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This paper studies the diffusion of battery electric vehicle (BEV) technology in Japan. The diffusion process in this study refers to both the innovation and adoption of BEV technology. While previous studies on BEV technological diffusion focus on policy selection, especially domestic policy, little is known about the connectedness between international and domestic factors in the BEV technological diffusion process. This paper aims to enrich the discussion of technology diffusion by expanding the argument that technology diffusion is not only a domestic problem but also an international relations problem using a case study of BEV technology diffusion in Japan. The findings indicate that two international factors push Japan to diffuse BEV technology: Japanese commitment to the global environmental regime and global competition in BEV manufacturing. However, the domestic institution factor hinders the BEV technology diffusion. The production structures of automobile industries and political economy relations between the automobile industry and the government further complicate the diffusion process of BEV technology. [ABSTRACT FROM AUTHOR]

This paper proposes a three‐layer coordinated optimization strategy based on fuzzy control of renewable energy sources (RESs), vehicle‐to‐grid (V2G) and energy storage. Firstly, the overall structure of this strategy is introduced, which is composed of V2G management system, renewable energy dispatching system and energy storage (ES) real‐time balancing system. Secondly, the control strategy is proposed for different systems, among them, the V2G management system considers shortage of power grid, forecasted maximum output of RESs, and state of charge (SOC) of electric vehicles (EVs) to calculate the charging plan for each EV in next time interval by fuzzy control algorithm; The renewable energy management system gets the expected shortage state of the power grid in the next period according to the charging plan and the current state of the power grid, then combined with the battery state of the energy storage system, the renewable energy guiding power generation in the next period can be calculated by the fuzzy control algorithm; The energy storage real‐time balancing system can charge or discharge according to the real‐time frequency and shortage of power grid to ensure balance between supply and demand of power grid. Finally, the simulation proves that the three‐layer management strategy proposed in this paper can help grid to cut peak and fill valley effectively, improve the load characteristics of the regional power grid and reduce the amounts of the rejected renewable energy. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Precise charging time prediction can effectively mitigate the inconvenience to drivers induced by inevitable charging behavior throughout trips. Although the effectiveness of the machine learning (ML) algorithm in predicting future outcomes has been established in a variety of applications (transportation sector), the investigation into electric vehicle (EV) charging time prediction is almost new. This calls for the investigation of the ML algorithm to predict EV charging time. The study developed an EV charging time prediction model based on two years of charging event data collected from 500 EVs in Japan. To predict EV charging time, this paper employed three ML algorithms: extreme learning machine (ELM), feed-forward neural network (FFNN), and support vector regression (SVR). Furthermore, ML algorithms parameters are optimized by a metaheuristic techniques: the gray wolf optimizer (GWO), particle swarm optimizer (PSO), and genetic algorithm (GA) to achieve higher accuracy and robustness. The prediction results reveal that GWO-based ML models yielded better results compared to other models. [ABSTRACT FROM AUTHOR]

To solve the problem of battery capacity degradation caused by high current magnitudes and frequent current variations in electric vehicles (EVs), a hybrid energy storage system (HESS) incorporating high energy density storage (battery) and high power density storage (ultracapacitor) is proposed. The HESS contains a multi‐port DC‐DC converter, which controls the energy flow among the battery pack, the ultracapacitor (UC) pack, and the port of output. Considering the state of charge (SOC) of UC, the speed, and demand power of EV, an energy management strategy (EMS) based on fuzzy logic control (FLC) for the HESS is proposed in this paper. According to the strategy, the battery pack serves as a base power source while the UC pack serves as a transient power source so as to smooth the battery pack current and prolong the battery life. Simulation and experiment results illustrate the effectiveness of the proposed strategy for UC/battery HESS in EVs. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Active distribution network is integrated with a large number of renewable energy sources and flexible loads such as electric vehicles(EVs). The unstable output of distributed generation and the random access to different phases cause three phase unbalance in the distribution network, which increase the energy loss and carbon emissions. Three phase unbalance reduction requires high real‐time dispatch and optimization of the power flow. This paper attempts to reduce the energy loss and carbon emission by using flexible multi‐state switch (FMSS) with optimal energy dispatch strategy. A multi‐objective optimization model is established based on second‐order cone relaxation to reduce the three‐phase unbalance and energy loss. EVs regulation strategy is proposed by analyzing their charging flexibility. A fast solution method is brought up based on second‐order cone model transformation. The effectiveness and rapidity of the method is verified by multiple cases. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

As climate change has become a pressing concern, promoting electric vehicles' (EVs) usage has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating charging stations in areas with an expanding charging infrastructure is crucial to the accessibility and future success of EVs. Nonetheless, suitable planning and deployment for EV fast-charging stations is one of the most critical determinants for large-scale EV adoption. Installing charging stations in existing fuel/gas stations in the city may be an effective way to persuade people to adopt EVs. In this paper, we aim to optimally locate a fast-charging station in an existing gas station in the real-world scenario of Aichi Prefecture, Japan. The purpose is to locate and size fast-charging stations in such ways that drivers can get access to these charging facilities within a rational driving range while considering real-world constraints. Furthermore, we include the investment cost and the EVs users' convenience cost. This problem is formulated by five integer linear programming using a weighted set covering models. The developed model determines where to locate charging stations as well as how many chargers should be installed in each charging station. The experimental results demonstrate that an appropriate location scheme can be obtained using the model M 5 . A computational experiment identifies the best infrastructure solutions for policymakers to consider in the context of growing environmental policies. [ABSTRACT FROM AUTHOR]

