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).

Cycle life prediction and match detection in retired electric vehicle batteries

The lifespan models of commercial 18650-type lithium ion batteries （nominal capacity of 1150 mA-h） were presented. The lifespan was extrapolated based on this model. The results indicate that the relationship of capacity retention and cycle number can be expressed by Gaussian function. The selecting function and optimal precision were verified through actual match detection and a range of alternating current impedance testing. The cycle life model with high precision （〉99%） is beneficial to shortening the orediction time and cutting the prediction cost.

Development and Verification of the Equilibrium Strategy for Batteries in Electric Vehicles

The inconsistency of the cells in a battery pack can affect its lifespan,safety and reliability in the electric vehicles. The balanced system is an effective technique to reduce its inconsistency and improve the operating performance. A hybrid equilibrium strategy based on decision combing battery state-of-charge（ SOC） and voltage has been proposed. The battery SOC is estimated through an improved least squares method. An equalization hardware in loop（ HIL） platform has been constructed. Based on this HIL platform,equilibrium strategy has been verified under the constant-current-constant-voltage（ CCCV） and dynamicstresstest（ DST） conditions. Experimental results indicate that the proposed hybrid equalization strategy can achieve good balance effect and avoid the overcharge and over-discharge of the battery pack at the same time.

Towards Realistic Vibration Testing of Large Floor Batteries for Battery Electric Vehicles (BEV)

This contribution shows an analysis of vibration measurement on large floor-mounted traction batteries of Battery Electric Vehicles(BEV).The focus lies on the requirements for a realistic replication of the mechanical environments in a testing laboratory.Especially the analysis on global bending transfer functions and local corner bending coherence indicate that neither a fully stiff fixation of the battery nor a completely independent movement on the four corners yields a realistic and conservative test scenario.The contribution will further show what implication these findings have on future vibration&shock testing equipment for large traction batteries.Additionally,it will cover an outlook on how vibration behavior of highly integrated approaches(cell2car)changes the mechanical loads on the cells.

The Electric Vehicle beyond Transport： Contexts for Meanings and Services Related to Batteries on Wheels

This paper explores the potential meanings of battery electric vehicles （battery EVs）. Relevant ideas were collated through facilitated exchange of explicated and tacit knowledge, realized by individual essay preparation and a facilitated seminar workshop. Additional classifications and clustering by the author led to the following principal results： the EV as a power source and buffer forms the foundation for most meanings beyond transport. EVs can act both in the context of＂shelters＂ for individuals as well as ＂community vehicles＂ with a focus on, e.g., local renewable energy production integration. Reduced to a simple product, EVs can also be designed to make sense in developing country environments. However, many ＂intelligent＂ features associated to EVs are available also for combustion engine vehicles and thus provide only necessary, but not unique added value to EVs. Concluding, EVs will take over market share from internal combustion vehicles only if they satisfy human needs beyond mobility.

Electrochemical performance of a nickel-rich LiNi0.6Co0.2Mn0.2O2 cathode material for lithium-ion batteries under different cut-off voltages

A spherical-like Ni0.6Co0.2Mn0.2(OH)2precursor was tuned homogeneously to synthesize LiNi0.6Co0.2Mn0.2O2as a cathode material for lithium-ion batteries. The effects of calcination temperature on the crystal structure, morphology, and the electrochemical performance of the as-prepared LiNi0.6Co0.2Mn0.2O2were investigated in detail. The as-prepared material was characterized by X-ray diffraction, scanning electron microscopy, laser particle size analysis, charge–discharge tests, and cyclic voltammetry measurements. The results show that the spherical-like LiNi0.6Co0.2Mn0.2O2material obtained by calcination at 900°C displayed the most significant layered structure among samples calcined at various temperatures, with a particle size of approximately 10 μm. It delivered an initial discharge capacity of 189.2 mAh•g−1at 0.2C with a capacity retention of 94.0% after 100 cycles between 2.7 and 4.3 V. The as-prepared cathode material also exhibited good rate performance, with a discharge capacity of 119.6 mAh•g−1at 5C. Furthermore, within the cut-off voltage ranges from 2.7 to 4.3, 4.4, and 4.5 V, the initial discharge capacities of the calcined samples were 170.7, 180.9, and 192.8 mAh•g−1, respectively, at a rate of 1C. The corresponding retentions were 86.8%, 80.3%, and 74.4% after 200 cycles, respectively. © 2017, University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg.

