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).

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.

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.

Novel flexible hybrid electric system and adaptive online-optimal energy management controller for plug-in hybrid electric vehicles

In order to achieve the improvement of the driving comfort and energy efficiency,an new e-CVT flexible full hybrid electric system(E2FHS) is proposed,which uses an integrated main drive motor and generator to take the place of the original automatic or manual transmission to realize the functions of continuously variable transmission(e-CVT).The design and prototype realization of the E2FHS system for a plug-in hybrid vehicle(PHEV) is performed.In order to analyze and optimize the parameters and the power flux between different parts of the E2FHS,simulation software is developed.Especially,in order to optimize the performance of the energy economy improvement of the E2FHS,the effect of the different energy management controllers is investigated,and an adaptive online-optimal energy management controller for the E2FHS is built and validated by the prototype PHEV.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

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.

Vehicle-to-grid power system services with electric and plug - in vehicles based on flexibility in unidirectional charging

With proper power scheduling and dynamic pricing,a unidirectional charger can provide benefits and regulation services to the electricity grid,at a level approaching that of bidirectional charging.Power scheduling and schedule flexibility of electric and plug-in hybrid vehicles are addressed.The use of electric vehicles(EVs)as flexibility resources and associated unidirectional vehicle-to-grid benefits are investigated.Power can be scheduled with the EV charger in control of charging or via control by a utility or an aggregator.Charging cost functions suitable for charger-and utility-controlled power scheduling are presented.Ancillary service levels possible with unidirectional vehicle-to-grid are quantified using sample charging scenarios from published data.Impacts of various power schedules and vehicle participation as a flexibility resource on electricity locational prices are evaluated.These include benefits to both owners and load-serving entities.Frequency regulation is considered in the context of unidirectional charging.

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.

Enhanced Deep Reinforcement Learning Strategy for Energy Management in Plug-in Hybrid Electric Vehicles with Entropy Regularization and Prioritized Experience Replay

Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.

基于GPS轨迹数据的电动出租车充电站选址规划

针对电动汽车保有量持续增长、充电设施匮乏难以满足用户需求的问题,提出一种基于GPS轨迹数据的电动出租车充电站选址规划方案。首先利用出租车GPS数据分析用户潜在充电需求并提取需求分布;其次提出一种基于网格密度分区的DBSCAN聚类方法,与传统算法相比DB指数由0.34降为0.30,对需求进行聚类和划分需求密集区,设置预选站址;最后,构建集合覆盖模型实现站址优化。利用此方案对北京大兴区出租车轨迹数据进行仿真,得出了合理的选址结果,即该方案可为电动出租车充电站规划提供参考。

基于运动学片段的纯电动出租车行驶特征模式挖掘

在逐步推行出租车全面电动化的背景下,针对目前对纯电动出租车行驶状态评估的不足,建立一种基于运动学片段的纯电动出租车行驶特征模式挖掘方法,研究纯电动出租车行驶状态特征.首先,基于行驶轨迹GPS数据,从速度特征、加减速和行驶状态3个方面,确定超速比例、加减速频率、行驶速度、怠速时间占比等13个特征指标刻画运动学片段,建立纯电动出租车运动学片段提取方法,研究纯电动出租车行驶状态特征.然后,根据行驶特征指标主成分的特征值大小及累积贡献率,确定关键特征指标,结合K-均值聚类算法,生成多时空场景下的纯电动出租车行驶特征模式,综合评价车辆行驶状态.最后,以深圳市共计9天采样间隔为1 s的700万条纯电动出租车GPS行驶轨迹数据为驱动,提取了1 757条纯电动出租车运动学片段.根据安全性、效率性和舒适性8个关键特征指标进行聚类分析,生成包含主干路、次干路和支路在早高峰、平峰和晚高峰9种时空场景下27类纯电动汽车行驶状态的特征模式库.研究结果表明:综合安全性、效率性、舒适性3方面,早高峰期间的纯电动出租车综合行驶状态优于平峰和晚高峰时段;基于运动学片段、主成分分析及多时空场景聚类分析的纯电动出租车行驶特征模式挖掘方法,能够有效反映并评估纯电动出租车行驶状态,并向驾驶员提供合理的驾驶建议.

A Predictive Energy Management Strategies for Mining Dump Trucks

The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).

