汽车动力电池 SOC 估算的卡尔曼滤波数学算法

汽车电器 ›› 2026, Vol. 1 ›› Issue (2) : 23-25.

新能源

赵晴晴

作者信息 +

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

Zhao Qingqing

Author information +

文章历史 +

摘要

在全球能源结构转型与环保需求升级背景下，电动汽车正引领汽车产业深刻变革，动力电池作为核心部件，其性能直接决定整车续航能力、安全性能及运行可靠性。荷电状态（State of Charge，SOC）是表征电池剩余电量的关键参数，对优化能量分配策略、防止电池过充过放、延长电池使用寿命具有重要意义。本文围绕动力电池 SOC 估算问题，系统分析其理论基础与技术瓶颈，阐述卡尔曼滤波理论的建模应用及算法性能评估要点，为工程实践提供理论参考与技术支撑。

Abstract

In the context of global energy structure transformation and the upgrading of environmental protection demands, electric vehicles are leading a profound transformation in the automotive industry. As a core component, the performance of the power battery directly determines the vehicle's range, safety performance, and operational reliability. The State of Charge (SOC) is a key parameter that indicates the remaining battery capacity and is of great significance for optimizing energy allocation strategies, preventing overcharging and overdischarging of the battery, and extending the battery's service life. This paper focuses on the issue of SOC estimation for power batteries, systematically analyzing its theoretical basis and technical bottlenecks, elaborating on the modeling application of Kalman Filter theory and the key points of algorithm performance evaluation, providing theoretical references and technical support for engineering practice.

导出引用

赵晴晴. 汽车动力电池 SOC 估算的卡尔曼滤波数学算法[J]. 汽车电器. 2026, 1(2): 23-25

Zhao Qingqing. Kalman Filter Mathematical Algorithm for Estimating the State of Charge of Automotive Batteries[J]. AUTO ELECTRIC PARTS. 2026, 1(2): 23-25

中图分类号： U469.72