Lithium-ion Battery Pack Design for Electric Vehicles Using GT-AutoLion: Multi-Physics Simulation and Multi-Criteria Optimization Approach
Other conference contribution, 2021

High specific energy battery systems with improved thermal performance are required for large-scale introduction of electric vehicles (EVs) into the market. This study presents a comprehensive multi-physics simulation and multi-criteria optimization framework for Lithium-ion (Li-ion) battery pack design for EV applications. The battery cells are modeled by electrochemical thermally coupled approach using GT-AutoLion. Multi-objective optimization using genetic algorithm is employed to explore energy and thermally efficient cell design alternatives. The performances of the optimally designed cells are then evaluated under pack environment to account for inhomogeneities in large traction battery packs under realistic working scenarios. It is observed that considering the thermal efficiency of battery cells is crucial for obtaining improved battery pack performance. The integrated framework developed in this work provides systematic pack-aware guidelines for manufacturers already at the initial cell design stage. Moreover, the proposed design optimization methodology is generic, handing over valuable knowledge for future cell and pack designs for various applications.

Author

Majid Astaneh

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Jelena Andric

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Lennart Löfdahl

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Peter Stopp

Gamma Technologies

Global Gamma Technologies Virtual Conference

Global Gamma Technologies Virtual Conference
, ,

Interaction between micro and macro processes in battery-powered vehicles

Swedish Energy Agency (47906-1), 2019-04-10 -- 2021-04-15.

Subject Categories

Energy Engineering

Vehicle Engineering

Energy Systems

More information

Created

10/30/2021