On Using Kriging Response Surface Method for EV Battery Pack Structural Response Prediction and Mass Optimization

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Published May 13, 2022
https://doi.org/10.37285/ajmt.1.2.8
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Vol. 2 No. 2 (2022): April-June 2022

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Deepak Sreedhar Kanakandath
Battery Durability & Fatigue, Tata Consultancy Services, Creator ITPL, Bangalore, Karnataka, India.
Sankha Subhra Jana
Battery Durability & Fatigue, Tata Consultancy Services, Creator ITPL, Bangalore, Karnataka, India.
Arunkumar Ramakrishnan
Battery Durability & Fatigue, Tata Consultancy Services, Creator ITPL, Bangalore, Karnataka, India.

Abstract

Structural response of battery packs in electric vehicles when subjected to road loads is an important factor that decides its performance and life during normal operation. In this paper a kriging response surface model is built using a Design of Experiment (DOE) run dataset to predict structural response and global modal frequency metrics of the battery pack. Using this Response Surface Model (RSM), we can rapidly optimize the battery pack design with respect to structural response and achieve significant mass reduction. This method reduces turnaround times for design optimization in early stages of battery pack design.


Keywords: Battery pack, Random vibration response, Kriging method, MDO, Optimization, RESS, Electric Vehicle, Battery pack, Multi-disciplinary optimisation, Mass optimisation, Response surface model, Automotive, CAE, Finite element method

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How to Cite
Deepak Sreedhar Kanakandath, Sankha Subhra Jana, & Arunkumar Ramakrishnan. (2022). On Using Kriging Response Surface Method for EV Battery Pack Structural Response Prediction and Mass Optimization. ARAI Journal of Mobility Technology, 2(2), 220–227. https://doi.org/10.37285/ajmt.1.2.8
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