Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Using machine learning to identify primary features in choosing electric vehicles based on income levels

An electric vehicle is becoming one of the popular choices when choosing a vehicle.People are generally impressed with electric vehicles’zero-emission and smooth drives,while unstable battery duration keeps people away.This study tries to identify the primary factors that affect the likelihood of owning an electric vehicle based on different income levels.We divide the dataset into three subgroups by household income from$50,000 to$150,000 or low-medium income level,$150,000 to$250,000 or medium-high income level,and$250,000 or above,the high-income level.We considered several machine learning classifiers,and naive Bayes gave us a relatively higher accuracy than other algorithms in terms of overall accuracy and F1 scores.Based on the probability analysis,we found that for each of these groups,one-way commuting distance is the most important for all three income levels.

Development and simulation of electric vehicle based on ADVISOR

Based on the electric vehicle simulator ADVISOR（ advanced vehicle simulator）, the electric vehicle which has a wheel driving system was developed and named ELVEC. The ELVEC consists of wheel, axle, body, motor/controller, energy storage, power bus, etc. The acceleration, grading, driving speed and fuel economy of the ELVEC are analyzed. The results show that the ELVEC has good dynamic performance and fuel economy. It is suitable for the driving conditions of the start-accelerate-stop and the low speed driving conditions in urban areas. At the same time, the motor performance, energy storage （batteries） and energy management of the ELVEC are simulated. It is concluded that the efficiencies of the motor, batteries and driveline are high, and the energy management and the fuzzy logic control strategy are efficient.

Development of Management Systems for Electric Vehicle Battery Series

A kind of management system for electric vehicle (EV) battery series was developed. The system can predict residual capacity for EV battery series and mileages. The system can determine if it is necessary for the battery series to be charged. The system can determine which battery is necessary to be updated for the reason of damage or aging. The system can display the total voltage of battery series, extreme voltage and temperature of every battery in the series. The system can display the accumulative discharge for every battery in the series. The system can alarm when both total or extreme voltage is at low level, or temperature of a battery in the series is at high level. The system provided with a microprocessor as key part can collect and record signal of charging and discharging current, total voltage, extreme voltage and temperature for every battery. The mathematical model of residual capacity for EV lead acid batteries was discussed in details. The system operates well in the laboratory and meets the requirement.

Design and Development of the DTC Induction Motor Drive for Electric Vehicle

The design and development of the traction controller for electric vehicle is introduced, which is based on the induction motor. This drive is developed by using a digital signal processor at low cost and carried out with the module design concept of both software and hardware. Nevertheless, a scheme of the sensorless direct torque control is based on the developed hardware, of which the feasibility is tested by a trial program. Additionally, both the interface function of the drive hardware and the feasibility of its software are proved to be good by the trail programs. A test motor can run about 18?r/min by a variable frequency program with the space vector pulse width modulation technology, of which the torque is visible pulsatile. In this presentation, based on the theoretical approach, the sensorless torque control is to be studied and applied to electric vehicles, of which the quick, smooth and stable torque response is emphasized because it quite benefits improving the drive performance of electric vehicles.

Research and development of electric vehicles for clean transportation

This article presents the research and development of an electric vehicle（EV） in Department of Human-Robotics Saitama Institute of Technology,Japan.Electric mobile systems developed in our laboratory include a converted electric automobile,electric wheelchair and personal mobile robot.These mobile systems contribute to realize clean transportation since energy sources and devices from all vehicles,i.e.,batteries and electric motors,does not deteriorate the environment.To drive motors for vehicle traveling,robotic technologies were applied.

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.

A comprehensive review on hybrid electric vehicles: architectures and components

The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

Review of optimized layout of electric vehicle charging infrastructures

Electric vehicle is a kind of new energy vehicle which uses batteries as energy supply unit.A huge gap in charging infrastructures will be created by the expansion of electric vehicles.The effectiveness and rationality of charging facilities will directly affect the convenience and economy of the users,as well as the safe operation of the power grid.Three types of charging facilities:charging pile,charging station and battery swap station are introduced in this paper.According to the different methods of charging infrastructure planning,the research status of the method of determining charging demand points is expounded.And the spatial distribution of charging demand points extracted by the current site selection method has a certain deviation.Then the models and algorithms of charging infrastructure optimized layout are reviewed.Currently,many researches focus on three categories optimization objectives:benefit of power company side,investment cost of charging facility and user side cost,and the genetic algorithm and particle swarm optimization are the main solving algorithms.Finally,the relative methods and development trend of the charging infrastructures optimized layout are summarized,and some suggestions on the optimized layout of electric vehicle charging infrastructures are given forward.

