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).

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.

OPTIMIZATION APPROACH FOR HYBRID ELECTRIC VEHICLE POWERTRAIN DESIGN

According to bench test results of fuel economy and engine emission for thereal power-train system of EQ7200HEV car. a 3-D performance map oriented quasi-linear model isdeveloped for the configuration of the powertrain components such as internal combustion engine,traction electric motor, transmission, main retarder and energy storage unit. A genetic algorithmbased on optimization procedure is proposed and applied for parametric optimization of the keycomponents by consideration of requirements of some driving cycles. Through comparison of numericalresults obtained by the genetic algorithm with those by traditional optimization methods, it isshown that the present approach is quite effective and efficient in emission reduction and fueleconomy for the design of the hybrid electric car powertrain.

ENERGY MANAGEMENT STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES

Energy management strategy (EMS) is the core of the real-time controlalgorithm of the hybrid electric vehicle (HEV). A novel EMS using the logic threshold approach withincorporation of a stand-by optimization algorithm is proposed. The aim of it is to minimize theengine fuel consumption and maintain the battery state of charge (SOC) in its operation range, whilesatisfying the vehicle performance and drivability requirements. The hybrid powertrain bench testis carried out to collect data of the engine, motor and battery pack, which are used in the EMS tocontrol the powertrain. Computer simulation model of the HEV is established in the MATLAB/Simulinkenvironment according to the bench test results. Simulation results are presented for behaviors ofthe engine, motor and battery. The proposed EMS is implemented for a real parallel hybrid carcontrol system and validated by vehicle field tests.

Hybrid Excited Permanent Magnet Machines for Electric and Hybrid Electric Vehicles

This paper reviews various hybrid excited(HE)machines from the perspective of location of PM and DC excitation,series/parallel connection of PM and DC excited magnetic fields,and 2D/3D magnetic fields,respectively.The advantages as well as drawbacks of each category are analyzed.Since an additional control degree,i.e.DC excitation,is introduced in the HE machine,the flux weakening control strategies are more complex.The flux weakening performance as well as efficiency are compared with different control strategies.Then,the potential to mitigate the risk of uncontrolled overvoltage fault at high speed operation is highlighted by controlling the field excitation.Since additional DC coils are usually required for HE machines compared with pure PM excitation,the spatial confliction inevitably results in electromagnetic performance reduction.Finally,the technique to integrate the field and armature windings with open-winding drive circuit is introduced,and novel HE machines without a DC coil are summarized.

Identification of dynamic model parameters for lithium-ion batteries used in hybrid electric vehicles

An electrical equivalent circuit model for lithium-ion batteries used for hybrid electric vehicles （HEV） is presented. The model has two RC networks characterizing battery activation and concentration polarization process. The parameters of the model are identified using combined experimental and extended Kalman filter （EKF） recursive methods. The open-circuit voltage and ohmic resistance of the battery are directly measured and calculated from experimental measurements, respectively. The rest of the coupled dynamic parameters, i.e. the RC network parameters, are estimated using the EKF method. Experimental and simulation results are presented to demonstrate the efficacy of the proposed circuit model and parameter identification techniques for simulating battery dynamics.

Regenerative braking control for hybrid electric vehicles under decelerating condition

The operating mode of a single shaft hybrid electric vehicle （SSHEV） in which the electric motor exerts negative torque on the shaft to imitate engine braking is analyzed. The method of determining the quantity of regenerative braking torque is proposed with the premise that the braking intensity required by the driver is satisfied. On this basis, factors that affect torque generated by the motor are listed, and how the battery＇ s temperature and state of charge （ SOC ） restrict and correct the braking torque is expounded. Finally, road test results show that the motor＇ s constant power or constant torque control is an effective way to recover the mechanical energy during decelerating.

Development of a Series Hybrid Electric Aircraft Pushback Vehicle: A Case Study

The work presented in this paper is a progression to previous research which developed an overcurrent-tolerant prediction model. This paper presents some of the modelling and development techniques used for the previous research, but more emphasis is placed on the requirements of the case study;whereby an aeroplane pushback tug is converted into a series Hybrid Electric Vehicle (HEV). An iterative design process enabled the traction motor, transmission, generator and battery pack parameters to be tailored for this vehicle’s unique duty cycle. A MATLAB/Simulink model was developed to simulate the existing internal combustion engine powertrain as well as the series HEV equivalent for comparative analysis and validation purposes. The HEV design was validated by comparing the simulation results to recorded real-world data collected from the existing vehicle (torque, speeds etc.). The HEV simulations provided greater fuel savings and reduced emissions over the daily duty cycle in comparison to the existing vehicle.

