Engineering Design of Battery Module for EVs: Comprehensive Framework Development Based on DFT, Topology Optimization, Machine Learning, Multidisciplinary Design Optimization and Digital Twins

Author:

Ghosh Nitika1,Garg Akhil2,Li Wei3,Gao Liang4,Nguyen-Thoi T.5

Affiliation:

1. Huazhong University of Science and Technology Luoyu road Wuhan, Hubei 43004 China

2. Huazhong University of Science and Technology Wuhan, 437004 China

3. No.174 Shazhengjie, Shapingba Chongqing, Chongqing 400044 China

4. Luo Yu Road 1037 Hongshan District Wuhan, Hubei, Select State/Province 430074 China

5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Abstract

Abstract Battery technology has been a hot spot for many researchers lately. Electrochemical researchers have been focusing on the synthesis and design of battery materials; researchers in the field of electronics have been studying the simulation and design of battery management system (BMS); whereas mechanical engineers have been dealing with structural safety and thermal management strategies for batteries. However, overcoming battery limitation in only one or two domains will not design an efficient battery pack as it requires an integrated framework. So far, there are few research studies that circumscribed all the multi-disciplinary aspects (cell material selection, cell-electrode design, cell clustering, state of health (SOH) estimation, thermal management, cell monitoring and recycling) simultaneously for battery packs in electric vehicles (EVs). This paper presents a holistic engineering design and simulation strategy for a future advanced battery pack and its parts by assimilating paradigmatic solutions for cell material selection, component design, cell clustering, thermal management, battery monitoring and recycling aspects of the battery and its components. The developed framework has been proposed based on DFT based cell material selection, topology design based cell-electrode design, machine learning (ML) based SOH estimation along with multi-disciplinary design optimization based liquid cooling system. The proposed framework also highlights the optimal configuration of cells using ML algorithms and multi-objective optimization of cell-assembly parameters. The role of digital twins for real-time and faster acquisition of data has been highlighted for the advanced and futuristic battery pack designs. Furthermore, preliminary investigation of robot assisted disassembly and recycling of battery packs has been summarized. Each proposed methodology has been discussed in detail, along with the advantages and limitations. Critical research orientations are also discussed in the end.

Publisher

ASME International

Subject

Mechanical Engineering,Mechanics of Materials,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

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