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| State Difference Identification Method of Parallel Battery Module Based on Electrochemical Impedance Spectroscopy |
| Dong Ming1, Luo Yang1, Lei Wanjun1, Ren Ming1, Guo Anxiang2 |
1. State Key Laboratory of Electrical Insulation and Power Equipment Xi' an Jiaotong University Xi' an 710048 China; ;
2. State Grid Shaanxi Electric Power Co. Ltd Electric power Research Institute Xi' an 710005 China |
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Abstract Single lithium is used in the form of a battery module in various series and parallel systems after consistency screening to meet high-voltage and significant capacity application demands of power or energy storage systems. Still, during the working process, the battery's solid electrolyte interface (SEI) continuously forms and thickens, contributing to the battery's aging. The existing battery management technology is challenging to identify the module charging and discharging promptly, which can cause thermal runaway accidents, undoubtedly endangering the safe and stable operation of large-scale energy storage. This paper employs a state difference identification method for the parallel battery module based on electrochemical impedance spectroscopy technology.
Firstly, this paper aims to design a 0% to 18% state of health difference in the parallel battery modules. The electrochemical impedance spectroscopy of the battery modules is tested. The impedance characteristics of the parallel battery modules are analyzed under various states of charge and state of health. Secondly, Randles' equivalent circuit models are built. Model parameters are fitted based on EIS test results, and relaxation time analysis confirms the accuracy of the model parameters. The suitable equivalent circuit parameters are extracted. Further, Spearman's correlation analysis examines the correlation between the parameters and SOC and SOH. The parameters of strong correlation with SOH and weak correlation with SOC are identified, determining the characteristic parameters for distinguishing the state differences of the parallel battery module. Finally, a support vector machine training model is developed to distinguish between battery states.
Measuring experiments were conducted using an electrochemical impedance spectroscopy testing system and a battery charging and discharging cycle system. The batteries were placed in a thermostat to ensure constant temperature conditions. An 18650 ternary lithium-ion battery was selected due to its high energy density and endurance. The samples were pretreated to achieve the best overall performance, using a 0.5C constant current and constant voltage to charge and a 0.2C constant current to discharge five times. The samples were placed in the thermostat for 5 minutes to stabilize the temperature. Then, the samples were measured, and data were collected from 100%SOC to 10%SOC.
The conclusions of this study are as follows. (1) The difference in the module can make the real impedance of the EIS in the 0.01~1 Hz band and the imaginary impedance in the 1~100 Hz band show a significant difference. (2) The simulation effect of the Randles equivalent circuit is good, in which Rs, Rp1, Rp2, and the guide Y1 have high correlation with SOH, which can be used as feature parameters. (3) The support vector machine identification model can effectively identify the state difference of the parallel battery module, and the accuracy reaches 94.12%. The EIS test of the battery module can reflect the state of the internal single battery. This paper develops a battery equivalent circuit model, calculates and determines the characteristic parameters, and constructs an identification model, providing a theoretical basis and technical route for detecting battery states in large-scale battery modules after work extension.
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Received: 17 October 2024
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2025, Vol. 40 
