Parameters matching and optimization of parallel hybrid electric vehicle powertrain

Aiming at the development of parallel hybrid electric vehicle （PHEV） powertrain, parameter matching and optimization are presented, According to the performance of PHEV, the optimization range of engine, motor, driveline gear ratio and battery parameters are determined. And then a two-level optimization problem is formulated based on analytical target cascading （ATC）. At the system level, the optimization of the whole vehicle fuel economy is carried out, while the tractive performance is defined as the constraints. The optimized parameters are cascaded to the subsystem as the optimization targets. At the subsystem level, the final drive and transmission design are optimized to make the ratios as close to the targets as possible. The optimization result shows that the fuel economy had improved significantly, while the tractive performance maintains the former level.

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.

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.

Energy Control of Plug-In Hybrid Electric Vehicles Using Model Predictive Control With Route Preview

The paper proposes an adoption of slope,elevation,speed and route distance preview to achieve optimal energymanagement of plug-in hybrid electric vehicles(PHEVs).Theapproach is to identify route features from historical and real-time traffic data,in which information fusion model and trafficprediction model are used to improve the information accuracy.Then,dynamic programming combined with equivalent con-sumption minimization strategy is used to compute an optimalsolution for real-time energy management.The solution is thereference for PHEV energy management control along the route.To improve the system's ability of handling changing situation,the study further explores predictive control model in the real-time control of the energy.A simulation is performed to modelPHEV under above energy control strategy with route preview.The results show that the average fuel consumption of PHEValong the previewed route with model predictive control(MPC)strategy can be reduced compared with optimal strategy andbase control strategy.

Series-parallel Hybrid Vehicle Control Strategy Design and Optimization Using Real-valued Genetic Algorithm

Despite the series-parallel hybrid electric vehicle inherits the performance advantages from both series and parallel hybrid electric vehicle, few researches about the series-parallel hybrid electric vehicle have been revealed because of its complex co nstruction and control strategy. In this paper, a series-parallel hybrid electric bus as well as its control strategy is revealed, and a control parameter optimization approach using the real-valued genetic algorithm is proposed. The optimization objective is to minimize the fuel consumption while sustain the battery state of charge, a tangent penalty function of state of charge（SOC） is embodied in the objective function to recast this multi-objective nonlinear optimization problem as a single linear optimization problem. For this strategy, the vehicle operating mode is switched based on the vehicle speed, and an ＂optimal line＂ typed strategy is designed for the parallel control. The optimization parameters include the speed threshold for mode switching, the highest state of charge allowed, the lowest state of charge allowed and the scale factor of the engine optimal torque to the engine maximum torque at a rotational speed. They are optimized through numerical experiments based on real-value genes, arithmetic crossover and mutation operators. The hybrid bus has been evaluated at the Chinese Transit Bus City Driving Cycle via road test, in which a control area network-based monitor system was used to trace the driving schedule. The test result shows that this approach is feasible for the control parameter optimization. This approach can be applied to not only the novel construction presented in this paper, but also other types of hybrid electric vehicles.

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.

The Future Trend of E-Mobility in Terms of Battery Electric Vehicles and Their Impact on Climate Change: A Case Study Applied in Hungary

The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).

Applications of Serial-Parallel Compensated Resonant Topology in Wireless Charger System Used in Electric Vehicles

There are lots of factors that can influence the wireless charging efficiency in practice, such as misalignment and air-gap difference, which can also change all the charging parameters. To figure out the relationship between those facts and system, this paper presents a serial-parallel compensated(SPC) topology for electric vehicle/plug-in hybrid electric vehicle(EV/PHEV) wireless charger and provides all the parameters changing with corresponding curves. An ANSYS model is built to extract the coupling coefficient of coils. When the system is works at constant output power, the scan frequency process can be applied to wireless power transfer(WPT) and get the resonant frequency. In this way, it could determine the best frequency for system to achieve zero voltage switching status and force the system to hit the maximum transmission efficiency. Then frequency tracking control(FTC) is used to obtain the highest system efficiency. In the paper, the designed system is rated at 500 W with 15 cm air-gap, the overall efficiency is 92%. At the end, the paper also gives the consideration on how to improve the system efficiency.

