A Stochastic Dynamic Principle for Hybrid Systems with Execution Delay and Decision Lags

TL;DR

This paper develops a stochastic dynamic programming algorithm for hybrid energy management in REEVs, incorporating physical constraints like delays and lags, and demonstrates its effectiveness through numerical comparisons.

Contribution

It introduces a novel SDP algorithm that accounts for execution delays and decision lags in hybrid systems, specifically applied to energy management in REEVs.

Findings

The SDP algorithm effectively minimizes energy consumption.

Including physical constraints improves model realism.

Numerical results show the stochastic approach outperforms deterministic methods.

Abstract

This work presents a stochastic dynamic programming (SDP) algorithm that aims at minimizing an economic criteria based on the total energy consumption of a range extender electric vehicle (REEV). This algorithm integrates information from the REEV's navigation system in order to obtain some information about future expected vehicle speed. The model of the vehicle's energetic system, which consists of a high-voltage (HV) battery, the main energy source, and an internal combustion engine (ICE), working as an auxiliary energy source), is written as a hybrid dynamical system and the associated optimization problem in the hybrid optimal control framework. The hybrid optimal control problem includes two important physical constraints on the ICE, namely, an activation delay and a decision lag. Three methods for the inclusion of such physical constraints are studied. After introducing the SDP algorithm formulation we comment on numerical results of the stochastic algorithm and its deterministic counterpart.