Optimizing Battery Design for Fast Charge through a Genetic Algorithm Based Multi-objective Optimization framework

Batteries have higher power density than before, and electric vehicles (EVs) can travel longer distance per charge which can be comparable to conventional vehicles. However, it still takes too long to charge EVs. Fast charging and supercharging are ones of the available solutions. However, they could potentially damage battery and accelerate battery degradation. It is critical to reduce the charging time without compromising battery's long-term performance. We aim to change charging strategies (e.g., plus charging) and design variables (e.g., porosity, particle size, and electrodes' thicknesses) to reduce battery charging time as well as mitigating its degradation. We utilized our previously developed physics-based side-reaction coupled battery model and computational optimization framework based on the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) to find the optimal design variables and plus charging strategies to reduce battery charging time and mitigate battery degradation. Through optimization, our proposed nonlinear pulse charging only takes 0.35 hour to fully charge the battery which has a reduction of charging time of 98% compared to CCCV.