State of Charge Estimation for Power Batteries in New Energy Vehicles Considering Temperature Fluctuations

SUN Junguang; DU Rui; CHEN Lixin; LI Hui; WANG Bin; ZHAO Xueru; DONG Xiuhui

doi:10.7652/xjtuxb202411004

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State of Charge Estimation for Power Batteries in New Energy Vehicles Considering Temperature Fluctuations

更新时间：2025-07-25

- State of Charge Estimation for Power Batteries in New Energy Vehicles Considering Temperature Fluctuations
- Vol. 58, Issue 11, Pages: 39-51(2024)
- 作者机构：
  
  1. 工业产品环境适应性全国重点实验室,广州,510663
  2. 中国电器科学研究院股份有限公司,广州,510663
  3. 西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202411004
  CLC： TM912
- Published：2024
- 稿件说明：
移动端阅览
SUN Junguang, DU Rui, CHEN Lixin, et al. State of Charge Estimation for Power Batteries in New Energy Vehicles Considering Temperature Fluctuations[J]. 2024, 58(11): 39-51.
DOI：

SUN Junguang, DU Rui, CHEN Lixin, et al. State of Charge Estimation for Power Batteries in New Energy Vehicles Considering Temperature Fluctuations[J]. 2024, 58(11): 39-51. DOI： 10.7652/xjtuxb202411004.

摘要

针对温度变化应用场景下新能源汽车动力电池荷电状态(SOC)估计不精确问题

提出了一种基于神经网络和无迹卡尔曼滤波修正的电池SOC估计方法。首先

考虑温度变化影响建立动态参数的Thevenin等效电路模型。分析温度变化条件下的电池开路电压变化特性

确定电池SOC与开路电压之间的对应关系。同时

分析温度变化条件下的电池容量变化特性

采用神经网络训练电池容量随温度变化的神经网络温度因子。进一步

通过带遗忘因子的递推最小二乘法辨识模型动态参数。在此基础上

利用神经网络温度因子和无迹卡尔曼滤波实时修正以实现温度变化条件下的精确SOC估计。实验结果表明:相比于传统的电池SOC估计方法

考虑温度变化的电池SOC估计方法可以显著提高SOC估计精度

在-15 ℃低温环境下

所提方法使SOC估计精度提高了2.77%。

Abstract

To address the issue of inaccurate state of charge(SOC)estimation for power batteries in new energy vehicles in scenarios involving temperature variations

a modified battery SOC estimation method based on neural network and unscented Kalman filter(UKF)is proposed. Initially

a Thevenin equivalent circuit model with dynamic parameters is established

taking into account the effects of temperature fluctuations. The characteristics of changes in battery open-circuit voltage(OCV)under varying temperature conditions are examined to establish the relationship between battery SOC and OCV. Additionally

the alterations in battery capacity under temperature variations are analyzed. A neural network is employed to train the neural network temperature factor for battery capacity reduction corresponding to temperature changes. Furthermore

the dynamic model parameters are identified using the recursive least squares algorithm with a forgetting factor. Subsequently

the neural network temperature factor and the UKF are utilized for real-time adjustments to ensure precise SOC estimation under changing temperature conditions. The results demonstrate that the battery SOC estimation method considering temperature variations significantly enhances SOC estimation accuracy compared to traditional methods. In a low-temperature environment of -15 ℃

the proposed method enhances SOC estimation accuracy by 2.77%.

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references

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