托项目,主持福州大学引进人员科研启动项目。担任《Apply acoustic》、《Journal of Mechanical Science and Technology》、《Frontiers in Neuroscience》等国际权威期刊特约审稿人。目前主要从事汽车噪声与振动控制、NVH主客观评价研究、新能源汽车关键技术研发、人因声振工程等研究。
关于Applied Energy
本期小编:魏长银 ;审核人:张俊涛
《Applied Energy》是世界能源领域著名学术期刊,在全球出版巨头爱思唯尔 (Elsevier) 旗下,1975年创刊,影响因子11.2,CiteScore 21.1,谷歌学术全球学术期刊第49位,工程期刊第19位,可持续能源子领域第2位,本刊旨在为清洁能源转换技术、能源过程和系统优化、能源效率、智慧能源、环境污染物及温室气体减排、能源与其他学科交叉融合、以及能源可持续发展等领域提供交流分享和合作的平台。开源(Open Access)姊妹新刊《Advances in Applied Energy》现已被ESCI收录。2024年将获得第一个影响因子。全部论文可以免费下载。在《Applied Energy》的成功经验基础上,致力于发表应用能源领域顶尖科研成果,并为广大科研人员提供一个快速权威的学术交流和发表平台,欢迎关注!
公众号团队小编招募长期开放,欢迎发送自我简介(含教育背景、研究方向等内容)至wechat@applied-energy.org
点击“阅读原文”
喜欢我们的内容?
点个“赞”或者“再看”支持下吧!