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.

An optimal energy management development for various configuration of plug-in and hybrid electric vehicle

Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.

Simulation research of energy management strategy for dual mode plug-in hybrid electrical vehicles

In this paper, a plug-in hybrid electrical vehicle(PHEV) is taken as the research object, and its dynamic performance and economic performance are taken as the research goals. Battery charge-sustaining(CS) period is divided into power mode and economy mode. Energy management strategy designing methods of power mode and economy mode are proposed. Maximum velocity, acceleration performance and fuel consumption are simulated during the CS period in the AVL CRUISE simulation environment. The simulation results indicate that the maximum velocity and acceleration time of the power mode are better than those in the economy mode. Fuel consumption of the economy mode is better than that in the power mode. Fuel consumption of PHEV during the CS period is further improved by using the methods proposed in this paper, and this is meaningful for research and development of PHEV.

Analysis of Hybrid Rechargeable Energy Storage Systems in Series Plug-In Hybrid Electric Vehicles Based on Simulations

In this paper, an extended analysis of the performance of different hybrid Rechargeable Energy Storage Systems (RESS) for use in Plug-in Hybrid Electric Vehicle (PHEV) with a series drivetrain topology is analyzed, based on simulations with three different driving cycles. The investigated hybrid energy storage topologies are an energy optimized lithium-ion battery (HE) in combination with an Electrical Double-Layer Capacitor (EDLC) system, in combination with a power optimized lithium-ion battery (HP) system or in combination with a Lithium-ion Capacitor (LiCap) system, that act as a Peak Power System. From the simulation results it was observed that hybridization of the HE lithium-ion based energy storage system resulted from the three topologies in an increased overall energy efficiency of the RESS, in an extended all electric range of the PHEV and in a reduced average current through the HE battery. The lowest consumption during the three driving cycles was obtained for the HE-LiCap topology, where fuel savings of respectively 6.0%, 10.3% and 6.8% compared with the battery stand-alone system were achieved. The largest extension of the range was achieved for the HE-HP configuration (17% based on FTP-75 driving cycle). HP batteries however have a large internal resistance in comparison to EDLC and LiCap systems, which resulted in a reduced overall energy efficiency of the hybrid RESS. Additionally, it was observed that the HP and LiCap systems both offer significant benefits for the integration of a peak power system in the drivetrain of a Plug-in Hybrid Electric Vehicle due to their low volume and weight in comparison to that of the EDLC system.

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).

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.

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.

基于Stacking融合模型的PHEV复合储能系统实时能量分配策略

为了解决插电式混合动力汽车单一电池低比功率、无法响应暂态功率需求的问题,设计了电池和超级电容并联式复合储能系统。同时针对采用动态规划法优化负载电流分配时缺乏实时性的问题,利用不同驱动工况下动态规划优化的结果构成训练集进行训练,并综合GRU网络以及XGBoost算法,提出了一种Stacking集成学习框架下多模型融合的能量分配策略。仿真结果表明,与仅使用单一电池的储能系统相比,基于Stacking融合模型的实时能量分配系统在UDDS和US06两种循环工况下,电池峰值电流分别降低了48.7%和50.8%,有效削弱了电池的峰值电流,提升了电池的整体性能。

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.

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.

Plug-In Vehicle Acceptance and Probable Utilization Behaviour

This paper presents a study undertaken to understand the plug-in vehicle acceptance and probable utilization behaviour in terms of charging habits and utility factor (probability of driving in electrical mode). A survey was designed to be answered via World Wide Web, throughout 3 months and only accessible to Portuguese inhabitants. The survey was composed by biographical and car ownership info, mobility patterns, awareness toward plug-in vehicle technologies, price premium and, finally, potential buyer’s attitudes regarding charging vehicles with electricity from the grid. An explanation of how each vehicle technology works in the case of a regular hybrid (HEV), a plug-in hybrid (PHEV) and a pure electric vehicle (EV) was provided. A total sample of 809 volunteers answered the survey, aged above 18 years old, 50% male and 50% female. The results allowed the estimation of the typical daily driving distance, the Utility Factor curve for plug-in hybrid future users, the charging preferences for future users of pure electric or plug-in hybrid vehicles and the necessary feebates to promote the market penetration of such technologies. Other correlations were also analyzed between driving patterns, type of owned car, price premium and the willingness to buy pure electric and plug-in hybrid vehicles. The main policy implications are that an increase of awareness campaigns is necessary if the government intends to support the plug-in electric vehicle technology widespread and a minimum of 5000 € investment per ton of avoided CO2 will be necessary in a year.