Nanocarbons and their hybrids as catalysts for non-aqueous lithium–oxygen batteries

Rechargeable lithium-oxygen (Li–O2) batteries have been considered as the most promising candidates for energy storage and conversion devices because of their ultra high energy density. Until now, the critical scientific challenges facing Li–O2batteries are the absence of advanced electrode architectures and highly efficient electrocatalysts for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), which seriously hinder the commercialization of this technology. In the last few years, a number of strategies have been devoted to exploring new catalysts with novel structures to enhance the battery performance. Among various of oxygen electrode catalysts, carbon-based materials have triggered tremendous attention as suitable cathode catalysts for Li–O2batteries due to the reasonable structures and the balance of catalytic activity, durability and cost. In this review, we summarize the recent advances and basic understandings related to the carbon-based oxygen electrode catalytic materials, including nanostructured carbon materials (one-dimensional (1D) carbon nanotubes and carbon nanofibers, 2D graphene nanosheets, 3D hierarchical architectures and their doped structures), and metal/metal oxide-nanocarbon hybrid materials (nanocarbon supporting metal/metal oxide and nanocarbon encapsulating metal/metal oxide). Finally, several key points and research directions of the future design for highly efficient catalysts for practical Li–O2batteries are proposed based on the fundamental understandings and achievements of this battery field. © 2016 Science Press

Customized Optimization for Vehicle Acoustic Statistical Energy Analysis

Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).

RE-Ni Based Alloys Used in Ni Hydride Batteries

The characteristics of LaNi5 and RE-Ni base alloys were systematically reviewed. The hydrogen-storage electrode materials made of RE-Ni base alloys, the operating characteristics of hydrogen-storage electrodes and the operating principle of Ni-hydride batteries were described in detail. Besides, the methods for preparing RE-Ni base alloys were outlined.

Driving and Braking Control of PM Synchronous Motor Based on Low-resolution Hall Sensor for Battery Electric Vehicle

Resolvers are normally employed for rotor positioning in motors for electric vehicles, but resolvers are expensive and vulnerable to vibrations. Hall sensors have the advantages of low cost and high reliability, but the positioning accuracy is low. Motors with Hall sensors are typically controlled by six-step commutation algorithm, which brings high torque ripple. This paper studies the high-performance driving and braking control of the in-wheel permanent magnetic synchronous motor （PMSM） based on low-resolution Hall sensors. Field oriented control （FOC） based on Hall-effect sensors is developed to reduce the torque ripple. The positioning accuracy of the Hall sensors is improved by interpolation between two consecutive Hall signals using the estimated motor speed. The position error from the misalignment of the Hall sensors is compensated by the precise calibration of Hall transition timing. The braking control algorithms based on six-step commutation and FOC are studied. Two variants of the six-step commutation braking control, namely, half-bridge commutation and full-bridge commutation, are discussed and compared, which shows that the full-bridge commutation could better explore the potential of the back electro-motive forces （EMF）, thus can deliver higher efficiency and smaller current ripple. The FOC braking is analyzed with the phasor diagrams. At a given motor speed, the motor turns from the regenerative braking mode into the plug braking mode if the braking torque exceeds a certain limit, which is proportional to the motor speed. Tests in the dynamometer show that a smooth control could be realized by FOC driving control and the highest efficiency and the smallest current ripple could be achieved by FOC braking control, compared to six-step commutation braking control. Therefore, FOC braking is selected as the braking control algorithm for electric vehicles. The proposed research ensures a good motor control performance while maintaining low cost and high reliability.

