Research Of Adaptive Real-Time Optimal Energy Management Strategy For Parallel Hybrid Electric Vehicles

Hybrid electric vehicles combine the advantages of traditional vehicles and pure electric vehicles,which can effectively alleviate energy crisis and environmental pollution problems.How to reasonably allocate the power between the engine and the motor through energy management strategies is the research focus in the field of hybrid electric vehicles.In the real-time optimal energy management strategy,Equivalent Consumption Minimization Strategy(ECMS)calculates the minimum equivalent fuel consumption to obtain the real-time optimal power of the engine and the motor.Among them,the equivalence factor will have an important impact on the result of the optimal allocation.At present,the adaptive real-time optimal energy management strategy of ECMS needs to determine the initial value of the equivalent factor,and the initial value of the equivalent factor under different working conditions is not completely the same,resulting in poor robustness in adapting to different working conditions.To this end,this thesis takes the parallel hybrid electric vehicle as the research object,and proposes a fuzzy adaptive real-time optimal energy management strategy for multi-working conditions,which can ensure fuel economy under different working conditions while effectively maintaining the state of charge(SOC),which improves its robustness to adapt to different working conditions.The main work of this thesis includes:1.Model research of parallel hybrid electric vehicleFirstly,the model of parallel hybrid electric vehicle including the driver,transmission,engine,motor,battery and other main components was built.In order to verify whether the model of parallel hybrid electric vehicle can operate stably,an energy management strategy based on deterministic rules is designed based on variables such as engine external characteristic curve,vehicle speed,total demand torque and SOC.Subsequently,the experimental verification of the parallel hybrid electric vehicle was completed under US06 and WLTC standard working conditions.The results show that the parallel hybrid electric vehicle can run stably under standard working conditions,which provides an effective guarantee for subsequent research.2.Research on instantaneous optimization energy management strategy based on ECMSScendly,equivalent fuel consumption and real-time optimization are two core parts in ECMS.In order to apply ECMS to the model,based on the simulation model,combined with the power output mode of the parallel hybrid electric vehicle,the realization principle of ECMS is analyzed in detail.Then based on the energy flow mode of ECMS,the instantaneous equivalent fuel consumption rate model of parallel hybrid electric vehicles under different working modes,namely equivalent fuel consumption,is established.The instantaneous equivalent fuel consumption of the parallel hybrid electric vehicle in different working modes is analyzed and calculated.When the instantaneous equivalent fuel consumption is the smallest,the optimal output of the engine and the motor can be obtained,that is real-time optimization.The establishment of ECMS provides a theoretical basis for the subsequent design of adaptive real-time optimization energy management strategy.3.Research on fuzzy adaptive real-time optimal energy management strategy for multi-working conditionsLastly,in order to solve the problem of poor adaptability of ECMS energy management strategies under different working conditions,this thesis is based on multiple standard working conditions such as UDDS,WLTC,NEDC,US06 and HWFET,and traverses all possible equivalent factors through ECMS,get the effective interval of the equivalent factor.Then,based on the effective interval of the equivalent factor,taking the deviation of SOC and its derivative as input,and the equivalent factor as output,a twodimensional fuzzy controller is designed.In order to realize the function of adaptive realtime optimization under a variety of working conditions,this thesis uses the effective interval of the equivalent factor as a bridge,and combines ECMS and a fuzzy controller to obtain a fuzzy adaptive real-time optimization energy management strategy for multiple working conditions.Finally,a horizontal comparison and analysis were carried out with other strategies under a variety of standard conditions.The experimental results show that,compared with other strategies,the fuel economy of the multi-condition fuzzy adaptive real-time optimal energy management strategy has been improved,up to 18.02%.In addition,the SOC deviation is also reduced,up to 75.41%.Therefore,the strategy proposed in this thesis can show good adaptability under different working conditions.