考虑放电倍率的电池储能容量自适应SOC估计方法

doi:10.19799/j.cnki.2095-4239.2025.0080

摘要/Abstract

摘要：

随着新能源技术的快速发展，储能电池在电力系统中的应用日益广泛，准确估计荷电状态（State of Charge，SOC）已经成为保障电池性能、延长使用寿命和确保安全运行的关键。为提高电网储能电池在变功率需求下的SOC估算精度，本文提出了一种基于容量动态修正的SOC估算方法。首先，针对传统SOC估算方法在复杂工况下的误差产生机理进行了深入分析，并提出了总体改进思路；其次，分析了储能电池在不同放电倍率下的容量变化特性，建立了放电倍率及容量的定量表征模型，为精确估算SOC提供了理论基础；接着，提出一种融合深度神经网络与扩展卡尔曼滤波法（Extended Kalman Filter，EKF）结合的CLA-EKF估计算法，充分利用二者在处理复杂非线性关系以及抗干扰方面的优势，进一步构建了考虑放电倍率的容量自适应修正模型，显著提高了SOC估算的精度和稳定性。实验结果表明，本文提出的基于容量修正的CLA-EKF方法在多种变功率工况下显著提升了SOC估算的精度，相较于传统方法显著提升了SOC估算精度，验证了其优越性和适用性。本文方法为电网储能电池多场景运行的SOC估计提供了有益参考，具有较好的实际应用价值。

关键词: 储能电池, SOC估计, 放电倍率, 卡尔曼滤波, 神经网络

Abstract:

With the rapid development of renewable energy technologies, the application of energy storage batteries in power systems has become increasingly widespread. Accurately estimating the State of Charge (SOC) of batteries is critical to ensuring battery performance, extending lifespan, and ensuring safe operation. To improve the accuracy of SOC estimation for grid energy storage batteries under varying power demands, this paper proposes an SOC estimation method based on dynamic capacity correction. First, the error generation mechanism of traditional SOC estimation methods under complex operating conditions is analyzed, and a general improvement strategy is proposed. Secondly, the capacity variation characteristics of energy storage batteries at different discharge rates are analyzed, and a quantitative model to characterize the relationship between discharge rate and capacity is established, providing a theoretical foundation for precise SOC estimation. Next, a hybrid estimation algorithm, CLA-EKF, is proposed by integrating deep neural networks with the Extended Kalman Filter (EKF). This approach leverages the advantages of both methods in handling complex nonlinear relationships and resisting disturbances, and further develops an SOC estimation method with adaptive capacity correction based on discharge rate. The experimental results demonstrate that the proposed capacity-correction-based CLA-EKF method significantly improves the accuracy of SOC estimation under various variable power conditions. This method proves superior to conventional methods, and provides an effective solution for SOC estimation in grid energy storage systems with high practical application value.

Key words: Energy storage battery, SOC estimation, Discharge rate, Kalman filter method, Neural network

中图分类号:

TM 912

贺悝, 冷肇星, 谭庄熙, 李雪源, 吴晓文, 陈超洋. 考虑放电倍率的电池储能容量自适应SOC估计方法[J]. 储能科学与技术, doi: 10.19799/j.cnki.2095-4239.2025.0080.

Li HE, Zhaoxing LENG, Zhuangxi TAN, Xueyuan LI, Xiaowen WU, Chaoyang CHEN. State-of-charge estimation of energy storage batteries based on adaptive capacity considering discharge rate[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2025.0080.

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放电倍率	容量
0.5C	127.91
0.75C	127.25
1C	126.69
1.5C	125.49
2C	123.51

电池参数	数值
额定容量	125Ah
充电截止电压	3.65V
放电截止电压	2.5V

方法	RMSE	MAE	ME
EKF	2.9937	2.8248	4.5807
考虑容量修正的EKF	1.7728	1.5576	3.0820

方法	RMSE	MAE	ME
EKF	2.9937	2.8248	4.5807
CLA	0.7433	0.5523	3.9093
LSTM-EKF	1.1992	1.0373	3.8048
CLA-EKF	0.4498	0.3859	1.0796

方法	RMSE	MAE	ME
考虑容量修正的EKF法	1.7728	1.5576	3.0820
CLA-EKF算法	0.4498	0.3859	1.0796
本文方法	0.2769	0.1722	0.8908