  This study presents a method for deep neural network nonlinear model
predictive control (DNN-MPC) to reduce computational complexity, and we show
its practical utility through its application in optimizing the energy
management of hybrid electric vehicles (HEVs). For optimal power management of
HEVs, we first design the online NMPC to collect the data set, and the deep
neural network is trained to approximate the NMPC solutions. We assess the
effectiveness of our approach by conducting comparative simulations with rule
and online NMPC-based power management strategies for HEV, evaluating both fuel
consumption and computational complexity. Lastly, we verify the real-time
feasibility of our approach through process-in-the-loop (PIL) testing. The test
results demonstrate that the proposed method closely approximates the NMPC
performance while substantially reducing the computational burden.
