An Electric Vehicle Battery Parameter Identification Method Based on VFFRLS Algorithm and Considering Environmental Temperature Influence

Accurate and real-time identification of battery model parameters is crucial for battery state estimation and lifetime prediction. Especially for electric vehicles (EV), the operating conditions are complex, with random charging and discharging, battery parameters vary with factors such as the operating conditions of EV, temperature, and usage life. To improve the accuracy of identification and better align with the complex operating conditions of EV, accelerate the development of EV, reduce fuel consumption, and protect the environment, this article employs the second-order RC circuit as the battery model. Firstly, a three-dimensional relationship (OCV-SOC-T) is established between the open-circuit voltage (OCV) and state of charge (SOC) of the battery and temperature. Then, a Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm is put forward, and this algorithm adjusts the forgetting factor online based on the multi-innovation matrix. Finally, combine OCV-SOC-T relationship and VFFRLS algorithm to recognize the battery parameters. Experimental data validation demonstrates that the proposed method not only achieves high-precision and real-time identification of EV battery parameters under dynamic operating conditions but also adapts to changes in environmental temperature.