Adaptive Energy Management Strategy Based on Driving Cycle Identification for Hybrid Electric Vehicles

To improve the control performance of traditional equivalent fuel consumption minimum strategy (ECMS) under the real complex road conditions, an adaptive equivalent consumption minimum strategy (A-ECMS) of energy management was proposed for a parallel hybrid electric vehicle, which can adjust the equivalent factor online according to the change of driving cycle. The significantly different characteristic parameters of driving cycle were extracted by statistical method. The driving cycles were classified with cluster analysis method, and the database of typical driving cycles was constructed. Then, the optimal equivalent factor for each typical driving cycle can be calculated. A recognizer of driving cycles was designed by learning vector quantization method, and its accuracy of recognization was proved up to 98.8%. The actual driving condition was identified to be one of the typical driving cycles by the recognizer, and the corresponding optimal equivalent factor was adopted as the optimization input of ECMS. Thus, the A-ECMS control strategy based on the driving condition online identification was established. The simulation results show that the optimization effect of A-ECMS is similar to ECMS under the given single cycle, the fuel economy of A-ECMS is decreased by 0.8% and SOC increased by 0.13%. Under the multi-driving cycles, the fuel economy of A-ECMS strategy is improved by 4.18%, and the fluctuation of SOC is reduced by 43.26%, which can prove the superiority of the A-ECMS. © 2018, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.