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| Joint Estimation of SOC and Usable Capacity of Lithium-Ion Battery with Wide Temperature and Full Life Based on Migration Model |
| Shen Jiangwei1, Gao Chengzhi1, Shu Xing1, Liu Yonggang2, Chen Zheng1 |
1. Faculty of Transportation Engineering Kunming University of Science and Technology Kunming 650000 China;
2. College of Mechanical Engineering Chongqing University Chongqing 400030 China |
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Abstract At present, the traditional equivalent circuit model is widely used in the research of joint estimation of state of charge (SOC) and available capacity. However, this method seriously depends on the model accuracy, and is affected by temperature changes and battery aging, which leads to the decline of estimation accuracy of SOC and available capacity in the whole life cycle and under complex temperature environment. To solve the above problems, this paper integrates the weight selection particle filter algorithm on the basis of the migration model theory, and studies the estimation of SOC and usable capacity in the wide temperature and full life range of lithium-ion batteries. The proposed method can greatly reduce the amount of data required for modeling, and the estimated SOC value has the advantages of high accuracy and strong ability to correct the initial value error.
First of all, this paper selects the traditional second-order RC equivalent circuit model as the basic model for building the migration model, the recursive least square method with forgetting factor is employed to identify the model parameters, and further uses polynomials to fit the correlation between the model parameters and SOC to complete the construction of the migration model. Secondly, the weight selection particle filter algorithm is used to realize the online migration of migration factors and complete the online determination of the real parameter information of the model. Further, the inaccurate SOC value can be migrated to obtain the real SOC value under the influence of temperature and aging. Finally, the available capacity estimation is realized by capacity inversion based on the obtained SOC estimation value.
In order to verify the estimation accuracy of the migration model for the battery SOC in a wide temperature environment, UDDS cycle condition experiments were conducted at -10℃, 10℃, 30℃ and 50℃ respectively. In addition, the initial value of SOC in the algorithm was set to 75%, while the actual initial value of the battery SOC was 100%, to verify the ability to correct the initial error. The results show that under the wide temperature environment of -10℃, 10℃, 30℃ and 50℃, the root mean square error (RMSE) of SOC estimation obtained by the migration model is 2.22%, 2.07, 2.29% and 1.83% respectively, and the time taken to converge to the true value within the 2% error band is 550s, 700s, 750s and 600s respectively, which shows that the migration model has a high estimation accuracy and a good correction ability for the initial value error in estimating SOC in a wide temperature range. At the same time, in order to verify the estimation accuracy of the battery SOC of the migration model under different aging conditions during the whole life cycle, it was verified based on the data of UDDS operating conditions in the initial aging stage (SOH=96.7%), moderate aging (SOH=93.1%) and late aging stage (SOH=85.1%) of the lithium-ion battery. The results show that under three different aging conditions, the RMSE of SOC estimation results based on migration model is 1.66%, 1.84% and 1.99%, respectively, indicating that the proposed migration model based SOC estimation method has high accuracy in the whole life range. Finally, this paper uses the aging experimental data of the selected battery to verify the battery capacity estimation results at different aging stages every 50 cycles. The capacity estimation results show that the maximum error between the real capacity and the model estimated capacity at different aging stages in the battery life cycle is 0.047A·h, less than 2%. It shows that the capacity estimation method proposed in this paper has high estimation accuracy.
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Received: 17 February 2022
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2023, Vol. 38 
