Fauzan Djamaluddin, Moch. Agus Choiron, Tommi Hariyadi
This study presents a comprehensive multi-objective optimization of a multi-cell crash box under quasi-static and dynamic axial loading conditions. Finite element analysis is conducted to investigate the crashworthiness indicators: total energy absorption (TEA), specific energy absorption (SEA), and peak crushing force (PCF). Extreme Gradient Boosting (XGBoost) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were developed to maximize SEA and minimize PCF. The results demonstrate that the optimized multi-cell configurations achieve superior energy absorption capacity. The proposed framework provides a computationally efficient approach for the crashworthiness design of crash boxes for electric vehicle applications. © 2026 Taylor & Francis Group, LLC.
Department of Mechanical Engineering, Hasanuddin University, Makassar, Indonesia; Applied Mechanics and Mechanical Design Research Group, Hasanuddin University, Makassar, Indonesia; Department of Mechanical Engineering, Brawijaya University, Malang, Indonesia; Department of Electrical Engineering, Universitas Pendidikan Indonesia, Bandung, Indonesia