Intelligent Vehicle Electrical Power Supply System with Central Coordinated Protection

The current research of vehicle electrical power supply system mainly focuses on electric vehicles（EV） and hybrid electric vehicles（HEV）.The vehicle electrical power supply system used in traditional fuel vehicles is rather simple and imperfect;electrical/electronic devices（EEDs） applied in vehicles are usually directly connected with the vehicle＇s battery.With increasing numbers of EEDs being applied in traditional fuel vehicles,vehicle electrical power supply systems should be optimized and improved so that they can work more safely and more effectively.In this paper,a new vehicle electrical power supply system for traditional fuel vehicles,which accounts for all electrical/electronic devices and complex work conditions,is proposed based on a smart electrical/electronic device（SEED） system.Working as an independent intelligent electrical power supply network,the proposed system is isolated from the electrical control module and communication network,and access to the vehicle system is made through a bus interface.This results in a clean controller power supply with no electromagnetic interference.A new practical battery state of charge（So C） estimation method is also proposed to achieve more accurate So C estimation for lead-acid batteries in traditional fuel vehicles so that the intelligent power system can monitor the status of the battery for an over-current state in each power channel.Optimized protection methods are also used to ensure power supply safety.Experiments and tests on a traditional fuel vehicle are performed,and the results reveal that the battery So C is calculated quickly and sufficiently accurately for battery over-discharge protection.Over-current protection is achieved,and the entire vehicle＇s power utilization is optimized.For traditional fuel vehicles,the proposed vehicle electrical power supply system is comprehensive and has a unified system architecture,enhancing system reliability and security.

A Vehicle Routing Problem Based on Intelligent Batteries Transfer Management for the EV Network

Batteries transfer management is one important aspect in electric vehicle(EV)network's intelligent operation management system.Batteries transfer is a special and much more complex VRP(Vehicle Routing Problem) which takes the multiple constraints such as dynamic multi-depots,time windows,simultaneous pickups and deliveries,distance minimization,etc.into account.We call it VRPEVB(VRP with EV Batteries).This paper,based on the intelligent management model of EV's battery power,puts forward a battery transfer algorithm for the EV network which considers the traffic congestion that changes dynamically and uses improved Ant Colony Optimization.By setting a reasonable tabv range,special update rules of the pheromone and path list memory functions,the algorithm can have a better convergence,and its feasibility is proved by the experiment in an EV's demonstration operation system.

Network traffic flow evolution with battery electric vehicles and conventional gasoline vehicles

In order to investigate the effect of the use of battery electric vehicles on traffic dynamics,the valid paths of electric battery vehicles are defined and a check-based method is proposed to obtain them.Then,assuming that travelers only focus on their past travel experience,a day-to-day traffic assignment model is established based on reinforcement learning and bounded rationality.In the proposed model,the Bush-Mosteller model,a reinforcement learning model,is modified to calculate path choice probability according to bounded rationality.The modified model updates the path choice probability only if the gap between expected travel time and perceived travel time is beyond the cognitive threshold.Numerical experiments validate the effectiveness of the model and show that traffic flows can converge to the equilibrium in any case of cognitive thresholds and penetration rates of battery electric vehicles.The cognitive threshold has a positive influence on the variation of traffic flows while it has a negative influence on the differences between traffic flows.The adaptation of battery electric vehicles leads to the poor performance of the traffic system.

Equalization Charging and Protection System for Electric Vehicle

A system is developed to improve the series battery packs uniformities and charging protection and the implementation of battery equalization charging and protection system is also introduced. The functions of equalization charging and overcharging protection are analyzed and the control model of series battery packs equalization charging is setup. The diverting-current and feedback bus voltage are measured during the series Li-ion battery packs equalization charging experiment. The field operation on Electric luxury transit bus BFC6100EV shows that the system betters the battery series charging uniformities and overcharging protection, improves the battery performance and extends the battery life.

A Design of the Automatic Power-off after the Storage Battery of Electric Bicycle Is Fully Charged

As the service life of the electric bicycle＇ s storage battery is shortened due to the long-term floating charge, an automatic power- off circuit for the storage battery of electric bicycle is designed, and also the composition and design of the circuit are specifically expounded. After a test, the circuit can achieve a desired effect. Therefore, it can prolong the service life of the electric bicycle＇ s storage battery and save electric energy in the actual applications.

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.

