Intelligent Hybrid Vehicle Power Control. Part 2. Online Intelligent Energy Management.

摘要

This is the second paper in a series of two that describe our research in intelligent energy management in a hybrid electric vehicle (HEV). Energy management in Hybrid Electric Vehicles (HEV) has been actively studied recently because of its potential to significantly improve fuel economy and emission control. Because of the dual-power-source nature and the complex configuration and operation modes in a HEV, energy management is more complicated and important than in a conventional vehicle. Most of the existing vehicle energy optimization approaches do not incorporate knowledge about driving patterns into their vehicle energy management strategies. Our approach is to use machine learning technology combined with roadway type and traffic congestion level specific optimization to achieve quasi-optimal energy management in hybrid vehicles. In this series of two papers, we present a machine learning framework that combines Dynamic Programming with neural networks to learn about roadway type and traffic congestion level specific energy optimization, and an integrated online intelligent energy controller to achieve quasi-optimal power management in hybrid vehicles. In the first paper we presented a machine learning framework, ML-EMO-HEV, developed for learning the knowledge about energy optimization in an HEV. The framework consists of machine learning algorithms for predicting driving environments and for generating optimal power split of the HEV system for a given driving environment. Experiments are conducted to evaluate these algorithms using a simulated Ford Escape Hybrid vehicle model provided in PSAT (Powertrain Systems Analysis Toolkit). In this second paper, we present three online intelligent energy controllers, IEC-HEV-SISE, IEC-HEV-MISE, and IEC-HEV-MIME.