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As battery temperature greatly affects performance, safety, and life of Li-ion batteries in plug-in and electric vehicles under various driving conditions, automakers and battery suppliers are paying increased attention to thermal management for Li-ion batteries in order to reduce the high temperature excursions that could decrease the life and reduce safety of Li-Ion batteries. Currently, the lack of fundamental understanding of the heat generation mechanism due to complex electrochemical phenomena prohibits accurate estimation of the heat generation within Li-ion cells under various operating conditions. Heat from Li-ion batteries can be generated from resistive dissipation, the entropy of the cell reaction, heat of mixing, and other side chemical reactions. Each of these can be a significant source of heat under a range of circumstances.

As one of many pack-level battery simulation approaches developed within the General Motors-led Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project, the system approach treats the entire battery pack as a dynamic system which includes multiple engineering disciplines for simulation. It is the most efficient approach of all the CAEBAT battery pack-level approaches in terms of computational time and resources. This paper reports the application of the system approach for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The results using the system approach are found to have a very good agreement with the measurements.

The Computer-Aided Engineering of Automotive Batteries (CAEBAT) Phase 1 project is a U.S. Department of Energy-funded, multi-year project which is aimed at developing a complete CAE tool set for the automotive battery pack design. This paper reports the application of the full field approach of the CAEBAT which is developed by the General Motors-led industry team, for a 24-cell liquid-cooled prototype battery pack. It also summarizes the verification of the approach by comparing the simulation results with the measurement data. The simulation results using the Full Field Approach are found to have a very good agreement with the measurement data.