A Deep-Learning-Based Approach to Eco-Driving-Based Energy Management of Hybrid Electric Vehicles
Seyedeh Mahsa Sotoudeh, Baisravan HomChaudhuri
Abstract
This article proposes a deep-learning-based hierarchical control framework for eco-driving-based energy management of connected and automated hybrid electric vehicles (HEVs). The article focuses on a computationally efficient solution for jointly optimizing the HEV’s driving cycle (velocity profile) and its powertrain’s power split (powertrain energy management). The proposed framework harnesses the benefits of long- and short-term decision-making and is highly computationally efficient because it learns the control law directly with a deep neural network (DNN). At the high level, pseudospectral optimal controller (PSOC) solves the powertrain’s energy management problem over driving cycle previews of the entire trip, approximated via data from vehicle-to-infrastructure (V2I) communications. At the low level, a DNN-based model predictive controller uses the high-level’s solution and jointly optimizes the driving cycle and powertrain’s energy management over short horizons. To satisfy the constraints, a quadratic programming (QP)-based control law modification algorithm has been developed in this article. Eco-driving-based energy management results of our framework, evaluated over standard, urban, and highway driving cycles, indicate its efficacy in generating real-time applicable energy-efficient solutions for unseen driving cycles of the same traffic pattern as the DNN’s training data.