Cyber Hierarchy Multiscale Integrated Energy Management of Intelligent Hybrid Electric Vehicles
作者机构：School of Automotive EngineeringShandong Jiaotong University5001 Haitang RoadJinan 250357China School of Transportation Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Transport Industry for Transport Vehicle TestingDiagnosis and Maintenance Technology of the Ministry of CommunicationsShandong Jiaotong UniversityJinan 250357China Shandong New Energy Vehicles Test and Identify Research InstituteShandong Jiaotong UniversityJinan 250023China
出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)
年 卷 期：2022年第5卷第4期
页 面：438-452页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
基 金：support from the Key R&D Program of Guangdong Province,China(2020B0909030002) the Natural Science Foundation of Shandong Province(ZR2021MB027) the Shandong Provincial Higher School Youth Innovation Technology Project of China(2020KJB002) the Doctoral research fund of Shandong Jiaotong University(BS2020006,BS2018045)
主 题：Cyber hierarchy Multiscale optimal control Physics-informed machine learning Genetic algorithm Battery
摘 要：The full-lifespan management concept provides a new pathway to seeking solutions from macro-application scenarios to micro-mechanism *** paper presents a cyber hierarchy multiscale optimal control method for multiple intelligent hybrid vehicles to fully release the potentials of vehicle components while guaranteeing driving safety and *** can be generally divided into the cyber intelligent driving system on the cyber-end and the intelligent vehicle system on the *** the cyber-end,the state information of the surrounding vehicles is transmitted via the Vehicle-to-Everything structure and further processed in the cloud platform to generate future driving behaviors based on a car-following *** the vehicle-end,an optimized control sequence for vehicle components at micro-levels is derived by incorporating a physics-informed neural network model for battery health *** results show that global optimization needs high coupling between the macro-and micro-physical *** introducing the genetic algorithm for time smoothing,the improved driving strategy is capable of macro-and micro-coupling,and thus improves the controllable performance in time ***,this method spans the complexity of space,time,and chemistry,enhances the interpretation performance of machine learning,and slows down the battery aging in the process of multiscale optimization.
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