This paper proposes a learning-based predictive control technique for self-driving hybrid electric vehicle (HEV). This approach is a hierarchical framework. The higher-level is a human-like driver model, which is applied to predict accelerations in the car following situation to replicate a human driver’s demonstrations. The lower-level is a reinforcement learning (RL)-based controller, which enforces the battery and fuel consumption constraints to improve energy efficiency of HEV. In addition, we present induced matrix norm (IMN) to handle cases that the training data cannot provide sufficient information on how to operate in current driving situation. Simulation results illustrate that the proposed method can reproduce human driver’s driving style and promote fuel economy.
Reinforcement Learning-Based Predictive Control for Autonomous Electrified Vehicles
2018-06-01
1902881 byte
Conference paper
Electronic Resource
English
REINFORCEMENT LEARNING-BASED PREDICTIVE CONTROL FOR AUTONOMOUS ELECTRIFIED VEHICLES
British Library Conference Proceedings | 2018
|Magnetically Actuated Hybrid Brake for Autonomous, Electrified Vehicles
DataCite | 2023
|