Plug-in混合动力汽车能量管理策略全局优化研究

应用动态规划全局优化算法,针对并联式Plug-in混合动力汽车在不同行驶里程下的能量管理策略进行了全局优化研究。结果表明:车辆的行驶里程小于55km时应使用电动机为主的能量管理策略,当车辆的行驶里程大于55km时应使用发动机为主的能量管理策略;在动态规划最优控制下,车辆行驶里程为55km时整车经济性能最佳,行驶里程小于110km时整车平均等价油耗为2.7L/100km,相对于原型车经济性提高了近58%。

Plug-In并联式混合动力汽车实时优化能量管理策略

能量管理策略是混合动力汽车的核心技术之一,其品质直接影响车辆的动力性、经济性和排放性能。首先制定了基于确定性规则的Plug-In并联式混合动力汽车能量管理策略;然后,为了提高车辆的燃油经济性,设计了电池能量观测单元,并对等效燃油消耗最小策略进行改进,提出了适用于Plug-In混合动力汽车的实时优化能量管理策略。研究结果表明,该能量管理策略显著提高了Plug-In并联式混合动力汽车的燃油经济性。

Plug-in混合动力汽车能量管理策略优化设计

应用PSAT前向仿真软件及矩阵分割全局优化算法,对并联式Plug-in混合动力汽车(PHEV)在不同电能消耗续驶里程(CDR)下的能量管理策略进行了优化设计研究。结果表明:PHEV电能消耗续驶里程越大,优化控制参数对整车经济性影响越小;小CDR优化控制参数的整车平均经济性能好于大CDR优化控制参数;利用优化设计得到的PHEV能量管理策略可以使整车平均等价油耗降至2.70 L/(100 km),相对于原型车经济性提高了近58%。

Plug-in混合动力汽车动力总成优化设计研究

应用PSAT前向仿真软件,建立了双离合器式并联PHEV仿真模型。在确定了PHEV整车性能约束条件并对动力总成主要部件进行了成本分析之后,对不同全电力续驶里程和动力电池类型的PHEV动力总成进行了优化。结果表明:动力电池设计容量对整车成本影响最大,而它主要取决于所要求的全电力续驶里程;随着所要求的全电力续驶里程的增大,所需电机最大输出功率升高,而发动机最大输出功率则降低。

Plug-in混合动力汽车动力系统参数匹配

以并联型Plug-in混合动力电动汽车(PHEV)为研究对象,提出了其动力驱动系统参数匹配的原则和实施方法.采用该方法对某型轿车动力总成参数进行了匹配,使用电动汽车仿真软件ADVISOR对整车性能进行仿真计算.仿真结果表明:Plug-in混合动力轿车动力性与原车相当,经济性与原车相比有很大提高,达到预期开发目标.

Plug-in混合动力汽车动力系统参数设计成本效益分析

基于成本效益分析,以年均使用成本和初始成本作为评价指标,建立插电式混合动力汽车(PHEV)动力系统设计分析模型,对PHEV动力系统纯电动续驶里程和能量储存装置参数的进行设计分析。以Prius为分析对象,结果显示当PHEV纯电动续驶里程等于汽车的日行驶里程时,节油量最大且年均成本最小,同时也得到了国内个人交通出行数据统计对PHEV设计的重要性。能量储存装置的容量、放电深度和功率对PHEV质量和初始成本均有较大影响,基于功率/能量比(RP/E)分析了能量储存装置参数对PHEV动力系统成本的影响。

基于ADVISOR二次开发的Plug-in HEV模糊控制研究

针对插电式混合动力电动汽车(PHEV)运行特点,提出了PHEV最优模糊控制策略。模糊控制器将发动机的工作点控制在发动机最小燃油消耗的高效率区间内,使发动机具有较好的燃油经济性。根据路况功率需求和蓄电池SOC,调节发动机给定功率,并对能量回收不足SOC下降的情况进行改进,实现电池SOC平衡控制。将建立的模糊控制模型嵌入ADVISOR仿真软件,进行二次开发,在不同工况下进行仿真实验。结果表明:PHEV最优模糊逻辑控制具有较好的鲁棒性,在能量回收不足工况下实现电池SOC平衡,且具有较好的燃油经济性。

Plug-in混合动力汽车能量管理策略研究

该文以能量流分配较复杂的插电式混合动力汽车(PHEV)为研究对象,设计出了一种以理想的SOC下降线为基础的模糊控制策略使汽车的需求转矩在发动机和电机之间合理分配,在满足汽车动力性的前提下,提高整车的燃油经济性和尾气排放性能。该策略尽可能地使用动力电池的电能,从整体上能够接近全局优化且能实时控制整车。仿真的结果与传统插电式混合动力的CD-CS策略对比,从结果来看排放性能和燃油经济性有了较大的提高。

基于PMP的Plug-in柴电混合动力汽车油耗与排放最优控制策略

综合考虑电池和SCR催化器在低温环境下的工作特性,针对低温下温度对Plug-in柴电混合动力汽车性能的影响,提出最短时间控制和燃油消耗最少问题。以SCR起燃温度和电池正常工作温度时间最短为优化目标,以电池温度、电池荷电状态(State of Charge,SOC)和SCR催化器温度为状态变量,利用极小值原理求得最优控制策略。通过仿真对比规则控制策略,分析了在不同低温条件下基于庞特里亚金极小值原理(Pontryagin’s Minimum Principle,PMP)的最短时间和最少油耗与排放优化控制策略对整车油耗和排放的影响。