Intelligent Energy Management Optimization Strategy In Consideration Of Battery Life For Plug-in Hybrid Electric Vehicle

With the development of the automotive industry,the problems of environmental pollution and energy shortage have become serious.In order to cope with these problem,plug-in hybrid electric vehicles which can be charged through an external power grid have been regarded as one of the research focuses for how to reduce petroleum demand and exhaust.In the driving cycle of plug-in hybrid electric vehicles,demand power is usually provided by multiple energy sources.Therefore,energy management strategies are crucial to reducing energy consumption.In addition,plug-in hybrid electric vehicles are often equipped with high-cost batteries which need to charge and discharge repeatedly.This indicates that if one wants to achieve economic feasibility for plug-in hybrid electric vehicles,battery health cannot be ignored.This paper studies intelligent energy management optimization strategy in consideration of battery life for plug-in hybrid electric vehicles.The main research content is as follows:A plug-in hybrid electric vehicle system model for designing energy management strategy with the consideration of battery life is established first.The main work is to establish an effective ampere-hour throughput model which can be used to evaluate battery life decay in the design of control strategy by fitting historical data.This model uses severity factor to assess the different effects on battery life of different operating conditions.Then,by analyzing and transforming the historical traffic information of commuter vehicles,a driving cycle model with probabilistic statistical characteristics was established.Based on the above model,the energy management optimization problem for plug-in hybrid electric vehicles with the consideration of energy consumption and battery health is formed.To solve the problem of multi-objective energy management strategy with the objects of minimum energy consumption and battery life loss,the optimal control strategy is obtained by using stochastic dynamic programming and particle swarm optimization.According to the probabilistic characteristics of driving cycle,the power of different states is distributed by policy iteration algorithm of stochastic dynamic programming.In order to achieve the balance between the two goals,energy consumption and battery life,particle swarm algorithm is used to select the weight coefficient.The optimal weight coefficients solved by particle swarm algorithm are substituted into the cost function of the stochastic dynamic programming.And the cost corresponding to the optimal control strategy obtained by stochastic dynamic programming is used as the fitness value of particle swarm algorithm.These two algorithm are nested within each other.Then the optimal control strategy that can minimize energy consumption and battery life loss can be find through iterations.In order to verify the effectiveness and real-time application of the designed energy management control strategy,the controller model is built under MATLAB/Simulink platform and plug-in hybrid electric vehicle with driver model is built on automotive simulation software GT-SUITE.Then the real-time simulation is carried out based on the strategy obtained on these GT-SUITE and MATLAB/Simulink platform.