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State of Charge Estimation Online Based on EKF-Ah Method for Lithium-Ion Power Battery

Jie Xu, Mingyu Gao, Zhiwei He, Quanjun Han, Xuguang Wang

Open publisher page 25 citations

Abstract

As for battery management systems (BMS), it is the most important and significant aspect to estimate state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power battery's properties and its operation condition in electric vehicles, we propose a new method named EKF-Ah that derives from extended Kalman filtering (EKF) algorithm and ampere hour counting method. This method has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. The paper covers the definition of SOC, analyzes and compares some common used estimations , finally discusses the EKF-Ah method in detail. Results of laboratory tests show that the maximal SOC estimation error is under 6.5%, which validates the feasibility and availability of the EKF-Ah online estimation.

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What this paper is about

As for battery management systems (BMS), it is the most important and significant aspect to estimate state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power battery's properties and its operation condition in electric vehicles, we propose a new method named EKF-Ah that derives from extended Kalman filtering (EKF) algorithm and ampere hour counting method. This method has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. The paper covers the definition of SOC, analyzes and compares some common used estimations , finally discusses the EKF-Ah method in detail. Results of laboratory tests show that the maximal SOC estimation error is under 6.5%, which validates the feasibility and availability of the EKF-Ah online estimation.

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Available abstract

As for battery management systems (BMS), it is the most important and significant aspect to estimate state of charge (SOC) accurately, which can provide the judgment basis to system control strategy. In view of the lithium-ion power battery's properties and its operation condition in electric vehicles, we propose a new method named EKF-Ah that derives from extended Kalman filtering (EKF) algorithm and ampere hour counting method. This method has a good performance on SOC estimation in complicated environment and is able to accomplish the requirements on power batteries. The paper covers the definition of SOC, analyzes and compares some common used estimations , finally discusses the EKF-Ah method in detail. Results of laboratory tests show that the maximal SOC estimation error is under 6.5%, which validates the feasibility and availability of the EKF-Ah online estimation.

Key concepts: Extended Kalman filter, State of charge, Battery (electricity), Computer science, Kalman filter, Power (physics), Control theory (sociology), State (computer science)

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