Fast and high-precision online SOC estimation for improved model of lithium-ion battery based on temperature correlation coefficient

• Published in: Ionics 2024, Vol.30 (6), p.3477-3493
• Main Authors: Yi, Lingzhi, Chen, Fuyou, Wang, Yahui, Luo, Bote, Fan, Lv, Cai, Xinkun
• Format: Article
• Language: English
• Subjects: Algorithms; Chemistry; Chemistry and Materials Science; Condensed Matter Physics; Correlation coefficients; Electric charge; Electrochemistry; Energy Storage; Equivalent circuits; Extended Kalman filter; Lithium-ion batteries; Optical and Electronic Materials; Parameter identification; Rechargeable batteries; Renewable and Green Energy; State of charge; Temperature
• ISSN: 0947-7047; 1862-0760
• DOI: 10.1007/s11581-024-05523-3

In high-energy and high-power applications, thousands of batteries are connected in series and parallel, imposing a substantial computational burden for state of charge (SOC) estimation. The second-order RC equivalent circuit model is often utilized for SOC estimation. However, this model requires the identification of numerous parameters, rendering the calculations complex and computationally intensive. Furthermore, the model often neglects the impact of temperature. To enhance the speed and accuracy of SOC estimation for numerous individual cells, an equivalent circuit model is constructed. This model incorporates temperature correlation coefficients and the electrical characteristics of lithium-ion batteries at various temperatures. Subsequently, a combined forgetting factor recursive least squares and extended Kalman filter algorithm is introduced for battery SOC estimation. The results demonstrate that the improved model significantly reduces SOC estimation time. Compared to the traditional second-order RC model, the improved model reduces the time by 37.8%, 58.3%, and 34% at − 10 °C, 0 °C, and 25 °C, respectively.