Neural Adaptive MPC With Online Metaheuristic Tuning for Power Management in Fuel Cell Hybrid Electric Vehicles

In this paper, we present an advanced control framework for power management applications, named Neural Adaptive Model Predictive Control (NA-MPC), designed to provide an optimal power allocation among multiple energy sources, perform a multi-objective online adaptation of the optimal control policy, and ensure a fast real-time execution with low computational demand. NA-MPC augments general MPC problems with three key features: 1) an online metaheuristic tuning strategy adapts the MPC cost function weights, to attain multiple concurrent control objectives at once; 2) through neural emulation, the MPC control policy is replaced by an equivalent neural MPC controller, exhibiting universal approximation guarantees and ensuring real-time feasibility; 3) a neural black-box MPC prediction model is employed, identified only via noise-corrupted input-output measurements from the plant, which is assumed to be unknown. The general formulation and versatility of NA-MPC make it potentially applicable to several power management scenarios; in this work, we apply NA-MPC to the case study of power management in fuel cell hybrid electric vehicles (FCHEVs), a topic of growing interest within the frame of sustainable transportation, for which novel and efficient strategies are still lacking. The effectiveness of NA-MPC is thoroughly assessed via numerical simulations, demonstrating its capability to optimally attain multiple control objectives concurrently in real time; moreover, NA-MPC consistently outperforms the most prominent state-of-the-art HEV power management strategies. Note to Practitioners—The aim of this paper is to introduce an advanced online-adaptive optimal control strategy, named NA-MPC, and employ it as a novel power management strategy for FCHEVs, with the purpose of addressing several technical shortcomings of the existing state-of-the-art strategies. Specifically, the latter typically fail in performing effective trade-offs between accurate power tracking and supply consumption, proving a merely suboptimal control action. Such strategies have also very limited adaptation capabilities, being either offline-tuned or employing simple non-optimal adaptation policies. Moreover, only few basic optimal control strategies are proposed in the literature, with little focus on their real-time feasibility. By contrast, our NA-MPC strategy provides an optimal power allocation, effectively attains multiple concurrent control objectives, and, thanks to its neural embedding, is real-time feasible and easily implementable on hardware with limited computational resources. Furthermore, the general formulation and versatility of NA-MPC enable its potential application across a wide variety of different power management scenarios.