Research on state of charge estimation of power battery in wide temperature range

doi:10.19799/j.cnki.2095-4239.2024.0001

Abstract

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

CLC Number:

TM 912

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.

Figures/Tables 14

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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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