Design Optimization of Battery Holder for Electric Vehicle

被引:0
|
作者
Bao, Nengsheng [1 ]
Zhao, Rongjiang [1 ]
机构
[1] Shantou Univ, Coll Engn, Shantou, Peoples R China
关键词
Battery holder; Mechanical property; Design and Optimization; RSM; LHS and ANN; LITHIUM-ION BATTERIES; HYBRID; STATE; MANAGEMENT; FRAMEWORK; PROGRESS; MODELS; HEALTH; PACK;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Lithium-ion battery pack composed of battery modules provide power for the electric vehicles. To ensure that lithium-ion batteries in battery modules are protected carefully, a battery holder was designed to provide support and protection for each lithium-ion battery. When the vehicles are at working, the battery pack will be affected by harsh environments, such as shocks due to road bumps and changes due to the road surface conditions. This will cause stress and deformation in the battery pack. The safety of vehicle largely lies on the stabilization of the battery pack, and the stabilization of the battery pack rests on the mechanical performance of the battery holder, for instance the capability to resist deformation and defy vibration. Furthermore, the lightweight of electric vehicles is still the primary method for saving energy, because lightweight can increase the service life of the battery pack while it can also increase the mileage of the vehicle. In this study, a composite design optimization method was proposed to optimize the mechanical properties of the battery holder (minimization of mass, minimization of the maximum deformation, and maximization of minimum natural frequency). The proposed method includes four steps. In the first step, the finite element model under load was obtained based on the static analysis. The natural frequency and the geometric volume of the battery holder were obtained Based on the modal analysis. In the second step, the method of Latin Hypercube Sampling was used to obtain the calculation points to design the experiment, and the Response Surface Method was used for developing an empirical model for the three mechanical characteristics. In the third step, the highest deformation, natural frequency and volume of the battery holder were further optimized based on Latin hypercube sampling and artificial neural network through the non-dominated sorting genetic algorithm. In the last step, the optimal inputs calculated from NSGA II was used to manufacture battery holder. Conclusions and research proposals are summarized to conduct the distant work.
引用
收藏
页码:79 / 84
页数:6
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