Abstract
Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.
Highlights
Due to the global energy shortage and environmental pollution, countries around the world have attached great importance to the development of electric vehicles
This paper aims to make the following contributions: (1) By comparing and analyzing equivalent circuit model (ECM), the most suitable ECM and parameter identification algorithms are decided; (2) A low-cost and accurate SOC estimator based on the adaptive Kalman filter (AKF) for the proposed model is developed, and its accuracy and robustness are verified by experiments under constant current working conditions and BBDST working conditions; and
An adaptive Kalman filter method based on the equivalent circuit model is proposed for the SOC estimation algorithm of a lithium-ion battery
Summary
Due to the global energy shortage and environmental pollution, countries around the world have attached great importance to the development of electric vehicles. The lithium-ion battery is considered to be an ideal electric vehicle power battery with high safety, large discharge power, environmental protection, less pollution, and long cycle life [1,2]. The increase in energy density and electrochemical performance of the battery often means a decrease in safety performance, which is prone to safety accidents. In [4], the differences in the safety behavior between un-aged and aged high-power 18650 lithium-ion cells were investigated at the cell and material level by accelerating rate calorimetry (ARC) and simultaneous thermal analysis (STA). The results show that the aging of the battery will lead to the mechanical deformation of the jelly roll and lithium plating
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