Machine learning an alternate technique to estimate the state of charge of energy storage devices

作     者：Zahid, Taimoor Xu, Kun Li, Weimin 

作者机构：Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Jining Inst Adv Technol Jining Peoples R China 

出 版 物：《ELECTRONICS LETTERS》 (电子学快报)

年 卷 期：2017年第53卷第25期

页      面：1665-1666页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：Natural Science Foundation of China [61273139  61573337  61603377] 

主　　题：battery powered vehicles electrical engineering computing learning (artificial intelligence) 

摘      要：State of charge (SOC) estimation plays a critical role in the operation of an electric vehicle (EV) power battery. In this Letter, the authors propose machine learning (ML) algorithms as alternate to the existing filtering algorithms used for SOC estimation of an EV battery. The SOC estimation approach is evaluated by the simulation experiments in advanced vehicle simulator (ADVISOR). For the modelling of ML algorithms, the input parameters that affect the SOC estimation are battery current, battery module temperature, power out of the battery (available and requested), battery power loss and heat removed from the battery. Training and testing stages of the models are carried out using the data collected from ADVISOR. As the drive cycle conditions provided by ADVISOR are universal therefore present method is applicable to all kinds of batteries used in EVs including lithium ion, nickel metal hydride and lead acid batteries. Thus, the proposed models for SOC estimation provide an alternative approach in SOC estimation.