Study on load performance of electric motor system used in hybrid electric vehicle

The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model based on Insight structure. The influence of the four control strategies to the load power of the electric motor system used on parallel hybrid electric vehicle is studied. It is found that 80 percent of the motor load power points are under 1/5 of the electric peak power. The motor load power of the power assist control strategy is distributed in the widest range during generating operation, and the motor load power of the global optimization control strategy has the smallest one.

Study on ultracapacitor-battery hybrid power system for PHEV applications

For the battery only power system is hard to meet the energy and power requirements reasonably, a hybrid power system with uhracapacitor and battery is studied. A Topology structure is analyzed that the uhracapacitor system is connected with battery pack parallel after a bidirectional DC/DC converter. The ultracapacitor, battery and the hybrid power system are modeled. For the plug-in hybrid electric vehicle （PHEV） application, the control target and control strategy of the hybrid power system are put forward. From the simulation results based on the Chinese urban driving cycle, the hybrid power system could meet the peak power requirements reasonably while the battery pack＇ s current is controlled in a reasonable limit which will be helpful to optimize the battery pack＇ s working conditions to get long cycling life and high efficiency.

Design and Strategy of Series-Parallel Hybrid System Based on BSFC

In this paper,a drive control strategy is developed based on the characteristics of series-parallel plug-in hybrid system.Energy management strategies in various modes are established with the basis on the minimum brake specific fuel consumption(BSFC)curve of engine.The control strategy,which is based on rules and system efficiency,is adopted to determine the entry/exit mechanisms of various modes according to battery state of charge(SOC),required power and required speed.The vehicle test results verify that the proposed control strategy can improve vehicle economy efficiently and makes a good effect on engine control.

基于规划路径能耗预测的PHEV全局自适应能量管理

新能源汽车智能化能量管理是先进汽车技术研究的重要领域,是进一步提升整车燃油经济性能的关键。针对插电式混合动力汽车(Plug-in hybrid electric vehicle,PHEV)能量全局化管理与控制的实时性和最优性难以兼顾的难题,开展了基于能耗预测的全路径自适应能量管理研究,提出了以等效燃油消耗最小化为目标的全规划路径PHEV自适应控制算法。最后,基于MATLAB/Simulink的建模与仿真分析验证了所提控制算法对实际行驶工况、里程和整车能量状态的变化具有较好的跟随性和自适应性,全路径近似全局性优化控制效果明显,较好地改善了整车的燃油经济性。

基于工况识别的PHEV能量管理策略

为提升并联式混合动力汽车(parallel hybrid electric vehicle,PHEV)的燃油经济性,针对等效燃油消耗最小控制策略(equivalent fuel consumption minimum strategy,ECMS)在不同工况下适应性差的问题,以优化整车等效燃油消耗量为目标,设计基于工况识别算法的变等效因子ECMS能量管理策略。选取3类典型工况建立支持向量机分类模型,通过递归特征消除法对样本特征进行选择,采用鲸鱼算法对支持向量机进行参数优化,使用模拟退火算法分别对3类工况的ECMS等效因子进行离线全局最优求解,并分别存储于等效因子库中,通过训练好的支持向量机分类器对目标优化工况进行工况识别,不同类型的工况片段采用不同的等效因子进行转矩分配。仿真结果显示:相比于逻辑门限能量管理策略,基于工况识别算法的变等效因子ECMS能量管理策略的电池荷电状态(state of charge,SOC)变化量减少8.67%,节油率为13.11%;相比于优化前的ECMS策略电池SOC变化量减少3.47%,节油率约为6.63%。本文提出的基于工况识别算法的变等效因子ECMS能量管理策略可以有效地减少燃油消耗量,提升PHEV的整车经济性。

