Self-Learning Neural Controller for Hybrid Power Management Using Neuro-Dynamic Programming Univ. of Michigan

Format:

Conference/Event

Author/Creator:

Johri, Johri, author.

Contributor:

Filipi, Zoran

Conference Name:

10th International Conference on Engines & Vehicles (2011-09-11 : Naples, Italy)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2011

Summary:

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, id est an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the cost-to-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique

Notes:

Vendor supplied data

Publisher Number:

2011-24-0081

Access Restriction:

Restricted for use by site license