Identification of temperature-dependent gas production rates in pouch lithium-ion cells

Conventional lithium-ion cell state models assume a constant gas production rate for each chemical reaction, reducing the prediction accuracy of thermal and mechanical states. The heat and gas evolution in cells involves a strongly-nonlinear dynamic process, and existing studies lack precise identification methods for dynamic gas production rates. This study proposes an identification approach for temperature-dependent gas production rates of pouch lithium-ion cells. A simple and low-cost experiment for the inverse calculation is designed to capture the temperature and height data of cells under thermal abuse conditions. A gas production analytical model is created, coupling chemical reaction, thermal circuit, and thermodynamic sub-models. An inverse model is formulated to identify the temperature-dependent gas production rate of each reaction. An equal-volume-change sequential searching algorithm is presented to suppress numerical oscillations caused by minor and abrupt responses. Numerical and experimental results from two commercial lithium-ion cells validate the performance of the proposed approach. The results show that this approach identifies the nonlinear relationship between gas production rates and core temperature. The second-level efficiency and robust convergence support this approach as an effective tool for accurately identifying gas production rates in cell state evaluation models. Identifying dynamic gas production rates can significantly enhance the precision of state prediction for lithium-ion batteries, which is crucial for ensuring the safety of battery packs during service.