Abstract
The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.
Highlights
Rising energy costs and tightening regulations on exhaust emissions of ground vehicles emphasize the need for electric vehicles (EVs) [1]
In [7], the initial state of charge (SOC) was obtained by the open-circuit voltage (OCV) method, and the SOC was estimated by the Coulomb counting method based on capacity and Coulombic efficiency modification
According to the analysis of the SOC estimation algorithm in Section 5.2.1, it can be determined that the operating conditions of the state of health (SOH) algorithm are as follows: the SOC used in the SOH algorithm should be greater than 50%, and the battery should not be at a low ambient temperature when the algorithm is calculated
Summary
Rising energy costs and tightening regulations on exhaust emissions of ground vehicles emphasize the need for electric vehicles (EVs) [1]. The Coulomb counting method calculates the remaining capacity of the battery by integrating the current and time, but the estimation accuracy decreases during operation due to cumulative errors. Many scholars studied deuterogenic algorithms based on the Kalman filter for SOC estimation, including the extended fractional Kalman filter, correntropy EKF, and improved EKF [13,14,15] These methods combine the CCM and the OCV method, and they possess the advantages of real-time performance, good computational efficiency, and good estimation accuracy. In [23], an SOH estimation model was established using a Gaussian regression process, and the OCV range, which has sensitive characteristic parameters, was quantified This method has great estimation accuracy, but the conditions of low current and specific range variation are unlikely to be met in practical applications.
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