An innovative square root - untraced Kalman filtering strategy with full-parameter online identification for state of power evaluation of lithium-ion batteries

In the context of the thriving development of new energy vehicles, lithium-ion batteries, as a crucial component of the power storage system, will increasingly contribute to the strategic advancement of the industry, while this paper addresses three key issues in the estimation of lithium-ion battery state of charge (SOC) and state of power (SOP). Firstly, an online modified square root - untraced Kalman filtering (SR-UKF) algorithm is proposed to analyze the impact of temperature-induced capacity fluctuations, achieving highly accurate and adaptive SOC tracking. Secondly, an online multi-limit factor fusion analysis SOP estimation method is designed to mitigate computational complexity and enhance algorithm feasibility by addressing parameter fitting issues during offline identification. Thirdly, a real-time tracking data-based full-parameter online identification method is developed to enhance the accuracy of parameter identification and effectively describe internal and external factors. Experimental results demonstrate the algorithm's high accuracy, with a voltage simulation error below 0.04 V. Compared to traditional methods, the SR-UKF algorithm exhibits lower SOC simulation error below 2.36 %, offering a novel approach for SOC estimation under ambient temperature influences. Moreover, the proposed algorithm effectively estimates SOP, with a peak power estimation error of down to 66 W. In conclusion. This paper presents a novel SOC and SOP evaluation strategy, achieving a more reliable and accurate estimate under varying operating conditions.