工程科学学报2024，Vol.46Issue(2)：376-384,9.DOI:10.13374/j.issn2095-9389.2022.11.22.005
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
Trip distance adaptive equivalent hydrogen consumption minimization strategy for fuel-cell electric vehicles integrating driving cycle prediction
摘要
Abstract
The environment pollution and petroleum problems,which are increasingly becoming serious,have caused the vehicle industry to transition into a low-carbon and energy-saving industry.During processes,plug-in fuel-cell electric vehicles(PFCEVs)play an important role due to their advantages of rapid fueling,high energy density and efficiency,low operating temperature,and zero onboard emissions.PFCEVs use high-capacity rechargeable batteries to avoid working in low-efficiency areas.However,a robust energy management strategy that can achieve reliable energy distribution by regulating the output power of the fuel cell and battery within the hybrid powertrain merits further investigation.Considering the close relationship between the driving cycle,state of charge(SOC),equivalent factor,and hydrogen consumption,a trip distance adaptive equivalent consumption minimum strategy integrating driving cycle prediction is proposed.A backpropagation-based neural network is used to predict short-term vehicle velocity and analyze future changes in vehicle demand power.Planning a path to the destination with the help of the global positioning system,the intelligent transportation system can also obtain traffic flow information for the entire trip.The equivalent factor is dynamically corrected in real time using the driving distance and SOC to realize the adaptability of the energy management strategy.Finally,the velocity prediction sequence is combined with the objective function.The sequential quadratic programming algorithm is used to optimize the equivalent hydrogen consumption of the objective function and to obtain the distributed power of the fuel cell and battery.The vehicle simulation model is built and compared with a traditional energy management strategy based on MATLAB/Simulink software.The simulation results show that the driving cycle prediction algorithm based on the backpropagation-based neural network predicts future short-term conditions better,with a 12.5%higher accuracy than the Markov method.The proposed energy management strategy allows the fuel cell to operate in high-efficiency areas.The hydrogen consumption is 55.6%less than that of the CD/CS strategy under the UDDS cycle.The hardware in the loop experiment verifies a hydrogen consumption that is 26.8%less than that of the CD/CS strategy under the EUDC cycle.The numerical validation results demonstrate the superior performance of the proposed strategy in terms of hydrogen consumption over the CD/CS strategy.The effectiveness of the proposed strategy is validated by hardware during the loop experiment.关键词
燃料电池汽车/能量管理策略/等效消耗最小策略/工况预测/反向传播神经网络Key words
fuel cell electric vehicle/energy management strategy/equivalent consumption minimum strategy/driving cycle prediction/BP neural network分类
交通工程引用本文复制引用
林歆悠,叶锦泽,王召瑞..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[J].工程科学学报,2024,46(2):376-384,9.基金项目
国家自然科学基金资助项目(52272389,51505086) （52272389,51505086）
载运工具与装备教育部重点实验室开放课题(KLCE2022-08) （KLCE2022-08）
安徽工程大学检测技术与节能装置安徽省重点实验室开放研究基金资助项目(JCKJ2021A04) （JCKJ2021A04）
