Cloud Computing Based Optimal Driving for a Parallel Hybrid Electric Vehicle

A cloud computing based optimal driving method is proposed and its feasibility is validated through a real-world scenario simulation.Based on principles of vehicle dynamics,the driving optimization problem has been formulated into an optimal control problem constrained by traffic rules,directed at achieving lower equivalent fuel consumption and shorter travel time.In order to conveniently specify the constraints and facilitate the application of the dynamic programming(DP)algorithm,the driving optimization problem is transformed into spatial domain and discretized properly.Considering the heavy computational costs of the DP algorithm,a cloud computing based platform structure is proposed to solve the optimal driving problem in real-time.A case study is simulated based on a real-world traffic scenario in Matlab.Simulation results demonstrate that the cloud computing framework is promising toward realizing the real-time energy management for hybrid electric vehicles.

OPTIMAL TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLE WITH AUTOMATIC MECHANICAL TRANSMISSION

In parallel hybrid electrical vehicle （PHEV） equipped with automatic mechanical transmission （AMT）, the driving smoothness and the clutch abrasion are the primary considerations for powertrain control during gearshift and clutch operation. To improve these performance indexes of PHEV, a coordinated control system is proposed through the analyzing of HEV powertrain dynamic characteristics. Using the method of minimum principle, the input torque of transmission is optimized to improve the driving smoothness of vehicle. Using the methods of fuzzy logic and fuzzy-PID, the engaging speed of clutch and the throttle opening of engine are manipulated to ensure the smoothness of clutch engagement and reduce the abrasion of clutch friction plates. The motor provides the difference between the required input torque of transmission and the torque transmitted through clutch plates. Results of simulation and experiments show that the proposed control strategy performs better than the contrastive control system, the smoothness of driving and the abrasion of clutch can be improved simultaneously.

Combined Estimation of Vehicle Dynamic State and Inertial Parameter for Electric Vehicles Based on Dual Central Difference Kalman Filter Method

Distributed drive electric vehicles(DDEVs)possess great advantages in the viewpoint of fuel consumption,environment protection and traffic mobility.Whereas the effects of inertial parameter variation in DDEV control system become much more pronounced due to the drastic reduction of vehicle weights and body size,and inertial parameter has seldom been tackled and systematically estimated.This paper presents a dual central difference Kalman filter(DCDKF)where two Kalman filters run in parallel to simultaneously estimate vehicle different dynamic states and inertial parameters,such as vehicle sideslip angle,vehicle mass,vehicle yaw moment of inertia,the distance from the front axle to centre of gravity.The proposed estimation method only integrates and utilizes real-time measurements of hub torque information and other in-vehicle sensors from standard DDEVs.The four-wheel nonlinear vehicle dynamics estimation model considering payload variations,Pacejka tire model,wheel and motor dynamics model is developed,the observability of the DCDKF observer is analysed and derived via Lie derivative and differential geometry theory.To address system nonlinearities in vehicle dynamics estimation,the DCDKF and dual extended Kalman filter(DEKF)are also investigated and compared.Simulation with various maneuvers are carried out to verify the effectiveness of the proposed method using Matlab/Simulink-CarsimR.The results show that the proposed DCDKF method can effectively estimate vehicle dynamic states and inertial parameters despite the existence of payload variations and variable driving conditions.This research provides a boot-strapping procedure which can performs optimal estimation to estimate simultaneously vehicle system state and inertial parameter with high accuracy and real-time ability.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

Power Matching and Simulation Analysis of ISG Hybrid Electric Vehicle

Aiming at the limitation of ISG light hybrid electric vehicle, the power matching design of ISG moderate hybrid electric vehicle was studied. According to the requirements of vehicle performance indicators, the parameters of vehicle power drive system including engine, ISG motor, battery and related power transmission system were designed;the basic control strategy of ISG moderate hybrid electric vehicle was put forward;the dynamic model of vehicle was established by using MATLAB/Simulink software platform, and the vehicle performance was simulated under selected cycle conditions. The simulation results show that the parameter matching of the drive system is reasonable, the power performance of the vehicle meets the corresponding requirements, and the fuel economy has been significantly improved.

On Designing of the Main Elements of a Hybrid-Electric Vehicle Driving System

The paper deals with the designing of an electric drive system used for hybrid electric vehicles. The driving system is realized with an induction motor and a voltage source inverter. Specifically, the application is for a series hybrid vehicle powered by electric storage batteries charged by solar batteries. In the first part of the paper the designing of the electric storage batteries and of the photoelectric system is presented. In the second part of the paper some aspects regarding the designing of the induction motor are presented. Then some aspects concerning the voltage source inverter designing are exposed.

