Intelligent energy control strategy for plug-in hybrid electric vehicle based on driving condition recognition

To improve the energy control performance of parallel hybrid electric vehicle (PHEV) under real complex driving conditions, an intelligent equivalent-fuel consumption minimum strategy (I-ECMS) was proposed in this work. First, the feature parameters of standard driving cycles were extracted by the principal component analysis (PCA) method, and the average vehicle speed, the minimal squared error of the speed, the ratio of the deceleration interval and the driving range were chosen as principal components. Then, the driving conditions were classified with fuzzy C-clustering analysis method and the recognition rule database was constructed. Thus, the optimal equivalent factors for each standard driving cycle are computed and the proposed I-ECMS automatically adjusts the equivalent factor online according to the actual driving condition. Thus, the I-ECMS strategy based on online recognizing of vehicle driving condition was established. To prove the effectiveness of the proposed I-ECMS, based on the driving conditions selected from the typical driving cycles, the recognition analysis and the optimization results under I-ECMS and traditional ECMS are obtained and compared. Results show that the accuracy of the recognizer is proved up to 97.7%, and the fuel economy is improved about 4.36%, the fluctuation of SOC is reduced about 43.8%. © 2018 Indian Pulp and Paper Technical Association. All Rights Reserved.