摘要
为降低燃料电池客车的等效氢耗量,延长动力电池的寿命,以中国重型商用车-客车工况(CHTC-C)中城市、郊区和高速3种工况为标准工况,提出灰狼优化支持向量机(GWO-SVM)工况识别算法,应用于燃料电池客车能量管理策略中,并在Cruise中搭建整车模型进行仿真验证。仿真结果表明:提出的能量管理策略车速跟随情况良好,符合客车动力性要求;相比于CV-SVM工况识别能量管理策略和无工况识别能量管理策略,提出的能量管理策略使动力电池荷电状态(SOC)变化相对平稳,客车的燃油经济性得到改善。
In order to reduce the equivalent hydrogen consumption of fuel cell bus and prolong the life of power battery,taking the urban,suburban and high-speed working conditions in China heavy commercial vehicle bus working condition(CHTC-C)as the standard working conditions,grey wolf optimized support vector machine(GWO-SVM)working condition recognition algorithm is proposed and applied to the energy management strategy of fuel cell bus.The whole vehicle model is built in Cruise for simulation verification.The simulation results show that the proposed energy management strategy has good speed following and meets the requirement of bus power performance;Compared with CV-SVM working condition recognition energy management strategy and no working condition recognition energy management strategy,the proposed energy management strategy makes the change of power battery state of charge(SOC)relatively stable and the fuel economy of the fuel cell bus is improved.
作者
王琳皓
何锋
李惠林
边东生
WANG Linhao;HE Feng;LI Huilin;BIAN Dongsheng(School of Mechanical Engineering,Guizhou University,Guiyang 550025,China;Chery Wanda Guizhou Bus Co.,Ltd.,Guiyang 550025,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2022年第5期62-69,共8页
Journal of Chongqing University of Technology：Natural Science
基金
贵州省科技支撑计划(黔科合支撑[2021]一般283)。
关键词
燃料电池客车
灰狼优化算法
支持向量机
工况识别
模糊控制
fuel cell bus
gray wolf optimization algorithm
support vector machine
working condition recognition
fuzzy control
