A Development of Design and Control Methodology for Next Generation Parallel Hybrid Electric Vehicle

Abstract

Commercially available Hybrid Electric Vehicles (HEVs) have been around for more than ten years. However, their market share remains small. Focusing only on the improvement of fuel economy, the design tends to reduce the size of the internal combustion engine in the HEV, and uses the electrical drive to compensate for the power gap between the load demand and the engine capacity. Unfortunately, the low power density and the high cost of the combined electric motor drive and battery packs dictate that the HEV has either worse performance or much higher price than the conventional vehicle. In this research, a new design philosophy for parallel HEV is proposed, which uses a full size engine to guarantee the vehicle performance at least as good as the conventional vehicle, and hybridizes with an electrical drive in parallel to improve the fuel economy and performance beyond the conventional cars. By analyzing the HEV fuel economy versus the increasing of the electrical drive power on typical driving conditions, the optimal hybridization electric power capacity is determined. Thus, the full size engine HEV shows significant improvement in fuel economy and performance, with relatively short cost recovery period.

A new control strategy, which optimizes the fuel economy of parallel configured charge sustained hybrid electric vehicles, is proposed in the second part of this dissertation. This new approach is a constrained engine on-off strategy, which has been developed from the two extreme control strategies of maximum SOC and engine on-off, by taking their advantages and overcoming their disadvantages. A system optimization program using dynamic programming algorithm has been developed to calibrate the control parameters used in the developed control strategy, so that the control performance can be as close to the optimal solution as possible. In order to determine the sensitivity of the new control strategy to different driving conditions, a passenger car is simulated on different driving cycles. The performances of the vehicle with the new control strategy are compared with the optimal solution obtained on each driving condition with the dynamic programming optimization. The simulation result shows that the new control strategy always keeps its performance close to the optimal one, as the driving condition changes.