An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles |
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| Affiliation: | 1. The School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798 Singapore;2. State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China;1. National Engineering Lab for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China;2. Energy, Controls, and Applications Lab, University of California, Berkeley, CA 94720, USA;3. State Key Lab of Automotive Safety and Energy, Dept. of Automotive Eng., Tsinghua University, Beijing 100084, China;1. National Engineering Laboratory for the Automotive Electronic Control Technology, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China;2. Center for Automotive Research, The Ohio State University, 930 Kinnear Road, Columbus, OH 43212, USA;1. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore;2. TUM CREATE, 1 CREATE Way, CREATE Tower, Singapore 138602, Singapore |
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| Abstract: | As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation. |
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| Keywords: | Adaptive sliding mode observer State of charge (SOC) estimation State of health (SOH) estimation |
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