City-Bus-Route Demand-based Efficient Coupling Driving Control for Parallel Plug-in Hybrid Electric Bus

Recently,plug?in hybrid electric bus has been one of the energy?e cient solutions for urban transportation. However,the current vehicle e ciency is far from optimum,because the unpredicted external driving conditions are di cult to be obtained in advance. How to further explore its fuel?saving potential under the complicated city bus driving cycles through an e cient control strategy is still a hot research issue in both academic and engineering area. To realize an e cient coupling driving operation of the hybrid powertrain,a novel coupling driving control strategy for plug?in hybrid electric bus is presented. Combined with the typical feature of a city?bus?route,the fuzzy logic inference is employed to quantify the driving intention,and then to determine the coupling driving mode and the gear?shifting strategy. Considering the response deviation problem in the execution layer,an adaptive robust controller for electric machine is designed to respond to the transient torque demand,and instantaneously compensate the response delay and the engine torque fluctuation. The simulations and hard?in?loop tests with the actual data of two typical driving conditions from the real?world city?bus?route are carried out,and the results demonstrate that the pro?posed method could guarantee the hybrid powertrain to track the actual torque demand with 10.4% fuel economy improvement. The optimal fuel economy can be obtained through the optimal combination of working modes. The fuel economy of plug?in hybrid electric bus can be significantly improved by the proposed control scheme without loss of drivability.

Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model

This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles.

Energy management with dual droop plus frequency dividing coordinated control strategy for electric vehicle applications

In this paper,a dual droop-frequency diving coordinated control strategy is proposed for electric vehicle(EV)applications,where the hybrid energy storage system(HESS)with supercapacitors and batteries is integrated to prolong the life time of storage elements.The dynamic power allocation between the supercapacitor and batteries are obtained through the voltage cascaded control,upon which the high and low frequency power fluctuation are absorbed by the supercapacitors and batteries respectively to fully exploit the advantages of the supercapacitors and batteries.Moreover,the power capacity is scaled up by connecting storage blocks in parallel.A dual droop control scheme for parallel-connected energy storage system and its operation principle is introduced on the aspect of current sharing characteristic and state-of-charging(SOC)management.After detailed analysis and formula derivation,the corresponding loop parameters are designed.Through this control method,the current sharing performance is ensured and each block makes the self-adaptive adjustment according to their SOC.Consequently,the load power can be shared effectively,which helps to avoid the over-charge/over-discharge operation and contributes to the life cycle of the energy storage system.Each module is autonomous controlled without the necessity of communication,which is easy,economic and effective to realize.Finally,the simulation and experimental results are exhibited to verify the effectiveness of the proposed control scheme.

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper,we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles(HEVs).Considering the inherent complexities brought about by the velocity profile optimization and energy management control,a hierarchical control architecture in the model predictive control(MPC)framework is developed for real-time implementation.In the higher level controller,a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving.The real-time control actions are derived through a computationally efficient algorithm.In the lower level controller,an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller.The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13%fuel consumption saving compared with a benchmark strategy.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles:cooperative velocity and lane-changing control

Purpose–This study aims to propose an enhanced eco-driving strategy based on reinforcement learning(RL)to alleviate the mileage anxiety of electric vehicles(EVs)in the connected environment.Design/methodology/approach–In this paper,an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed for connected EVs.The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving.Moreover,this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.Findings–To illustrate the performance for the EEDC-HRL,the controlled EV was trained and tested in various traffic flow states.The experimental results demonstrate that the proposed technique can effectively improve energy efficiency,without sacrificing travel efficiency,comfort,safety and lane-changing performance in different traffic flow states.Originality/value–In light of the aforementioned discussion,the contributions of this paper are two-fold.An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs.A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

Critical Speeds of Electric Vehicles for Regenerative Braking

Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.

考虑道路识别的四驱电动汽车再生制动策略

为了充分利用路面附着条件分配再生制动力,提高制动能量回收率,根据双电动机四驱结构电动汽车复合制动系统的特点,对其制动能量回收控制策略进行了研究.分析了驱动轮与地面附着特性,设计了道路识别器来计算每个轮胎与当前路面之间的峰值附着系数,并使用模糊控制策略确定每个车轮与8种道路的滑移率和附着利用率的相似输入.根据双电动机外部特性的制动力分配关系,提出了使更多制动能量回馈给电池的策略.结果表明:制动强度为0.3时,能量回收率为65.55%,具有较高的回收效率.

