Research On Dynamic ECMS Energy Management Strategy For Hybrid Electric Vehicles

In recent years,hybrid electric vehicles(HEVs)have become one of the main development trends in the field of transportation to reduce carbon emissions and alleviate environmental pollutions.Energy management strategy(EMS),as an important part of HEVs,could optimize the operation of different power sources to effectively improve the vehicular fuel economy and reduce the pollution emissions.An equivalent consumption minimization strategy(ECMS),as a local optimization strategy,has become one of the most promising and widely studied EMS technologies due to its good theoretical optimization and real-time applicability.This thesis takes the rangeextended hybrid electric systems as the research object.Starting from the conventional ECMS,new dynamic ECMS(DECMS)methods based on different information sources and multi-objective optimization are proposed and verified through simulation and experiment verification.The proposed DECMS not only could enhance the functionality of energy management,but also improve the fuel economy,driving comfortability of the vehicles.The main contents of this work are summarized as follows.A DECMS based on vehicle speed feature recognition is proposed,in which the short-term historical speed information of HEVs is utilized.The self-organizing map(SOM)neural network method is used to identify the vehicle speed features of various standard-driving cycle segments,and four typical vehicle speed features are obtained.Then,the characteristic driving conditions corresponding to these four speed features are constructed,and the optimal equivalent factors under these different characteristic driving conditions are obtained by using the shooting method.At the same time,the start-up hysteresis characteristics of the engine is utilized,and a fuzzy controller is introduced to control the minimum operating time of the engine,which could realize the adaptive optimization adjustment function of the strategy for the battery state of charge(SOC)and the ambient temperature.The simulation results show that,compared with the rule-based single-point strategy and conventional ECMS,the proposed DECMS results in fuel-saving improvements of 4.56%～7.87% and reduction of the battery peak charging-and-discharging current by 42.94%～62.19% which is beneficial to battery’s life under different driving conditions.In addition,the real-world road experiments show that this strategy also has obvious advantages in the vehicular fuel economy.To further improve the fuel economy of DECMS,for commercial HEVs with fixed driving routes,a DECMS based on vehicle speed prediction algorithm(DECMS-Ⅱ)is proposed.A high-precision vehicle speed prediction algorithm is obtained by combining the the traditional Markov model and the trigonometric function approximation together with the moving average filtering method.Compared with the traditional Markov model,the prediction accuracy of the 5-second and 10-second period is improved by 32.28% and 23.33%,respectively.On this basis,the radial-base function neural network method is used to fit the relationship between the predicted vehicle speed and the suboptimal SOC trajectory slope,and the method is applied in an autoregressive moving-average ECMS based on SOC feedback to optimize the vehicular fuel economy.The simulation results show that the vehicular fuel economy results with the use of DECMS-Ⅱ are very close to the theoretical suboptimal results,within a gap of 0.46%,which is better than those with other compared strategies.In oreder to extend the DECMS method to the situation when the vehicle mileage information is known,an extended DECMS(EDECMS)based on the mileage information is proposed.By constructing a reference SOC related to the mileage information,the hybrid driving mode decision and mode optimization are separated.The reference SOC constructed by the mileage information is utilized to switch hybrid driving modes,and a cost function with the noise constraints is utilized to optimize the vehicular fuel economy and the noise level in the cockpit in real time.The verification results of the vehicle road experiments show that compared with DECMS,EDECMS can further reduce the vehicular fuel consumption,and meanwhile,the noise level in the cockpit is also suppressed.