MODELING AND IMPLEMENTATION OF SPEED GOVERNOR FOR THE HYBRID ELECTRIC VEHICLE ENGINE

A speed control analysis for an in-line gasoline fueled internal combustion （IC） engine is presented for the purpose of alleviation of high frequency oscillations in engine revolutions. A dynamic cylinder-by-cylinder model is proposed, base on slider-crank mechanism, which is extended to develop a digital governor providing a high fidelity estimation of rotary speed oscillation for hybrid vehicle engines. A modified PID controller that P and I gain is placed in feedback path is also described for hybrid electric vehicle （HEV） engine speed regulation, By comparison between measured and estimated signals, it is demonstrated that a good agreement has been achieved and the governor behaves an excellent damping speed ripple.

Designing the cooling system of a hybrid electric vehicle with multi-heat source

In order to reduce the power consumption and meet the cooling demand of every heat source component, three kinds of multi-heat source cooling system schemes were designed base on the characteristic of power split hybrid electric vehicle （HEV）. Using the numerical simulation meth- od, the power system heat transfer model was built. By comparing the performance of three differ- ent schemes through the Simulink simulation, the best cooling system scheme was found. Base on characteristics of these cooling system structures, the reasonableness of the simulation results were analyzed and verified. The results showed that the cooling system designation based on the numerical simulation could describe the cooling system performance accurately. This method could simplify the design process, improve design efficiency and provide a new way for designing a multi-heat source vehicle cooling system.

Different human-simulated characteristics of the driver models in the forward simulation of hybrid electric vehicles

Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles （HEV）. The impact to the vehicle speed tracking and the fuel economy is compared among the different driver models. The different human-simulated characteristics of the driver models are emphatically analyzed. The analysis results indicate that the driver models based on PID, simple fuzzy logic and expert PID are corresponding to the handling characteristics of different drives. The driver models of different human-simulated characteristics bring the handling divergence of drivers with different driving level and habit to the HEV forward simulation, and that is significant to the all-around verification and validation of the control strategy for HEV. System simulation results of different driver models validate the impact of driver models to the dynamic and fuel economy performance of HEV.

Development of High Torque and High Power Density Hybrid Excitation Flux Switching Motor for Traction Drive in Hybrid Electric Vehicles

This paper presents design feasibility study and development of a new hybrid excitation flux switching motor (HEFSM) as a contender for traction drives in hybrid electric vehicles (HEVs). Initially, the motor general construction, the basic working principle and the design concept of the proposed HEFSM are outlined. Then, the initial drive performances of the proposed HEFSM are evaluated based on 2D-FEA, in which the design restrictions, specifications and target performances are similar with conventional interior permanent magnet synchronous motor (IPMSM) used in HEV. Since the initial results fail to achieve the target performances, deterministic design optimization approach is used to treat several design parameters. After several cycles of optimization, the proposed motor makes it possible to obtain the target torque and power of 333 Nm and 123 kW, respectively. In addition, due to definite advantage of robust rotor structure of HEFSM, rotor mechanical stress prediction at maximum speed of 12,400 r/min is much lower than the mechanical stress in conventional IPMSM. Finally, the maximum torque and power density of the final design HEFSM are approximately 11.41 Nm/kg and 5.55 kW/kg, respectively, which is 19.98% and 58.12% more than the torque and power density in existing IPMSM for Lexus RX400h.

A Study of Advanced Efficient Hybrid Electric Vehicles, Electric Propulsion and Energy Source

Recently global warming and the depletion of fuel resources have accelerated researchers’ efforts to produce more efficient and clean alternatives. This research presents a comprehensive review of the different adjustments/con figurations of electric vehicles (EVs) and hybrid electric vehicles (HEVs), traction motors for power systems, and wireless speed control of traction drive. Electronic installation of technology can reduce pollution efficiently and effectively. The efficient operation has always been one of the most common investigators’ objectives in the automotive industry and academic areas. There are several renewable energy resources for hybrid vehicles that will replace depleted gasoline worldwide. The purpose of this paper is the development of more efficient pure EVs, HEVs, and fuel cell electric vehicles (FCEV) present both a challenge and a definite solution to current mobility issues. Fuel consumption in cars is a concern due to the harmful effects on the environment. Among other battery sources, fuel cells (FC), super capacitors (SC), and photovoltaic cells are studied for vehicle application. A combination of these renewable energy sources can be used for hybrid electric vehicles (HEV) in the next generation of transportation. With the significant progress of automobile technology, the hybrid electric vehicle has already become the main achievement of transportation electrification due to its excellent fuel-saving performance.

