A Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter
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Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter Jiabao Taoa, Dunyao Zhua, Chuan Sunb, c, *, Duanfeng Chua, Yulin Mac, Haibo Lid, Yicheng Lie, and Tingxuan Xuf a
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, 430063 China School of Electromechanical and Automobile Engineering, Huanggang Normal University, Huanggang, 438000 China c Suzhou Automotive Research Institute, Tsinghua University, Suzhou, 215134 China dDongfeng Motor Corporation Technical Center, Wuhan, 430056 China e Suzhou Change Auto Technology Co., Ltd, Suzhou, 215134 China fInternational Department, The Affiliated High School of SCNU, Guangzhou, 510630 China *e-mail: [email protected]
b
Received November 26, 2019; revised March 10, 2020; accepted March 11, 2020
Abstract—Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. Besides, SOC estimation accuracy and stability is superior to traditional algorithm obviously. Keywords: Electric vehicle, SOC, 1-order lag model, observation noise adaptive adjust, adaptive particle filter DOI: 10.3103/S0146411620050089
1. INTRODUCTION The cost, specific energy and cycle life of the power batteries are the key factors that restrict the development of electric vehicles. There are two approaches to solve this problem: to develop a battery with low cost, high specific energy, and long cycle life; to develop an effective battery management system and give full play to its performance advantages through scientific management [1]. State of Charge (SOC) estimation is an important part of battery management. To study the SOC estimation problems of the electric vehicles is of practical significance for battery science management and a reasonable charging schedule. Battery SOC estimation methods can be classified into three categories: traditional methods (including open-circuit voltage method [2], internal resistance method [3] and ampere-hour integral method [4]); black-box model based estimation methods (including neural network model [5], fuzzy logic model and support vector regression model [6]); state-space model based estimation methods (generally including Kalman filter [7], particle filter [8] and H∞ algorithm). Among which, the traditional methods are not suitable for estimation when the battery is used. The black-box model base
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