Machine Learning Enabled Design Automation and Multi-Objective Optimization for Electric Transportation Power Systems

Abstract

This article presents an automated design and optimization framework for electric transportation power systems (ETPS) enabled by machine learning (ML). The use of physical models, simulations, and optimization methods can greatly aid the engineering design process. However, when considering the optimal co-design of multiple interdependent subsystems that span multiple physical domains, such model-based simulations can be computationally expensive, and traditional metaheuristic optimization methods can be unreliable. Bayesian optimization (BO), an ML framework, paves one feasible pathway to realize an efficient design process practically. However, current state-of-the-art BO algorithms are non-compatible or perform poorly when applied to system-level ETPS design with multiple objectives and constraints. This article proposes a novel BO algorithm referred to as max-value entropy search for multiobjective optimization with constraints (MESMOC) to solve multiobjective optimization (MOO) problems with black-box constraints that can only be evaluated through design simulations. After a full presentation of the algorithm, MESMOC is applied to a realistic ETPS design case using a heavy-duty electric vertical-takeoff-landing (eVTOL) urban aerial vehicle (UAV) power system. Two MOO experimental trials show a drastic reduction in the number of design simulations to discover a high-quality Pareto front. In Trial 1, MESMOC uncovered the entire Pareto front while only requiring to explore ~4% of the design space. With expanded design parameters and larger design space in Trial 2, a near complete but high-quality Pareto front was uncovered. Both trials compared MESMOC to the popular genetic algorithm NSGA-II and another BO algorithm predictive entropy search for multi-objective Bayesian optimization with constraints (PESMOC), showing superior performance.