Energy Management Strategies For New Energy Vehicles Based On V2X Communication

Faced with the increasingly prominent energy shortage and environmental pollution issues,electric vehicles are regarded as the future development direction of the automotive industry due to their advantages of energy saving,environmental protection and low cost of use.However,the energy density of existing power batteries is much lower than that of gasoline,resulting in short range of electric vehicles.In addition,battery development and manufacturing costs account for about 40 % of the total vehicle cost,but battery life is usually only 3 to 5 years.Therefore,research on improving the energy efficiency of electric vehicles and extending the battery life is crucial to the development of electric vehicles.Based on the existing V2 X communication technology,this paper realizes the mobility optimization of electric vehicles on urban roads from the aspects of energy saving and battery life prolonging.Adaptive cruise system is an important assistant driving system and one of the transitional technologies to realize automatic driving。During the driving process of an electric vehicle,a formation is formed spontaneously according to the driving information of the vehicle in front.The adaptive cruise system is used to implement the following driving mode,which can not only improve the safety but also relieve the driver’s driving burden.In order to achieve the following performance,the traditional adaptive cruise control system will lead to aggressive acceleration and deceleration behavior,which will accelerate the energy consumption of electric vehicles and reduce the driving range.In view of these shortcomings,this paper introduces the power consumption economy index in the design of adaptive cruise driving system,aiming to reduce the energy consumption of electric vehicles and extend their driving range by optimizing the track of vehicle following speed on the premise of ensuring vehicle safety.This project studies the design of ACC based on multi-objective collaborative optimization,and analyzes the safety,stability,passenger comfort and power consumption economy in the process of vehicle tracking,and and designs an adaptive cruise system with hierarchical control structure to achieve multi-objective collaborative optimization.Considering that the model predictive control algorithm can make full use of the future prediction information to improve the optimization effect and has the advantage of solving the constrained multi-objective optimization problem,this paper analyzes multiple optimization indexes within the framework of model predictive control algorithm and transforms them into corresponding objective functions and system constraints.The multiobjective collaborative optimization ACC is transformed into a linear model predictive control problem and finally solved by quadratic programming.The simulation results show that compared with the traditional ACC,the multiobjective ACC designed in this paper considering the power consumption economy can save about 20% of energy consumption,which will effectively alleviate the range anxiety of electric vehicles during driving.In addition,due to the optimization of the speed trajectory,the change rate of the vehicle’s speed and acceleration in the process of vehicle following is reduced,thus improving the ride comfort of passengers.During the driving process of an electric vehicle,frequent aggressive driving behaviors such as frequent acceleration and deceleration or emergency acceleration and braking will generate large currents in an instant,accelerating the aging of the power battery,and seriously affecting the battery life.At present,researches on improving battery discharge performance and prolonging battery life usually only consider the impact of dynamic performance of drive system on battery,but ignore the important influence of HVAC system on battery,which is the largest auxiliary energy consumption system in electric vehicles.In this paper,we designed an HVAC control strategy based on driving behavior to extend battery life,which compensated for the influence of aggressive driving behavior on battery discharge performance and service life by adjusting the operation of HVAC system.Firstly,the dynamic characteristics of the driving system and HVAC system of electric vehicles are analyzed and modeled respectively.Then,based on the established model and the control goal of extending battery life,a nonlinear model predictive control algorithm for HVAC system is designed,and the online solution is realized by using sequential quadratic programming algorithm.Finally,the performance of HVAC control algorithm is simulated under different driving conditions and external temperatures.Simulation results show that the algorithm can effectively compensate the influence of aggressive driving behavior on battery discharge characteristics and battery life by adjusting the operating state and energy consumption of the HVAC system,thus reducing the discharge pressure of the battery and prolonging its lifetime.By analyzing multiple optimization indexes within the framework of model predictive control algorithm and transforming them into corresponding objective functions and system constraints,the multi-objective collaborative optimization ACC is transformed into a linear model predictive control problem and finally into a quadratic programming problem with constraints.