Proceedings of the
9th International Conference of Asian Society for Precision Engineering and Nanotechnology (ASPEN2022)
15 – 18 November 2022, Singapore
doi:10.3850/978-981-18-6021-8_OR-12-0063
A Method to Predict the Lithium-ion Battery Internal Temperature
Department of Mechanical Engineering, Yeungnam University, Republic of Korea
ABSTRACT
The estimation accuracy of lithium-ion battery internal temperature is not high, which may cause thermal runaway fire and other safety problems. In this work, Electrochemical impedance spectrum (EIS) is used to study the variation law of battery temperature. A new battery internal temperature estimation method independent of SOC and SOH is proposed. The law of impedance variation with temperature is studied. The internal relationship between the temperature and the imaginary part of the characteristic quantity is found under different temperatures. The temperature prediction model of lithium-ion batteries is established, and experiments verify its accuracy. The results show that the battery's internal temperature can be accurately predicted even when the SOC and SOH of the battery are unknown. This prediction method has great application prospects in the real-time monitoring of the lithium-ion batteries internal temperature.
Keywords: Lithium-ion battery, Electrochemical impedance spectrum, Internal temperature, Prediction
Department of Mechanical Engineering, Yeungnam University, Republic of Korea
ABSTRACT
The estimation accuracy of lithium-ion battery internal temperature is not high, which may cause thermal runaway fire and other safety problems. In this work, Electrochemical impedance spectrum (EIS) is used to study the variation law of battery temperature. A new battery internal temperature estimation method independent of SOC and SOH is proposed. The law of impedance variation with temperature is studied. The internal relationship between the temperature and the imaginary part of the characteristic quantity is found under different temperatures. The temperature prediction model of lithium-ion batteries is established, and experiments verify its accuracy. The results show that the battery's internal temperature can be accurately predicted even when the SOC and SOH of the battery are unknown. This prediction method has great application prospects in the real-time monitoring of the lithium-ion batteries internal temperature.
Keywords: Lithium-ion battery, Electrochemical impedance spectrum, Internal temperature, Prediction