驾驶风格和路况对HEV 能耗影响分析

基于驾驶风格和路况影响因素,从整车能量效率及需求功率两个角度出发,进一步分析HEV在不同运行场景下的能耗差异。数据分析在市区、郊区路况下驾驶风格随着激进性强度的增加,具有更低的整车能量效率以及更大的需求功率,在高速路况下,需求功率差异较小,能耗差异仅由能量效率差异导致。结果表明,由驾驶风格导致的整车能耗在市区、郊区、高速路况下最大差异为37.3%、37.4%及14%,同样的路况导致的整车能耗在冷静型、一般型、激进型下最大差异为43.4%、50.2%以及58.8%。

基于KL散度工况识别的混合动力汽车队列的分层控制

针对混合动力汽车队列行驶过程中工况的适应性问题,提出了一种基于KL(Kullback-Leibler)散度工况识别的分层控制方法。上层控制器利用车—车通信技术,获取队列中前车状态信息,采用模型预测控制(MPC)算法,实现队列纵向控制,并计算出最优跟车车速;下层控制器基于典型工况,离线求解需求功率的转移概率矩阵,并通过Q-Learning算法训练最优Q表嵌入整车模型中;在行驶中以固定时间长度在线更新转移概率矩阵,采用KL散度进行工况识别,根据识别的工况类型,结合当前时刻车速、需求功率和电池荷电状态(SOC),通过在线查表实现转矩分配。结果表明:与未考虑工况识别策略相比,本策略的油耗降低了8.6%;与作为基准的动态规划(DP)相比,增加了4.8%;在与DP油耗基本保持相同的前提下,该策略离线仿真时间缩短21%,不仅能够在线应用,还能实时适应工况变化。

Design and optimization of equivalent consumption minimization strategy for 4WD hybrid electric vehicles incorporating vehicle connectivity

This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.

Real-time optimization power-split strategy for hybrid electric vehicles

Energy management strategies based on optimal control theory can achieve minimum fuel consumption for hybrid electric vehicles, but the requirement for driving cycles known in prior leads to a real-time problem. A real-time optimization power-split strategy is proposed based on linear quadratic optimal control. The battery state of charge sustainability and fuel economy are ensured by designing a quadratic performance index combined with two rules. The engine power and motor power of this strategy are calculated in real-time based on current system state and command, and not related to future driving conditions. The simulation results in ADVISOR demonstrate that, under the conditions of various driving cycles, road slopes and vehicle parameters, the proposed strategy significantly improves fuel economy, which is very close to that of the optimal control based on Pontryagin's minimum principle, and greatly reduces computation complexity.

Powertrain control of a solar photovoltaic-battery powered hybrid electric vehicle

This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simula-tion and experimental studies. The simulation and experimental results are presented to validate the proposed technique.

HEV再生制动时电池快速充电模糊控制策略

为了提高混合动力汽车(HEV)再生制动时蓄电池的充电效率,保证蓄电池的使用安全,在分析蓄电池充电过程热交换模型的基础上,建立了电池开路电压-内阻模型与充电效率间的数学关系.然后,基于马斯定律,设计了适用于HEV再生制动时电池快速充电模糊控制算法.在Mat-lab环境下搭建了闭环控制系统仿真模型,通过建模与仿真计算出HEV在不同控制策略下的电能回收率.结果表明在相同制动情况下,设计的快速充电模糊控制策略与限流充电控制策略相比,电能回收率增加了8.41%.

基于工况识别的HEV自适应能量管理算法

提出了自适应能量管理算法,通过简化的神经网络对实时车速进行采集、分析和比较,在运行一段时间后,自动寻找出与之相近的标准循环工况,控制参数也相应转换为标准工况的已优化参数.基于CRUISE软件进行建模实现,选取城市客车循环工况进行仿真,分析结果表明,多工况自适应整车管理算法能够较好地根据车速对工况进行分类识别,并达到在保证混合动力汽车(HEV)电量平衡基础上节约燃油的目的.