Research On Control Strategy Of Parallel Hybrid Electric Vehicle Based On Fuzzy Control

With the increasingly serious energy and environmental problems,the development and promotion of new energy vehicles with low fuel consumption and emissions has become a trend in the development of the automotive industry today.Hybrid vehicles have the advantages of both traditional fuel vehicles and pure electric vehicles,and have become the most promising new energy vehicles today,with better power,low energy consumption and lower emissions,which improve the economy and improve the cruising range of the whole vehicle.The rationality of the energy management strategy design is the key to affecting the performance of hybrid vehicles,so this paper focuses on the design and optimisation of the energy management strategy for parallel hybrid vehicles.(1)Parallel hybrid system selection and parameter matchingThis paper takes a pure fuel car as the prototype,selects the P2 configuration single motor parallel hybrid power system,and selects and matches the parameters of the parallel hybrid power system with the vehicle’s maximum speed,100 km acceleration time and maximum climbing degree as the power index.According to the basic parameters and performance indicators of the vehicle,and combined with the power matching principle of the vehicle,the parameters of the engine,motor,battery pack and transmission system components are determined.(2)Energy management strategy based on logical threshold rulesThe working mode of a parallel hybrid vehicle is analyzed and used as the basis for designing a logic threshold rule energy management strategy.AVL-Cruise is used to build a simulation model of the whole vehicle,Simulink is used to model the control strategy with logic threshold rules,and the vehicle model is co-simulated through the Cruise Interface interface,and the simulation results show that the designed energy management strategy meets the initial requirements and provides a basis for subsequent comparison and analysis with the double fuzzy control strategy.(3)Energy management strategy based on double fuzzyThe double fuzzy energy management strategy is constructed by designing separate fuzzy controllers for drive and brake conditions.the drive fuzzy controller with the whole vehicle demand torque and battery pack charge state as input and engine torque as output,and the brake fuzzy controller with brake pedal opening,vehicle speed and battery pack state of charge as input and motor regenerative braking torque as output,and the selection of input and output variables,fuzzy control quantization,fuzziness,design of the membership function,formulation of fuzzy control rules and anti-fuzziness,etc.The design work is analyzed and a reasonable design solution is given.Using Matlab/Simulink to build a model based on double fuzzy control strategy and simulated jointly with the vehicle model.Compared to the control strategy with logic threshold rules,the double fuzzy control strategy optimizes the torque distribution of the power system and further reduces the fuel consumption and emissions of the vehicle.(4)Optimization of double fuzzy control strategy using adaptive particle swarm algorithm and immune particle swarm algorithmTo address the shortcomings such as the large subjectivity in the design process of the fuzzy controller,the adaptive particle swarm algorithm and the immune particle swarm algorithm are used to optimise the parameters of the affiliation function.Using the difference between the battery charge state and the range of values of the optimisation variables as constraints,and applying weight factors,the vehicle fuel consumption and emission indexes are used as the objective function for multi-objective optimisation.The simulation results show that both the adaptive particle swarm algorithm and the immune particle swarm algorithm can effectively reduce the fuel consumption and emissions of the vehicle,and the adaptive particle swarm algorithm can reduce the engine fuel consumption and the overall fuel consumption by 4.9% and 6.61% respectively,and reduce the HC,CO and NOx emissions by 5.1%,0.9% and 8.9% respectively compared with the pre-optimisation strategy.However,the convergence speed and optimisation effect of the immune particle swarm algorithm were better,with the combined engine and vehicle fuel consumption reduced by 8.6% and 9.91%,and the HC,CO and NOx exhaust emission values reduced by 9.4%,1.6% and 14%,respectively.