并联式混合动力汽车(PHEV)动态协调控制方法硬件在环仿真

在指出并联式混合动力汽车(Parallel hybrid electric vehicle,PHEV)发动机与电动机动力耦合过程中存在的协调问题基础上,提出基于模型匹配控制的动态协调控制方法,开发出双驱动电动机结构的硬件在环仿真试验平台硬件系统和基于Matlab/Simulink/RTWT与Visual C++环境的软件系统,并建立PHEV动态协调控制方法硬件在环仿真试验台。对所设计的动态协调控制方法进行硬件在环仿真试验,试验结果表明,该动态协调控制方法能有效控制两个动力源的动力耦合过程,具有较高的转矩控制精度和很好的动态响应特性。

基于模糊控制策略的PHEV仿真研究

针对并联式混合动力汽车,在电辅控制策略的基础上设计一种基于模糊逻辑推理的动力分配控制策略。将其模型嵌入仿真软件ADVISOR中,在CYC_EUDC循环道路工况下进行仿真计算。仿真结果表明:与电辅控制策略相比,采用模糊逻辑控制策略的混合动力电动汽车具有较好的经济性、排放性、鲁棒性,能够使发动机尽量集中工作在优化工作曲线附近,并保证电池SOC在较小的范围内变化。

PHEV对电池性能的要求——第22届国际电动车年会概述

介绍了第22届国际电动车年会有关可外接充电式混合动力汽车(PHEV)的研究情况,PHEV的发展状况、动力系统结构、工作模式,国外关于PHEV专用电池的开发情况;分析了PHEV对电池的能量密度、功率密度、寿命、成本等方面的性能要求。

基于模型匹配控制的PHEV动态协调控制方法

在分析发动机与电动机的动态响应特性基础上,设计基于模型匹配控制的整车动力系统动态协调控制方法,并在所建立的并联式混合动力汽车(PHEV)的前向仿真模型上对该方法进行研究,证明该方法能有效控制2个动力源的动力耦合过程,具有较高的转矩控制精度和很好的动态响应特性,并利用所建立的硬件在环仿真试验台对该方法进行试验验证。

PHEV动力总成系统的仿真分析研究

并联式混合动力汽车具有复杂的动力总成系统,作为一种重要的研究手段,计算机仿真技术在并联式混合动力车控制策略的研究中发挥越来越重要的作用。本文采用试验建模和理论建模相结合的方式,借助计算机仿真的手段,分析控制策略及控制效果,并确定控制参数,建立了基于MATLABSimulink的混合动力总成控制系统仿真软件和模型,并对其控制策略进行仿真研究,得到相应的仿真结果。

基于Cruise的PHEV动力总成集成控制研究

针对目前PHEV动力总成系统各部件协调控制的不足,对混合动力汽车多能源动力总成的集成控制进行了研究,提出了一种动力总成部件的系统集成控制方法。依据发动机、电机、蓄电池、动力耦合器等各部件电控单元ECU的属性及特点,构建了动力总成各主要部件ECU的模型,以此为基础搭建了集成系统的协调框架,利用系统ECU实时监测各动力部件ECU的工作状态和环境状态,结合逻辑门限阈值控制和状态流程图,判断总成系统的工作模式,并在Matlab/Simulink和Cruise环境下进行了控制策略的联合仿真验证。研究结果表明,PHEV动力总成的集成控制策略具有良好的控制效果,有效适应了外界工况的变化,改善了发动机的工作点并且降低了燃油消耗,解决了总成各部件的动力匹配与协调控制问题。

基于FCMAC神经网络的PHEV转矩分配策略

针对混合动力汽车转矩分配策略对整车的燃油经济性和排放性能具有重要的影响,提出了基于模糊小脑模型(FCMAC)神经网络控制的整车转矩分配方法.以某变速箱前置式双轴转矩耦合并联混合动力汽车为研究对象,建立了以发动机的输出转矩为目标的FCMAC神经网络控制策略,将输入量离散化后的高斯基模糊隶属度进行规则相乘后作为FCMAC神经网络的输入,经过hash映射及权值调整输出发动机转矩,实现了混合动力汽车的转矩分配.在AVL-Cruise及Matlab/Simulink平台上建立仿真模型,将其控制策略与基于逻辑门限值控制策略的仿真结果进行比较.结果表明:在NEDC工况下FCMAC控制策略较逻辑门限值控制策略,油耗与排放都有改善,提升了并联式混合动力汽车的燃油经济性和排放性.