A universal optimal sizing for hybrid energy storage system of electric vehicles

Energy storage systems in electric vehicles (EVs) are made up of several cells or modules that are connected in series or parallel. The configuration of such cells/modules has a significant impact on the performance of the EV. This paper proposes a universal double-layer optimal sizing framework for all configurations of the battery/supercapacitor hybrid energy storage system (HESS). For the outer layer, the Non-dominated Sorting Genetic Algorithm (NSGA-II), which is a well-recognized approach for multi-objective optimization of complex models, is used to determine HESS designs that optimize a set of techno-economic criteria. In the inner layer, the alternative Pontryagin's Minimum Principle (alt-PMP) is exploited to manage the power flow between the battery and the supercapacitor. The sizing result of each HESS topology forms a Pareto front, unveiling the trade-offs between the objective functions. The non-dominated (Pareto optimal) solutions obtained by the proposed framework demonstrate superior performance when compared to the rule-based and optimal singlelayer sizing approaches with the same energy management strategy. Finally, the operation of optimal designs in real-time is validated by hardware-in-the-loop (HIL) simulation. The proposed sizing framework is effective, reliable, and can be extended to more complex multi-source systems in future work.