Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management

TL;DR

This paper introduces three real-time energy management algorithms for hybrid hydraulic vehicles using model predictive control, incorporating driver behavior and route predictions to optimize energy use under constraints.

Contribution

The work develops general MPC-based methods for HHV energy management that adapt to driver behavior and route forecasts, including a steady state distribution planning mechanism.

Findings

Algorithms operate in real time on limited hardware.

Incorporating driver behavior improves energy efficiency.

Simulation results validate the effectiveness of the proposed methods.

Abstract

This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka model predictive control) framework, are rather general and can be applied to systems with nonlinear dynamics subject to a Markov disturbance. State and input constraints are considered in each method. A mechanism based on the steady state distribution of the underlying Markov chain is developed for planning beyond a finite horizon in the HHV energy management problem. Road elevation information is forecasted along the horizon and then merged with the statistical model of driver behavior to increase accuracy of the horizon optimization. The characteristics of each strategy are compared and the benefit of learning driver behavior is analyzed through simulation on three drive cycles, including one real world drive cycle. A simulation is designed to explicitly demonstrate the benefit of adapting the Markov chain to real time driver behavior. Experimental results demonstrate the real time potential of the primary algorithm when implemented on a processor with limited computational resources.