考虑驾驶风格的混合动力汽车强化学习能量管理策略

为了提升混合动力汽车能量管理策略对不同风格驾驶员的适应性,基于深度强化学习和等效燃油消耗最小策略(equivalent consumption minimization strategy,ECMS),提出一种考虑驾驶风格的混合动力汽车能量管理策略。通过实车试验采集驾驶员行驶数据,基于采集数据进行驾驶员驾驶风格的聚类分析,建立驾驶风格识别模型;构建基于强化学习和ECMS的能量管理策略,将驾驶风格系数作为强化学习状态变量,利用多种驾驶风格的组合工况训练深度确定性策略梯度智能体,获取不同工况和驾驶风格下ECMS等效因子,采用ECMS求解最优发动机、电机转矩分配以及变速箱挡位;搭建硬件在环测试平台,并基于实际采集的不同驾驶员驾驶数据构建测试工况,验证所提出控制策略的有效性。研究结果表明,相较于基于规则策略、基于等效因子比例修正的自适应ECMS以及DRL-SAC策略,提出的考虑驾驶风格的强化学习能量管理策略使整车能量消耗分别降低16.35%、11.11%和7.56%,所提控制策略的有效性得到了验证。

考虑分散协作及数量折扣的双目标电动车辆路径优化

当前传统燃油车辆造成了极大的空气污染和资源浪费,电动车辆和协作物流是降低碳排放、提高运输效率的有效途径。本文基于协作物流的思想,建立以运输利润最大及配送任务完成量最大为双目标,考虑分散协作及数量折扣的带时间窗电动车辆路径优化模型。设计将贪婪随机自适应搜索—进化邻域搜索(GRASP-ELS)混合算法与ε-约束法相结合的ε-约束混合进化算法,并通过算例对模型和算法进行测试。实验结果表明:所提出的算法优于多目标优化算法NSGA-Ⅱ;通过灵敏度分析给出管理启示。本文为分散协作情境下电动车辆配送优化提供方法借鉴与决策参考。

基于循环工况的电动汽车双电机驱动系统协同优化设计

双电机优化设计需要考虑双电机系统的耦合运行,在传统优化方法中多采用估计效率Map的方法得到双电机系统最优的转矩分配系数。然而在高效电机优化设计中无法忽视估计法带来的误差。此外不同工况下电机的控制策略也不同。为了提高双电机驱动系统全工况运行效率,本文基于两种循环工况,系统地提出了一种电动汽车双电机协同优化设计方法。首先通过参数匹配获得了初代电机参数,接着提出了基于半解析模型的电机效率Map计算方法。基于此方法研究了不同工况下双电机系统的最优转矩分配和控制策略。然后基于粒子群算法,考虑电机不同工况下的控制策略对双电机本体参数进行了全工况优化。最后搭建了半实物实验平台,仿真和实验结果表明,所提优化方法能够提高双电机驱动系统的运行效率。

基于混合粒子群的电动汽车转矩分配策略研究

针对双电机驱动系统电动汽车前、后轴电机转矩分配问题,利用Simulink与AVL-Cruise软件建立了双电机电动汽车整车模型;基于车辆构型特点建立双电机驱动系统效率数学模型,以双电机驱动系统的整体效率最高为优化目标,在前、后轴电机特性约束范围内通过混合粒子群算法,对转矩分配系数进行迭代优化研究。仿真结果表明,在单个工况点和连续运行工况下,混合粒子群算法的转矩分配策略能够实现合理的转矩分配满足车辆行驶的需求,在NEDC、CLTC-P、WLTC三种循环工况下,采用该策略的电动汽车续驶里程延长了11.71%、9.06%、9.46%,双电机电动汽车的经济性明显提升。

基于工况识别的纯电动汽车2DCT换挡策略研究

为使纯电动汽车具有更好的经济性,同时保持较好的动力性,针对自主研发的纯电动汽车两挡干式双离合变速器(2DCT),提出一种基于工况识别的实时换挡策略。通过径向基神经网络预测车速,利用动态规划提取7种循环工况的最优换挡点,并搭建基于相似度比对的工况识别模型,对车辆行驶工况进行识别,实现实时换挡。基于MATLAB/Simulink进行仿真并完成2DCT台架实验。结果表明,提出的基于工况识别的实时换挡策略能同时兼顾车辆的经济性和换挡频率。

电动汽车电驱动系统抗干扰扭振自适应控制研究

为了提高电动汽车处于纯电动模式下工作效率,设计了一种电动汽车电驱动系统抗干自适应扰扭振控制策略。构建电驱动系统动力平衡方程,同时引入了前馈与反馈控制器进行系统调控。有干扰仿真结果表明:设置前馈控制后,电动汽车启动阶段存在外部干扰因素影响时,可通过反馈控制器减缓传动部件产生的振动作用,从而达到更平稳控制效果,实现乘坐舒适度的显著改善。该研究能够有效消除电动汽车电驱动系统抗干扰能力,具有很好的应用价值。

某混合动力车型低温纯电续航能耗分解

文章通过对一款混合动力汽车在环境温度25℃、-7℃和-18℃三种温度下进行纯电续航仿真和实测,对比分析了动力电池放电功率点、PTC功耗、DCDC功耗和驱动电机效率差异,结果表明:环境温度-7℃比25℃模拟用户实际工况电耗上升7.4kW·h,-18℃比25℃电耗上升8.3kW·h,低温环境下PTC功耗、DCDC功耗增大,电机效率降低;客户续航感受越差。