基于BP人工神经网络的动力电池SOC估算方法

doi:10.12028/j.issn.2095-4239.2019.0027

储能科学与技术 ›› 2019, Vol. 8 ›› Issue (5): 868-873.doi: 10.12028/j.issn.2095-4239.2019.0027

基于BP人工神经网络的动力电池SOC估算方法

苏振浩¹, 李晓杰¹, 秦晋², 杜文杰¹, 韩宁¹

1 中北大学能源动力工程学院, 山西太原 030051;
2 中北大学机械工程学院, 山西太原 030051

收稿日期:2019-03-06 修回日期:2019-04-09 出版日期:2019-09-01 发布日期:2019-04-15
通讯作者: 苏振浩(1994-),男,硕士研究生,研究方向为电动汽车动力电池SOC的估算,E-mail:956540819@qq.com。
作者简介:苏振浩(1994-),男,硕士研究生,研究方向为电动汽车动力电池SOC的估算,E-mail:956540819@qq.com。

SOC estimation method of power battery based on BP artificial neural network

SU Zhenhao¹, LI Xiaojie¹, QIN Jin², DU Wenjie¹, HAN Ning¹

1 North University of China, School of Energy and Power Engineering, Taiyuan 030051, Shanxi, China;
2 North University of China, School of Mechanical Engineering, Taiyuan 030051, Shanxi, China

Received:2019-03-06 Revised:2019-04-09 Online:2019-09-01 Published:2019-04-15

摘要/Abstract

摘要： 电池剩余电量（SOC）的估算是电池管理系统中的关键技术之一，在众多估算方法中，神经网络在估算的准确性及鲁棒性上具有明显优势。庞大的数据量是获得SOC精确值的重要因素。针对以上问题，研究提出了基于BP人工神经网络的动力电池SOC估算方法，以某型号整包电池作为实验对象，通过对电池电压、电流、内阻及温度的数据采集，获得海量数据。建立电池的等效电路模型，考虑电池极化、充放电倍率及温度的影响对初始数据进行修正。基于MATLAB平台建立BP人工神经网络模型，数据修正后用于网络模型的训练，并验证了模型的可行性。将模型用于实验数据的预测，通过函数拟合实现了SOC的估算。最后，通过对比SOC的预测值与实际测量值，最终证明建立的人工神经网络模型对SOC估算的有效性。

关键词: 动力电池, 等效电路, 数据修正, 神经网络模型, SOC估算

Abstract: The state of charge (SOC) is one of the key technologies in battery management system. Among many estimation methods, neural network has obvious advantages in accuracy and robustness of estimation. The huge amount of data is an important factor to obtain the accurate value of SOC. To solve the above problems, a method of estimating SOC of power batteries based on BP artificial neural network is proposed. Taking a certain type of batteries as the experimental object, a large amount of data is obtained by collecting the data of voltage, current, internal resistance and temperature of batteries. Equivalent circuit model of battery was established, and the initial data were corrected considering the effects of battery polarization, charge-discharge ratio and temperature. BP artificial neural network model is established based on MATLAB platform, and the data are modified to train the network model, and the feasibility of the model is verified. The model is applied to the prediction of experimental data, and the estimation of SOC is realized by function fitting. Finally, by comparing the predicted and measured values of SOC, the validity of the artificial neural network model for estimating SOC is proved.

Key words: power battery, equivalent circuit, data correction, neural network model, SOC estimation

中图分类号:

苏振浩, 李晓杰, 秦晋, 杜文杰, 韩宁. 基于BP人工神经网络的动力电池SOC估算方法[J]. 储能科学与技术, 2019, 8(5): 868-873.

SU Zhenhao, LI Xiaojie, QIN Jin, DU Wenjie, HAN Ning. SOC estimation method of power battery based on BP artificial neural network[J]. Energy Storage Science and Technology, 2019, 8(5): 868-873.

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基于BP人工神经网络的动力电池SOC估算方法

SOC estimation method of power battery based on BP artificial neural network

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