Dimensioning and configuration of EES systems for electric vehicles with boundary-conditioned adaptive scalarization

Abstract

Electric vehicles (EVs) are widely considered as a solution for efficient, sustainable and intelligent transportation. An electrical energy storage (EES) system is the most important component in an EV in terms of performances and cost. This work proposes an approach for optimal dimensioning and configuration of EES systems in EVs. It is challenging to find optimal design points in the parameter space, which expands exponentially with the number of battery types available and the number of cells that can be implemented for each type. A multi-objective optimization problem is formulated with the driving range, rated power output, installation space and cost as design targets. We report a novel boundary-conditioned adaptive scalarization technique to solve both convex and concave problems. It provides a Pareto surface of evenly distributed Pareto points, presents the group of Pareto points according to different specific requirements from automotive manufacturers and also takes the fact in EES system design into account that the importance of an objective could be nonlinear to its value. Numerical and practical experiments prove that our proposed approach is effective for industry use and produces optimal solutions.