Performance Study of the MPC based on BPNN Prediction Model in Thermal Management System of Battery Electric Vehicles

In this paper,a model predictive control(MPC)based on back propagation neural network(BPNN)prediction model was proposed for compressor speed control of air conditioning system(ACS)and battery thermal management system(BTMS)coupling system of battery electric vehicle(BEV).In order to solve the problem of high cooling energy consumption and inferior thermal comfort in the cabin of the battery electric vehicle thermal management system(BEVTMS)during summer time,this paper combines the respective superiorities of artificial neural network(ANN)predictive modeling and MPC,and creatively combines the two methods and uses them in the control of BEVTMS.Firstly,based on ANN and heat transfer theory,BPNN prediction model,ACS and BTMS coupling system were established and verified.Secondly,a mathematical method of MPC was established to control the speed of the compressor.Then,the state parameters of the coupled system were predicted using a BPNN prediction model,and the predicted values were passed to the MPC,thus achieving accurate control of the compressor speed using the MPC.Finally,the effects of PID control and MPC based on BPNN prediction model on thermal comfort of cabin and compressor energy consumption at different ambient temperatures were compared in simulation under New European Driving Cycle(NEDC)conditions.The results showed for the constructed BPNN prediction model predicted and tested values of the selected parameters the mean squared error(MSE)ranged from 2.498%to 8.969%,mean absolute percentage error(MAPE)ranged from 4.197%to 8.986%,and mean absolute error(MAE)ranged from 3.202%to 8.476%.At ambient temperatures of 25℃,35℃ and 45℃,the MPC based on the BPNN prediction model reduced the cumulative discomfort time in the cabin by 100 s,39 s and 19 s,respectively,compared with the PID control.Under three NEDC conditions,the energy consumption is reduced by 1.82%,2.35%and 3.48%,respectively.When the ambient temperature was 35℃,the MPC based on BPNN prediction model can make the ACS and BTMS coupling system have better thermal comfort,and the energy saving effect of the compressor was more obvious with the temperature.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

A Nonlinear Observer Approach of SOC Estimation Based on Hysteresis Model for Lithium-ion Battery

In this paper, a state of charge U+0028 SOC U+0029 estimation approach for lithium-ion battery based on equivalent circuit model and the input-to-state stability U+0028 ISS U+0029 theory has been proposed. According to the electrochemical performance of lithiumion battery, the equivalent circuit model with two RC networks is established, which includes hysteresis characteristic in inner electrochemical response process. The nonlinear relation between open circuit voltage U+0028 OCV U+0029 and SOC is obtained from a rapid test. Exponential fitting method is used to identify the parameters of the model. A novel state observer based on ISS theory is designed for lithium-ion battery SOC estimation. The designed observer is tested on AMESim and Simulink co-simulation. The simulation results show that the proposed method has a high SOC estimation accuracy with an error of about 2 percent. © 2017 Chinese Association of Automation.

LiPF6 and lithium difluoro(oxalato)borate/ethylene carbonate+dimethyl carbonate+ethyl(methyl)carbonate electrolyte for LiNi0.5Mn1.5O4 cathode

LiODFB electrolyte’s compatibility with LiNi0.5Mn1.5O4high-voltage cathode material was studied by cyclic voltammetry, charge-discharge test and AC impedance. The results show that at 25 and 60 °C, the LiODFB-based electrolyte has better electrochemical stability than LiPF6. AC impedance plots show that the LiODFB battery has a lower charge-transfer resistance than LiPF6battery at 60 °C, which indicates that LiODFB battery has excellent cycling performance at high temperature. At 25 and 60 °C, the LiNi0.5Mn1.5O4/Li half cells with LiODFB or LiPF6as electrolyte all have simple redox peak, showing that each of them has an excellent reversibility. LiODFB battery has better cycle performance than LiPF6battery at 25 °C and 60 °C. At 25 °C, their 0.5C initial discharge specific capacities are 126.3 and 131.6 mA·h/g, and their capacity retention ratios of the 100th cycle are 97.1% and 94.7%, respectively. At 60 °C, their 0.5C initial discharge specific capacities are 132.6 and 129.1 mA·h/g, and their capacity retention ratios of the 100th cycle are 94.1% and 81.7%, respectively. © 2017, Central South University Press and Springer-Verlag GmbH Germany.