一种电动汽车电池包托底等效应力计算与预测方法
By combining finite element simulation and machine learning methods, a stress prediction model for the lower shell of the battery pack system was established, which solved the high cost problem of mechanical safety analysis under the condition of electric vehicle battery pack bottoming out, and realized efficient and low-cost battery pack system design and safety early warning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHONGQING UNIV
- Filing Date
- 2023-09-12
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for analyzing the mechanical safety of electric vehicle battery packs under bottoming conditions are time-consuming and labor-intensive, making it difficult to efficiently design the battery pack system. Furthermore, finite element analysis is costly and difficult to promote in the highly competitive automotive industry.
By combining finite element simulation and machine learning methods, a finite element model of the battery pack system is established. The impact stress at the bottom of the battery pack system is predicted by the machine learning model. The machine learning model is optimized by the whale optimization algorithm. The characteristics of the obstacle are simulated by the cone model, and a stress prediction model of the lower shell of the battery pack system is constructed.
It achieves high-precision, low-cost prediction of the bottom stress of battery pack systems, avoids complex finite element analysis, supports efficient design and safety early warning of battery pack systems, and can assess the safety of battery modules in real time.
Smart Images
Figure CN117216987B_ABST