Optimized energy management control for the Toyota Hybrid System using dynamic programming on a predicted route with short computation time

Abstract

Among the general problematic of the HEV power trains, the most critical point is the determination of the power-split ratio between the mechanical and the electrical paths, known as the energy management strategy (EMS). Many EMS are proposed in the literature, and can be grouped in two categories: the local optimization EMS and the global optimization EMS. The local optimization category corresponds to the EMS based on human expertise and the knowledge of the power train components efficiency maps. Thus, the local optimization EMS manages the power train operations by referring to predefined rules. The drawback of such strategies is that it brings an instantaneous fuel consumption optimization, and does not fully optimize the fuel consumption over the whole trip. Therefore, additional fuel savings are still possible. This paper presents an overall optimized predictive EMS for the Toyota Hybrid System (THS-II) power train of the Prius. The proposed EMS is based on Dynamic Programming (DP), where the prior knowledge of the route is required in order to predetermine the power-split ratio and optimize the fuel consumption for the whole predicted route. The DP EMS proposed for the THS-II power train is designed with a very short computation time, intended to be implemented in real-time applications. The potential of this DP-controller in reducing fuel consumption on regulatory cycles are computed and compared to a rule-based controller and to the Prius published fuel consumption results. Finally, the fuel reduction enhancements of the DP-controller are computed for real road tests achieved on a MY06 Prius in Ile-de-France, by comparing to the associated observed consumption measurements.