Design optimization of battery pack enclosure for electric vehicle

Lithium-ion Battery pack which is comprised of assembly of battery modules is the main source of power transmission for electric vehicles. During the actual operation of electric vehicle, the battery packs and its enclosure is subjected to harsh environmental conditions such as the external vibrations and shocks due to varying road slopes. This will result in stresses and deformations of different degrees. The vehicle safety heavily depends on on the safety of battery pack which in turn is dependent on its mechanical features, such as the ability to resist deformation and vibration shocks. In addition, lighter weight vehicle is preferred because it can increase the range of vehicle and the life cycle of a battery pack. In this study, a design optimization methodology is proposed to optimize the features of mechanical design (e.g. minimization of mass, maximization of minimum natural frequency and minimization of maximum deformation) of the battery pack enclosure. The proposed methodology is comprised of four phases. In the first phase, finite element models for maximum deformation (based on static analysis), minimum natural frequency (based on modal analysis) and the mass are developed by using the combination of four methods (i.e. central composite design (CCD) and response surface methodology (RSM), CCD and artificial neural network (ANN), Latin hypercube sampling (LHS) and RSM, LHS and ANN). In the second phase, the best combination of methodology (CCD and ANN) is then selected for experimental design and the empirical models are formulated for three features of mechanical design. In the third phase, the models based on CCD and ANN for the maximum deformation, minimum natural frequency and mass are further optimized by using non-dominated sorted genetic algorithm (NSGA II). In the fourth phase, the optimum combination of inputs obtained by using NSGA II is used for the manufacturing of battery pack enclosure. Conclusions are made and research recommendations are proposed for the future work.