An application of evolutionary computation algorithm in multidisciplinary design optimization of battery packs for electric vehicle. Issue 3 (30th April 2020)

• Record Type: Journal Article
• Authors: Cui, Xujian; Panda, Biranchi; Chin, Christina May May; Sakundarini, Novita; Wang, Chin‐Tsan; Pareek, Kapil
• Other Names: Gao Liang guestEditor.; Garg Akhil guestEditor.
• Is Part Of: Energy storage. Volume 2:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-30
• Subjects: battery capacity -- battery pack enclosure -- electrical vehicle (EV) -- evolutionary computation (EC) -- genetic programming (GP); Energy storage -- Periodicals; Energy storage; Periodicals; 621.04205
• DOI: 10.1002/est2.158
• Languages: English
• ISSNs: 2578-4862

Abstract

Abstract: The increased concerns of climate change are driving a shift in the transport sector from fossil to green fuels and electric vehicles (EVs) systems capable of delivering long‐term sustainability. However, there are some challenges remaining related to batteries used in EVs. These challenges exist in four different levels, that is, cell, module, pack, and EV level which can be easily solved by evolutionary computation (EC) techniques rather than using conventional modeling or optimization methods. In this study, a framework, using EC is proposed to solve the problems of EV applications. Firstly, a multi‐gene genetic programming is proposed to analyze the data in cell level and formulate a model for lithium‐ion battery capacity. The second case study targets optimization of battery encloser design at pack level using non‐dominated sorting genetic algorithm (NSGA‐II). Validation results manifest the outstanding optimization function of NSGA‐II and improved performance of the enclosure. Moreover, the results show improved performance of NSGA‐II when combined with other artificial intelligence algorithms. The results suggest that EC can be integrated in the EV system for monitoring its performance and ensure its safety.