A strategy of electrical energy management for internal combustion engine vehicle based on driving cycle recognition and electrical load perception

Abstract

Electrical energy consumption is an important component of energy consumption for internal combustion engine vehicle, which directly affects the fuel economy. A bus-based electrical energy management system is built, and an electrical energy management strategy based on driving cycle recognition and electrical load perception is presented to achieve the refined management of vehicle energy. Six typical driving cycles are selected to establish an improved learning vector quantization neural network model for driving cycle recognition. The corresponding model training algorithm is designed by utilizing a similar driving cycle classification and the gradient optimization so that the better recognition accuracy and the less computation intensity can be obtained. An online recognition mechanism based on sliding time window is devised, and the optimal time window length is determined. To minimize fuel consumption, a dynamic optimal regulation strategy for the output power of the alternator and battery, which considers driving cycle recognition and electrical load perception, is proposed. Experimental results show that the strategy can effectually improve the vehicle fuel economy according to the driving cycle and the electrical load change and decrease the fuel consumption per 100 miles of vehicle.