State of Charge Estimation of Lithium Battery Packs in Wide Temperature Environments Based on Migration Model

The challenges posed by single-cell inconsistency and external ambient temperature variations in Li-ion battery packs hinder the accurate and efficient estimation of their state of charge (SOC). Traditional methods often overlook the impact of cell inconsistency or exhibit high computational complexity, making it challenging to achieve both efficient and accurate SOC estimation for battery packs. To improve the accuracy and efficiency of SOC estimation of lithium-ion battery packs under the actual use environment, this study proposes a method to estimate the SOC of lithium-ion battery packs based on the migration model. First, the migration model is built based on the traditional second-order RC equivalent circuit model through parameter identification and SOC-model parameter relationship curve fitting to handle the influence of temperature changes on model parameters thereby reduce the workload required for repetitive modeling; then, the battery pack SOC is characterized by combining the V min+ V max model (VVM) to fully consider the influence of battery inconsistency and reduce the complexity of the battery pack SOC estimation. Simultaneously, in the battery pack state of charge (SOC) estimation, two weight factors are introduced to adjust the battery pack's output SOC. This measure is implemented to prevent overcharging and over-discharging of the battery pack, ensuring its safety; Finally, experimental tests were designed for the battery pack under various temperatures and dynamic temperature states to validate the accuracy of single-level and module-level SOC estimation, and comparative analysis was conducted with the traditional battery pack SOC estimation method to assess performance differences. The validation results indicate that the proposed method maintains good computational accuracy in estimating the SOC of individual cells and modules at several different temperatures, under variable temperature conditions, the SOC estimation method for battery packs still maintains a high accuracy. Among them, the maximum average absolute error in estimating the SOC of an individual battery is 1.30%; the maximum average absolute error in estimating the SOC of the battery pack under constant temperature conditions is 1.49%; the maximum average absolute error in estimating SOC of the battery pack under variable temperature condition is 1.21%. The results demonstrate that the proposed method offers high computational accuracy, and low computational complexity, and ensures safety and reliability. © 2024 Chang'an University. All rights reserved.