基于综合型卡尔曼滤波的锂离子电池荷电状态估算

doi:10.19595/j.cnki.1000-6753.tces.171560

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Abstract

A comprehensive Kalman filtering algorithm is given to estimate the state of charge (SOC) of Li-ion battery. The battery model parameters are identified and modified by recursive least squares algorithm (RLS) in real time, and a comprehensive Kalman filter is applied to estimate the battery SOC, namely a linear Kalman filter (KF) and a square-root high-degree cubature Kalman filter (SHCKF) are used to process the linear part and nonlinear part of the model respectively. The combination of two filters efficiently reduces the computation complexity. By adopting the 5th-degree spherical-radial cubature quadrature rule and square root filtering technology, SHCKF achieves higher estimation accuracy and strong numerical stability than traditional nonlinear filters, such as extended Kalman filter (EKF), unscented Kalman filter (UKF) and cubature Kalman filter (CKF). The experimental results prove the feasibility and effectiveness of the proposed algorithm.

Key words： Battery state of charge (SOC) square-root high-degree cubature Kalman filter (SHCKF) comprehensive Kalman filter

Received: 16 November 2017 Published: 29 January 2019

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TM912

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	Gu Miao
	Xia Chaoying
	Tian Congying

Cite this article:

Gu Miao,Xia Chaoying,Tian Congying. Li-ion Battery State of Charge Estimation Based on Comprehensive Kalman Filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171560 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I2/419

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