MPC-Informed Dynamic Screening for the Co-Design of Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles

Hardware sizing and energy management for hybrid energy storage systems are usually designed sequentially, hiding the interactions between them. This paper proposes an MPC-informed dynamic screening framework in which every candidate configuration is simulated under one model predictive control law over a complete driving cycle, so that operational behaviour, not static metrics, determines selection. A fully documented post-evaluation criterion aggregates tracking, battery electrical stress, soft constraint violations and design overhead into one score normalised against an exact baseline anchor. Because one evaluation costs about 60 ms, the complete exact Pareto front of an electric transit bus case study is screened, not a sample. The static design cost proves almost uninformative regarding dynamic performance: the rank correlation between the two orderings is statistically indistinguishable from zero, the sets that they rank highest share no member, and the statically cheapest design falls far down the dynamic ranking, ending below the baseline. The cause is structural opposition on the pack voltage, which improves the dynamic performance but raises the static cost. The framework returns a leading design family that improves on the baseline overall, quantifies the battery stress that its leaner supercapacitor incurs, and shows the verdict to be robust to controller tuning but dependent on the duty and control strategy.