Abstract
The crash analysis of complete electric vehicles demands high accuracy, speed, and modeling flexibility of the crash finite element analysis of single battery and battery packs. Herein, the crash analysis process is optimized using an artificial neural network (ANN) and a genetic algorithm (GA), and according to experimental conditions, working characteristic parameters of a single 18650 lithium‐ion battery, such as state of charge value and discharge mode are examined. It is combined with the beam elements of a simplified model of a five‐layer single 18650 battery, and the mechanical characteristic parameters are identified. The process comprises two parts: prediction of the mechanical properties of the battery cell from the operating characteristics of the single 18650 battery and the rapid solution of the simplified mechanical parameters of the beam elements from the mechanical properties of the battery. The accuracy of the prediction results of the ANN model reaches over 97%, and the fit between the simulation results of the GA identification parameters and the experimental results reaches over 95%. The identification parameters can make quick responses to the experimental results under different working conditions, which ground the application of the simplified beam element model in the battery packs.
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