Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System

被引：14

作者：

Kim, Min-Joon ^{[1
]}

Chae, Sung-Hun ^{[1
]}

Moon, Yeon-Kug ^{[1
]}

机构：

[1] Korea Elect Technol Inst, SoC Platform Res Ctr, Seongnam, South Korea

来源：

2020 17TH INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC 2020) | 2020年

关键词：

State-of-charge (SOC); Battery management system (BMS); Extended kalman filter (EKF); Electric vehicle (EV); LITHIUM-ION BATTERY;

D O I：

10.1109/ISOCC50952.2020.9332950

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.

引用

页码：288 / 289

页数：2

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