汽车动力电池SOC估算的卡尔曼滤波数学算法

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中图分类号：U469.72 文献标识码：A 文章编号：1003-8639（2026）02-0023-03

Kalman Filter Mathematical Algorithm for Estimating the State of Charge of Automotive Batteries

Zhao Qingqing (Shangqiu Institute of Technology,Shangqiu 476ooo, China)

【Abstract】In the context of global energy structure transformation and the upgrading of environmental protection demands,electricvehiclesareleadingaprofoundtransformation intheautomotiveindustry.Asacorecomponent,the performanceofthe powerbatterydirectlydetermines thevehicle'srange,safetyperformance,andoperationalreliability. The Stateof Charge (SOC)is akey parameter that indicates theremaining battery capacityand is of great significance for optimizing energy alocation strategies,preventing overcharging and overdischarging of thebattery,and extendng the batery'sservice life.This paper focuses onthe isse of SOC estimation for power bateries,systematicallyanalyzing its theoretical basisand technical botlenecks,elaborating on the modeling aplicationof Kalman Filtertheoryand the key pointsof algorithmperformance evaluation,providing theoretical references andtechnicalsupportforengineering practice.

【Key words】 automotive power batteries；SOC estimation; KalmanFilter;algorithmoptimization

0 引言

在实际应用中，SOC估算存在诸多技术难点：电池本身的非线性特征十分显著，其电化学行为受工作温度、充放电速度、循环老化等多种因素影响；传感器测量噪声及系统干扰也使估算难度增加。（剩余6104字）