Dynamic programming technique in hybrid electric vehicle optimization

Abstract

Hybrid electric vehicle (HEV) is a type of vehicle which combines a conventional internal combustion engine (ICE) propulsion system with an electric propulsion system. HEV is intended to achieve either better fuel economy than a conventional vehicle, or better performance. HEVs have been gaining popularity given that they are an effective solution to reducing fuel consumption and emissions. However, its potential in fuel economy is hardly fully explored by existing control strategies based on engineering intuition. Dynamic programming (DP) technique is an effective tool to find the globally optimal use of multiple energy sources over a pre-defined drive cycle. As a global optimizing algorithm, DP ensures to converge to the global optimum. Even though DP is an off-line algorithm, the results can serve as a benchmark to evaluate and improve an existing online algorithm. In this paper, the procedures for implementing DP to three typical HEV powertrains are explained in detail. Also, the cost function of DP is discussed. In the case study of Toyota hybrid system, a simplified vehicle model is given and validated. Then DP is applied to this model and the effect of cost function on fuel economy and battery state of health (SOH) is discussed. Comparing to the simulation results over UDDS cycle obtained from the Prius model in Advisor, the DP results over the same drive cycle shows a 30% potential improvement in overall cost, which converts the electricity cost into fuel cost. In addition, based on the DP results, a lookup table based real-time control strategy is developed. This control strategy results in an improvement of 27% of overall cost, which is very close to the ideal case.