锂离子电池组内单体不一致性以及外界环境温度变化给其荷电状态(State of Charge,SOC)精确且高效估计带来挑战,而传统方法往往忽略电池不一致性影响或者计算复杂度高,难以对电池组进行高效且精确的SOC估计.为提升实际使用环境下锂电池组SOC估计精度和效率,开发了一种基于迁移模型的锂电池组SOC估计方法.首先,在传统二阶RC等效电路模型的基础上通过参数辨识、SOC与模型参数关系曲线拟合等工作完成迁移模型搭建,以此来应对温度变化对模型参数的影响并降低重复建模所需的工作量;之后,结合Vmin+Vmax模型(Vmin+Vmax Model,VVM)对电池组SOC进行了表征,充分考虑电池不一致性影响并且减少了电池组SOC估计的复杂程度.同时在电池组SOC估计的过程中融入权重因子,调整电池组输出SOC,防止电池组出现过充过放现象,保证电池组的使用安全;最后,设计开展了不同温度以及变温状态下的电池组试验测试,对电池组中单体级及模组级SOC估计精度进行了验证,并与传统的电池组SOC估计方法进行对比分析.验证结果表明:所提方法在几种不同温度下的单体及模组SOC估计结果均保持较好的计算精度;在变温状态下,电池组SOC估计方法仍然具有较高的精度.其中,单体电池SOC估计结果平均绝对误差最大为1.30％;在恒温状态下,电池组SOC估计结果的最大平均绝对误差为1.49％;而电池组SOC估计结果在变温状态下的最大平均绝对误差为1.21％.证明了所提方法具有计算精度高、计算复杂程度低以及安全可靠的优点.
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 Vmin+Vmax 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.
automotive engineeringstate of charge estimationmigration modelbattery packtemperature
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