{"title":"一种用于电动汽车锂电池荷电状态估计的自适应滑模观测器","authors":"Y. Huangfu, J. N. Xu, S. Zhuo, M. Xie, Y.T. Liu","doi":"10.1109/PESA.2017.8277750","DOIUrl":null,"url":null,"abstract":"On the basis of the established second order RC equivalent circuit model, a novel adaptive sliding mode observer (ASMO) is proposed to estimate the state of charge (SOC) of lithium battery in the electric vehicle. The ASMO can adaptively adjust the switching gain according to the system output deviation. The Lyapunov stability theory is employed to prove the convergence of ASMO. Three different discharge curves are carried out, and the comparisons with conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF) are also presented to evaluate the performance of ASMO. The results show that: (1) compared with CSMO, ASMO can solve the contradiction between the SOC convergence speed and the chattering (2) compared with AEKF, ASMO has the similar SOC estimation accuracy, but possesses faster convergence speed, stronger robustness and less computation time.","PeriodicalId":223569,"journal":{"name":"2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"A novel adaptive sliding mode observer for SOC estimation of lithium batteries in electric vehicles\",\"authors\":\"Y. Huangfu, J. N. Xu, S. Zhuo, M. Xie, Y.T. Liu\",\"doi\":\"10.1109/PESA.2017.8277750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"On the basis of the established second order RC equivalent circuit model, a novel adaptive sliding mode observer (ASMO) is proposed to estimate the state of charge (SOC) of lithium battery in the electric vehicle. The ASMO can adaptively adjust the switching gain according to the system output deviation. The Lyapunov stability theory is employed to prove the convergence of ASMO. Three different discharge curves are carried out, and the comparisons with conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF) are also presented to evaluate the performance of ASMO. The results show that: (1) compared with CSMO, ASMO can solve the contradiction between the SOC convergence speed and the chattering (2) compared with AEKF, ASMO has the similar SOC estimation accuracy, but possesses faster convergence speed, stronger robustness and less computation time.\",\"PeriodicalId\":223569,\"journal\":{\"name\":\"2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA)\",\"volume\":\"282 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PESA.2017.8277750\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Power Electronics Systems and Applications - Smart Mobility, Power Transfer & Security (PESA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PESA.2017.8277750","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel adaptive sliding mode observer for SOC estimation of lithium batteries in electric vehicles
On the basis of the established second order RC equivalent circuit model, a novel adaptive sliding mode observer (ASMO) is proposed to estimate the state of charge (SOC) of lithium battery in the electric vehicle. The ASMO can adaptively adjust the switching gain according to the system output deviation. The Lyapunov stability theory is employed to prove the convergence of ASMO. Three different discharge curves are carried out, and the comparisons with conventional sliding mode observer (CSMO) and adaptive extended Kalman filter (AEKF) are also presented to evaluate the performance of ASMO. The results show that: (1) compared with CSMO, ASMO can solve the contradiction between the SOC convergence speed and the chattering (2) compared with AEKF, ASMO has the similar SOC estimation accuracy, but possesses faster convergence speed, stronger robustness and less computation time.