基于延迟接受算法的电动出租车线上派单策略

为减少乘客等车时间,提升电动出租车系统的服务时间和运力.本文以出租车、充电站、订单和线上调度平台为研究对象,搭建离散事件驱动的车辆运营仿真场景.在此基础上,根据线上订单发起位置的不同,将订单分为2类:出发地预约订单和乘车地预约订单.同时建立评估乘客和司机之间的等待行为对出行体验和收益影响的指标,并提出1种基于延迟接受算法的电动出租车线上派单策略.以上海市黄浦区为例进行验证,结果表明:所提策略可减少乘客等车时间,提高电动出租车系统服务效率.

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles.While fuel economy is a key factor in the design of plug-in hybrid electric vehicles,a fuel economy label can educate customers about the economic advantage of purchasing a particular car.The fuel economy label of a PHEV consists of parameters like driving range,electrical energy consumption,fuel economy for city,highway,and combined use,battery recharge time,and fuel consumption rates.The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs.Firstly,the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model.The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model.The output variables simulated were engine size in litres;estimated driving range when the battery is fully charged in km,battery recharged time in hours,city fuel consumption(L/100 km),highway fuel consumption(L/100 km),combined fuel consumption(L/100 km),estimated driving range when the tank is full,carbon dioxide(CO_(2))emission in grams/km,electric motor power in kW,number of cylinders,and electrical charges consumed in kWh/100 km.Different cases of input variables were considered for the inverse function model.The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

Estimating optimal substitution scale of urban gasoline taxis by electric taxis in the era of green energy:a case study of Zhengzhou City

Electric Taxis(ETs)are the most favored alternatives to Gasoline Taxis(GTs)in cities that aim to reduce environmental pollution.How to develop a reasonable scale on which GTs are substituted by ETs remains a challenge to governments due to the dynamics and complexity of the taxi system.To address this challenge,this paper develops a discrete-event-based simulation framework to simulate participants in the system and estimate the results under different substitution scales,which are helpful to understanding the status changing law of entities under different substitution scales,such as the operating indices of ETs,the unsatisfied travel requirements of passengers,and the usage state of charging facilities.The framework abstracts the behavioral process of ETs into three elements,namely,entity,behavior,and event.The entities are constructed from the information derived from the trajectory data.The behaviors are defined by rules following behavioral logic under anxiety psychology,which is caused by the limited range of ETs.The events are triggered based on rules from reality.With the help of this framework,a multi-objective optimization model is developed to obtain the optimal substitution scale of GTs in the case study area of Zhengzhou City.Overall,the approach could provide a practical tool to address this challenge,which could support further studies of the effect of ETs on urban taxis.

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.

基于电动出租车数据的充电桩选址聚类方法比较

为有效降低出租车运营企业及经营者的经济成本,通过分析出租车的卫星轨迹数据,比较和选取用于电动出租车充电桩选址规划的聚类方法。以上海市电动出租车充电站的选址规划为研究对象,分别基于孤立森林和聚类算法设计异常值检测方法,对相关时段的出租车卫星数据进行清理以及数据可视化处理;比较层次聚类(Agglomerative Clustering)、高斯混合模型(Gaussian Mixture Model,GMM)、K-means聚类、Mean-Shift聚类以及谱聚类(Spectral Clustering)5种算法的聚类效果,并选取K-means算法作为充电桩选址规划参考算法。从城市区域划分及企业运营角度确定充电桩选址方案,为未来上海市区电动出租车充电桩的数量和容量配置提供设计依据。

Global Optimization‑Based Energy Management Strategy for Series–Parallel Hybrid Electric Vehicles Using Multi‑objective Optimization Algorithm

The study of series–parallel plug-in hybrid electric vehicles(PHEVs)has become a research hotspot in new energy vehicles.The global optimal Pareto solutions of energy management strategy(EMS)play a crucial role in the development of PHEVs.This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs.The algorithm combines the Radau Pseudospectral Knotting Method(RPKM)and the Nondominated Sorting Genetic Algorithm(NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle.The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM.The RPKM results serve as the fitness values in iteration through the NSGA-II method.The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87%improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM.The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality,with the added benefits of faster and more uniform solutions.

Improving reliability of distribution networks using plug-in electric vehicles and demand response

Nowadays,utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply.Different methodologies exist for utilities to improve the reliability of network.In this paper,demand response(DR)programs and smart charging/discharging of plug-in electric vehicles(PEVs)are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization(PSO)algorithm.Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction,urban air pollution and greenhouse gas emissions.Additionally,the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks.The reliability analysis is performed in terms of loss of load expectation(LOLE)and expected energy not served(EENS)indexes,where the characteristics contain load profile,load peak,voltage profile and energy loss.Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.