基于IFO-GA-IUKF算法的锂电池多温度SOC估计

doi:10.19799/j.cnki.2095-4239.2024.0001

摘要/Abstract

摘要：

锂电池已经在新能源汽车中被广泛利用，准确的估计电池荷电状态（SOC）对于电动汽车锂电池的使用寿命与利用效率具有重要意义。现阶段锂电池面对温度变化时，存在SOC估计精度较差的问题。针对这个问题，本文提出改进的分数阶模型与全新的联合算法。首先，本文建立了改进的温度-容量分数阶模型（IFOM），针对改进分数阶模型，采用遗传算法（GA）进行参数辨识，并采用NEDC工况进行验证；其次，本文引入费罗贝尼乌斯范数，提出了非正定矩阵最优估计方法，解决了无迹卡尔曼滤波算法（UKF）协方差矩阵非正定问题，建立了改进无迹卡尔曼滤波算法（IUKF）；最后，基于IFOM与算法建立IFO-GA-IUKF联合算法，在0-40 ℃环境温度条件下，对NCR18650PF三元锂电池进行DST工况测试。实验结果表明，改进后的模型可实现多种温度下极高精度，相比未改进二阶分数阶模型（2FOM）与改进的二阶整数阶模型（I2IOM），0 ℃时，RMSE分别提升37%与12.8%。本文提出的IFO-GA-IUKF算法相比IFO-GA-UKF联合算法，精度在温域两端均呈现较高优势，尤其0 ℃低温环境，RMSE提高35%以上；相比IO-VFFRLS-PF、IO-PSO-PF联合估计算法，SOC估计精度在全温域均大幅领先。IFO-GA-IUKF联合算法可实现多种温度下的SOC估计，估计效果呈现极高精度与鲁棒性。

关键词: 荷电状态, 锂离子电池, 分数阶模型, 遗传算法, 改进无迹卡尔曼滤波

Abstract:

Accurate estimation of the state of charge (SOC) of a battery is important for the lifetime and efficiency of the battery. At the present stage, lithium batteries have poor SOC estimation accuracy In the event of a change in temperature, especially in low temperature environments.To address this problem, this paper proposes an improved fractional order model with a new joint algorithm. Firstly, an Improved Fractional order model (IFOM) for temperature-capacity is developed in this paper. For the fractional order model, genetic algorithm (GA) is used for parameter identification. Validation using NEDC working conditions. Then, to solve the problem the covariance matrix of the Unscented Kalman filter (UKF) algorithm appears to be non-positive definite. This article introduces the Frobenius norm. Optimal estimation of the state error covariance matrix. In this paper, Improved Unscented Kalman filter (IUKF) algorithm is established; Finally, a joint IFO-GA-IUKF algorithm is established.DST working condition test of NCR18650PF lithium battery under 0-40 ℃ ambient temperature condition. The experimental results were used to validate the algorithm. The results show that Improved model achieves very high accuracy overmultiple ambient temperatures. Compared to the unimproved second-order fractional-order model (2FOM) and the improved second-order integer-order model (I2IOM), the RMSE is improved by 37% versus 12.8% at 0 ℃, respectively. The IFO-GA-IUKF algorithm proposed in this paper compared to the joint IFO-GA-IUKF algorithm. Accuracy presents a high advantage at both ends of the temperature domain, especially in 0 ℃ low-temperature environments, where RMSE is improved by more than 35%; Compared with the IO-VFFRLS-PF and IO-PSO-PF joint estimation algorithms, the SOC estimation accuracy is significantly better in all temperature domains, and the IFO-GA-IUKF joint algorithm realizes SOC estimation in a wide temperature domain at multiple temperatures, and the estimation results show extremely high accuracy and robustness.

Key words: state of charge, lithium-battery, fractional order model, genetic algorithm, improved unscented Kalman filter

中图分类号:

TM 912

余传祥, 张英健, 潘傲然, 郭豪杰, 毛文鹏. 基于IFO-GA-IUKF算法的锂电池多温度SOC估计[J]. 储能科学与技术, doi: 10.19799/j.cnki.2095-4239.2024.0001.

Chuanxiang Yu, Yingjian Zhang, Aoran Pan, Haojie Guo, Wenpeng Mao. Research on state of charge estimation of power battery in wide temperature range[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2024.0001.

图/表 14

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表3.1

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温度(℃)	平均容量Q₀(Ah)	温度(℃)	平均容量Q₀(Ah)
3	2.53589	23	2.75972
13	2.67596	32	2.81164
17	2.70605	36	2.82426

T/℃	0	10	20	40
IFOM	0.017	0.01051	0.00843	0.008
2FOM	0.027	0.01873	0.01047	0.01276
I2IOM	0.0195	0.00925	0.00864	0.00895

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