Optimization strategy for coupled battery system design models using Gaussian Process Regression and Classification

In the development of battery systems for electric vehicles (EV), numerous components from different physical subareas must be harmonized with each other. Automotive engineers already use extensive simulation models to optimize individual components satisfying increasing demands of EV range and power. Yet, strategies for the combined optimization of battery systems have not been addressed in the literature. This work presents an optimization strategy for the holistic design of battery systems, which utilizes coupled simulations of technical submodels representing cellmodules, mechanics, cooling, and electronics. Given user-specified battery system requirements, methods of Gaussian Process Regression and Classification are combined to determine the optimal battery system design in terms of costs and feasibility. An inherited mixed-integer problem is addressed by using discretization of the solution space and refinement strategies in likely optimal regions. Moreover, the information gain per iteration is maximized by means of predictive calculations and parallelization methods. Testing the presented optimization strategy in different scenarios gives promising results, showcasing its robustness towards different technical requirements for battery systems. Also, exemplary analyses regarding the impact of the total installation space on costs and feasibility are conducted.

Automated summary

This study proposes an optimization strategy for the holistic design of battery systems by utilizing coupled simulations and combining Gaussian Process Regression and Classification methods. The results show promising outcomes in terms of meeting different technical requirements.