Bi-Level Control Co-Design for Parallel Electric-Hydraulic Hybrid Vehicles

This study presents a control co-design method that utilizes a bi-level optimization framework for parallel electric-hydraulic hybrid powertrains, specifically targeting heavy-duty vehicles like class 8 semi-trailer trucks. The primary objective is to minimize battery energy consumption, particularly under high torque demand at low speed, thereby extending both battery lifespan and vehicle driving range. The proposed method formulates a bi-level optimization problem to ensure global optimality in hydraulic energy storage sizing and the development of a high-level energy management strategy. Two nested loops are used: the outer loop applies a Genetic Algorithm (GA) to optimize key design parameters such as accumulator volume and pre-charged pressure, while the inner loop leverages Dynamic Programming (DP) to optimize the energy control strategy in an open-loop format without predefined structural constraints. Both loops use a single objective function, i.e. battery energy consumption, to ensure a globally optimal offline solution. Key findings reveal that the Recurrent Neural Network (RNN)-based online energy controller replicates the offline DP solution with near-optimal performance, achieving real-time and closed-loop control with robustness. Furthermore, simulation results demonstrate significant savings in battery energy and overall system efficiency, highlighting the potential of this method for real-world applications.