Research On Real-time Energy Management Strategy Of Single-shaft Parallel Hybrid Electric Vehicle

Energy management strategy,as the core technology of hybrid electric vehicles,can greatly improve the fuel efficiency of hybrid electric vehicles by allocating the energy of the engine and motor reasonably so that the engine can work in an efficient area as much as possible.However,current energy management strategies still have shortcomings in terms of practical control feasibility and adaptability to complex driving conditions.Therefore,this article takes P2 coaxial parallel hybrid electric vehicles as the research object,combines driving condition recognition technology and transmission system parameter matching optimization algorithms,and conducts in-depth research on the real-time energy management strategy of hybrid electric vehicles.The main research content is as follows:(1)A parameter matching optimization method based on an improved multi-objective differential evolution algorithm is proposed to address the issue of parallel optimization of energy management strategy parameters and transmission system parameters,as well as the comprehensive optimization of fuel consumption and emissions,in the current transmission system parameter matching optimization algorithm.This strategy takes rule-based energy management strategy parameters and transmission system parameters as optimization variables,with vehicle power,economy,and emissions as optimization objectives.The fuzzy correlation entropy method is used to select a compromise solution that takes into account various optimization objectives,providing a reference for parameter matching of hybrid electric vehicle transmission systems.(2)A real-time energy management strategy based on approximate equivalent fuel consumption minimization is proposed to address the issue of high computational complexity in processing real-time power allocation in current real-time energy management strategies.This strategy combines the structural characteristics of a P2 coaxial parallel hybrid electric vehicle,and fits the engine fuel consumption data into a quadratic function;Equivalent to the energy consumption of the motor to fuel consumption introduces a power distribution factor,and transforms the cost function relationship into a cubic function about the power distribution factor;Solve candidate solutions in real-time to obtain the optimal power allocation,achieving the goal of simplifying calculations.At the same time,to reduce the impact of equivalent factors on the strategy,a hybrid particle swarm optimization algorithm is used to optimize the equivalent factors,further improving the fuel economy of the vehicle.(3)A real-time energy management strategy based on driving conditions is proposed to address the issue of poor adaptability of energy management strategies to complex driving conditions.The limitations of this strategy on current driving condition recognition algorithms,such as reducing recognition computation by discarding feature parameters with low contribution rates,resulting in loss of driving condition data and affecting recognition accuracy,etc.,adopt an extreme learning machine recognition model that integrates driving condition feature parameters;To solve the problem that the dynamic programming algorithm needs to predict the driving conditions and cannot achieve real-time power allocation due to the large amount of calculation,a real-time allocation method is proposed by fitting and quantifying the offline optimization results of each typical driving condition based on dynamic programming,with the driving condition type,equivalent distance coefficient and vehicle demand torque as the input and the optimal power allocation factor as the output,which is combined with the driving condition identification model,It forms a real-time energy management strategy with good adaptability to complex driving conditions.(4)A simulation analysis was conducted on the proposed real-time energy management strategy,and the simulation results showed that the proposed energy management strategy has significant improvements in real-time performance and adaptability to complex driving conditions.The proposed real-time energy management strategy was tested using a new energy vehicle test bench,and the test results verified the practicality and effectiveness of the real-time energy management strategy.