Power battery SOC estimation based on DP model and support vector regression

Power battery SOC estimation is one of the most important functions of a battery management system and is a quantitative assessment of the driving range of an electric vehicle. Due to the complex battery dynamics and environmental conditions, existing data-driven battery state estimation techniques are unable to accurately estimate the battery state. To address these issues, this paper proposes an algorithm based on a combination of data-driven and equivalent circuit models to improve estimation accuracy and adapt to a variety of application scenarios. The details of the research are as follows: the advantages and disadvantages of the equivalent circuit model-based and support vector regression (SVR) algorithms are combined to compensate for the errors in the estimation results based on the equivalent circuit model through quadratic estimation, and then the algorithm is tested offline at different temperatures and under different levels of capacity degradation to verify its superiority. The experimental results show that the average prediction error is less than 0.015 for different temperatures and capacity decay, with a slight increase in prediction error at very low temperatures (-20°C) and multiple cycles (600 cycles), with the largest errors occurring at the end of the resting period when the discharge is switched.