Development of Fuel Economy Improvement by Using Driving Condition Prediction System for Hybrid Vehicle

摘要

If the future driving condition such as road information and traffic condition can be predicted, the use of electrical power source will be controlled appropriately in order to improve the fuel economy of Hybrid vehicle. In this paper the algorithm for the driving condition prediction model and the rule-based controller for HEV are developed and verified through simulation and road test. With road information and traffic from 3D navigation, the types of road (uphill, flat or downhill) and the traffic condition (congestion or free driving) can be predicted by the Driving Condition Prediction System (DCPS). The rule-based controller for HEV can determine the control strategy (discharge-oriented, charge-oriented, or normal) depending on the future driving condition. With this technology the system can secure more battery capacity for regenerating when downhill is anticipated and engine can be operated within high efficiency area by discharging battery energy when uphill is anticipated. When congestion is predicted the battery is charged in advance in order to increase electric driving (EV) range and prevent inefficient series-path driving. Compared to the previous study, the methodology to determine future road condition and control strategy of HEV suggested in this paper is simple and fast enough to apply to real-time controller.