CAJ | 학술논문

在大量充放电模拟试验和随车试验数据采集的基础上,构建了基于数据信号处理器(DSP)芯片TMS320C2812的电池管理系统,实现了数据监测、荷电状态(SOC)估计、控制局域网(CAN)通信及USB存储等功能.在SOC估计算法上,根据电池所处状态进行了分类分析,并对估算难度最大的电池动态放电状态的算法进行了仿真实验.实验结果表明,该算法对镍氢电池的SOC能进行准确预测,并具有较高的精度.
재대량충방전모의시험화수차시험수거채집적기출상,구건료기우수거신호처리기(DSP)심편TMS320C2812적전지관리계통,실현료수거감측、하전상태(SOC)고계、공제국역망(CAN)통신급USB존저등공능.재SOC고계산법상,근거전지소처상태진행료분류분석,병대고산난도최대적전지동태방전상태적산법진행료방진실험.실험결과표명,해산법대얼경전지적SOC능진행준학예측,병구유교고적정도.
According to the data obtained from charge/discharge experiments and on-site data from a running Electric Vehicle(EV), a battery management system has been developed on the basis of 32-bits DSP controller TMS320C2812.The functions of the system include data monitoring, State of Charge(SOC) estimation, Controller Area Network(CAN) communications, USB-based data storage and so on.This paper also proposed an improved SOC estimation method, which divides into three algorithms according to the state of batteries.In the algorithm of the dynamic discharge state, Ampere/hours method is integrated with RBF neural network, and its accuracy and practicality are validated via Matlab and Advisor simulation.