Decentralized Dynamic Event-Triggered Communication and Active Suspension Control of In-Wheel Motor Driven Electric Vehicles with Dynamic Damping

This paper addresses the co-design problem of decentralized dynamic event-triggered communication and active suspension control for an in-wheel motor driven electric vehicle equipped with a dynamic damper. The main objective is to simultaneously improve the desired suspension performance caused by various road disturbances and alleviate the network resource utilization for the concerned in-vehicle networked suspension system. First, a T-S fuzzy active suspension model of an electric vehicle under dynamic damping is established. Second,a novel decentralized dynamic event-triggered communication mechanism is developed to regulate each sensor's data transmissions such that sampled data packets on each sensor are scheduled in an independent manner. In contrast to the traditional static triggering mechanisms, a key feature of the proposed mechanism is that the threshold parameter in the event trigger is adjusted adaptively over time to reduce the network resources occupancy. Third, co-design criteria for the desired event-triggered fuzzy controller and dynamic triggering mechanisms are derived. Finally, comprehensive comparative simulation studies of a 3-degrees-of-freedom quarter suspension model are provided under both bump road disturbance and ISO-2631 classified random road disturbance to validate the effectiveness of the proposed co-design approach. It is shown that ride comfort can be greatly improved in either road disturbance case and the suspension deflection, dynamic tyre load and actuator control input are all kept below the prescribed maximum allowable limits, while simultaneously maintaining desirable communication efficiency.

Location of Electric Vehicle Charging Station Based on Spatial Clustering and Multi-hierarchical Fuzzy Evaluation

For the charging station construction of electric vehicle,location selecting is a key issue.There are two problems in location selection of the electric vehicle charging station.One is determining the location of charging station;the other is evaluating the location of charging station.To determine the charging station location,an spatial clustering algorithm is proposed and programmed.The example simulation shows the effectiveness of the spatial clustering algorithm.To evaluate the charging station location,a multi-hierarchical fuzzy method is proposed.Based on the location factors of electric vehicle charging station,the hierarchical evaluation structure of electric vehicle charging station location is constructed,including three levels,4first-class factors and 14second-class factors.The fuzzy multi-hierarchical evaluation model and algorithm are built.The analysis results show that the multi-hierarchical fuzzy method can reasonably complete the electric vehicle charging station location evaluation.

Alternating current heating techniques for lithium-ion batteries in electric vehicles:Recent advances and perspectives

The significant decrease in battery performance at low temperatures is one of the critical challenges that electric vehicles(EVs)face,thereby affecting the penetration rate in cold regions.Alternating current(AC)heating has attracted widespread attention due to its low energy consumption and uniform heating advantages.This paper introduces the recent advances in AC heating from the perspective of practical EV applications.First,the performance degradation of EVs in low-temperature environments is introduced briefly.The concept of AC heating and its research methods are provided.Then,the effects of various AC heating methods on battery heating performance are reviewed.Based on existing studies,the main factors that affect AC heating performance are analyzed.Moreover,various heating circuits based on EVs are categorized,and their cost,size,complexity,efficiency,reliability,and heating rate are elaborated and compared.The evolution of AC heaters is presented,and the heaters used in brand vehicles are sorted out.Finally,the perspectives and challenges of AC heating are discussed.This paper can guide the selection of heater implementation methods and the optimization of heating effects for future EV applications.

Innovation and Firm Co-ownership Network in China’s Electric Vehicle Industry

Firms are embedded in complex networks,where diverse ideas combine and generate new ideas.Shareholders of firms are of-ten seen as critical external resources that have significant influence on firm innovation.The current literature tends to focus on the rela-tionship between firms and their shareholders,while paying less attention to the connections between firms with the same shareholders.This article identifies two types of network spillover effects,intra-city network effect and inter-city network effect,by visualizing the co-ownership networks in China’s electric vehicle(EV)industry.We find that firms with the same shareholders,which are defined as co-owned EV firms,are more innovative than non-co-owned ones.Furthermore,there are two dominant types of firm co-ownership ties formed by corporate and financial institution shareholders.While corporate shareholders help exploiting local tacit knowledge,financial institutions are more active in bridging inter-city connections.The conclusion is confirmed at both firm and city levels.This paper theor-izes the firm co-ownership network as a new form of institutional proximity and tested the result empirically.For policy consideration,we have emphasized the importance of building formal or informal inter-firm network,and the government should further enhance the knowledge flow channel by institutional construction.

Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric vehicles.The demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of recharge.The establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this issue.Nevertheless,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of service.Modelling the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging infrastructure.It is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic patterns.When vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering system.Initially,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature selection.Subsequently,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic congestion.Based on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is provided.The results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algorithm(GA),to the theoretical median that would be expected that there is no difference.

Fuzzy Adaptive State Estimation of Distributed Drive Electric Vehicles with Random Missing Measurements and Unknown Process Noise

Accurate estimation of sideslip angle and vehicle velocity is crucial for effective control of distributed drive electric vehicles.However,as these states are not directly measured,Kalman-based approaches utilizing in-vehicle sensors have been developed to estimate them.Unfortunately,existing methods tend to ignore the impact of data loss on estimation performance.Furthermore,the process noise,which changes dynamically due to varying driving conditions,is not adequately considered.In response to these constraints,we propose a novel method called the fuzzy adaptive fault-tolerant extended Kalman filter(FAFTEKF).Initially,a fault-tolerant EKF is devised to handle missing measurements.Additionally,a fuzzy logic system that dynamically updates the process noise matrix,is built to improve estimation accuracy under different driving conditions.Extensive experimental results validate the superiority of the FAFTEKF over the traditional EKF across various scenarios with different degrees of data loss.

Integrated Active Suspension and Anti-Lock Braking Control for Four-Wheel-Independent-Drive Electric Vehicles

This paper presents an integrated control scheme for enhancing the ride comfort and handling performance of a four-wheel-independent-drive electric vehicle through the coordination of active suspension system(ASS)and anti-lock braking system(ABS).First,a longitudinal-vertical coupled vehicle dynamics model is established by integrating a road input model.Then the coupling mechanisms between longitudinal and vertical vehicle dynamics are analyzed.An ASS-ABS integrated control system is proposed,utilizing an H∞controller for ASS to optimize load transfer effect and a neural network sliding mode control for ABS implementation.Finally,the effectiveness of the proposed control scheme is evaluated through comprehensive tests conducted on a hardware-in-loop(HIL)test platform.The HIL test results demonstrate that the proposed control scheme can significantly improve the braking performance and ride comfort compared to conventional ABS control methods.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

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.

A New Structure of Multilevel Inverter with Reduced Number of Switches for Electric Vehicle Applications

Both Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs) need a traction motor and a power in-verter to drive the traction motor. The requirements for the power inverter include high peak power, opti-mum consumption of energy, low output harmonics and inexpensive circuit. In this paper, a new structure of multilevel inverter with reduced number of switches is proposed for electric vehicle applications. It consists of an H-bridge and an inverter in each phase which produces multilevel voltage by switching the dc voltage sources in series. As the number of switches are reduced, both conduction and switching losses will be de-creased, which leads to increase the efficiency of converter. The size and power consumption of driving cir-cuits are also reduced. The proposed three phase inverter can produces more number of voltage levels in the same number of the voltage source and reduced number of switches compared to the conventional inverters. This structure minimizes the total harmonic distortion (THD) of the output voltage waveforms. The structure of proposed multilevel inverter, modulation method, switching losses, THD calculation and simulation re-sults with PSCAD/EMTDC software are shown in this paper.

A priority-based electric vehicle-assisted grid frequency regulation system

During electric vehicle(EV)-assisted grid frequency modulation,inconsistent state of charge(SOC)among EVs can result in overcharging and discharging of the batteries,affecting the stability of the electrical system.As a solution,this paper proposes a priority-based frequency regulation strategy for EVs.Firstly,models for the primary and secondary frequency regulation of EV-assisted power grids are established.Secondly,a consensus algorithm is used to construct a distributed com-munication system for EVs.Target SOC values are used to obtain a local frequency regulation priori-ty list.The list is used in an optimal control plan allowing individual EVs to participate in frequency regulation.Finally,a simulation of this strategy under several scenarios is conducted.The results indicate that the strategy ensures uniform SOC among the participating group of EVs,thereby avoi-ding overcharging and discharging of their batteries.It also reduces frequency fluctuations in the electrical system,making the system more robust compared with the frequency regulation strategy that is not priority-based.