This paper studies the distribution of emergency relief for electric vehicles (EVs), which considers energy saving, multi-depot, and vehicle routing problems with time windows, and the named energy saving-oriented multi-depot vehicle routing problem with time windows (ESMDVRPTW). Our aim is to find routes for EVs such that all the shelter demands are fulfilled during their time windows and the total cost traveled by the fleet is minimized. To this end, we formulate the ESMDVRPTW as a mixed-integer linear programming model. Since the post-disaster transportation network contains a large number of vertices and arcs composed of vertices, we propose a two-stage approach to solve the ESMDVRPTW. The first stage is to obtain the minimal travel cost between any two vertices in real-time on a post-disaster transportation network using the proposed Floyd algorithm combined with the neighboring list (Floyd-NL algorithm). In the second stage, we develop the genetic algorithm (GA) incorporating large neighborhood search (GA-LNS), which determines the delivery scheme of shelters. Simulation results of the MDVRPTW benchmark illustrate that the performance of the GA-LNS is better than GA, simulated annealing (SA) and tabu search (TS). Finally, case studies are constructed on two real cases acquired from the OpenStreetMap (OSM) generated by the Quantum Geographic Information System (QGIS) in Ichihara city, Japan, and the test results of case studies show the effectiveness of the proposed two-stage approach. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have developed rapidly. However, as the power source of electric vehicles, lithium battery has poor performance at low temperature, and has some problems such as reduced capacity and increased internal resistance. In this paper, two thermal environments, including natural convection condition and near‐adiabatic condition, are established, and discharge capacity test and cyclic dynamic stress test (DST) are conducted for lithium iron phosphate batteries. The actual effect of near‐adiabatic conditions is analyzed from three perspectives: battery surface temperature, terminal voltage and EIS. The results show that the near‐adiabatic working condition has almost no effect on the discharge capacity at the standard discharge current (0.5 °C) under the same temperature conditions, ranging from −15 to 25 °C. In the low‐temperature − 10 and − 15 °C cyclic dynamic stress test, compared with the natural convection working condition, the near‐adiabatic condition has a rapid rise in terminal voltage one cycle earlier, causing the cycle to stop. Also, the polarization impedance increases at least twice after the cycle under the near‐adiabatic condition. Finally, according to the analysis of variance (ANOVA), the near‐adiabatic condition has a significant effect on voltage and polarization resistance at low temperatures. Still, it has almost no effect on ohmic resistance and discharge capacity. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

SUMMARY This paper evaluates the impact of an extensive introduction of electric vehicles (EVs) and plug-in hybrid vehicles (PHEVs) on the energy supply mix in Japan. Energy consumption in Japan's transport sector has been dominated by petroleum, and CO2 emissions in this sector account for 20% of Japan's CO2 emissions. Therefore, the deployment of clean energy vehicles, such as EV and PHEV, is expected to play a significant role in tackling energy security and environmental concerns. In order to assess the energy and environmental benefits of EV and PHEV, we develop an energy system model integrating an optimal power generation mix model and a vehicle penetration model, with consideration of the optimal electricity charging profile of those advanced vehicles. The calculated results suggest that massive EV penetration serves as an energy saving measure in Japan's whole energy system due to significant petroleum reductions exceeding the growth of fuel input into the power generation sector derived from significant EV and PHEV penetration. Massive EV deployment is estimated to contribute to CO2 mitigation in the energy system as well. Evaluating CO2 emissions by mileage by automobile, however, carbon emissions by mileage for EVs is almost equivalent to that of gasoline hybrid vehicles (HEVs) in the case of decommissioning of nuclear power plants, which eventually causes a higher carbon intensity of the electricity supply. [ABSTRACT FROM AUTHOR]

In this paper, we present an ensemble stacked generalization (ESG) approach for better prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of multiple base regression models to one meta-regressor, which enhances the model prediction and decreases the model variance over a single regressor model. For the current study, we develop ESG by combining three individual base machine learning algorithms, i.e., Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN), to predict the EVs' energy consumption. Tackling the challenge of predicting EVs' energy consumption, the data were collected from Aichi Prefecture, Japan, combining the digital elevation map with long-term GPS tracking data. EVs energy consumption in terms of energy efficiency (kWh/km) was estimated using several important variables such as average trip speed (km/h), trip distance, nighttime lighting, air conditioner (A/C), heater usage ratio, and road gradient. Several statistical evaluation metrics were used to evaluate the performance of the proposed methods. The prediction results show that ESG is more robust in predicting EVs' energy consumption and outperformed other models by yielding more acceptable values for proposed evaluation metrics. The results further demonstrate that the accuracy of predictive models for EVs energy consumption can be reasonably accomplished by adopting stacking techniques. The finding of this study could provide essential guidance to decision-makers and practitioners for planned development and optimal placing of EV charging infrastructures in urban areas. [ABSTRACT FROM AUTHOR]

In order to solve the shortcomings of traditional orderly charging strategy, such as the inability to accurately respond to EV load, the difficulty to formulate an effective charging strategy and the lack of positive response from users. In this paper, a novel interactive network model of distribution network and electric vehicles is constructed. The optimization factor is assigned to the charging station, so that the scheduling for electric vehicles is no longer random. Further based on the time‐of‐use price for the charging, the multi‐objective orderly charging model from the point of both users and power grid is established. The minimum charge cost and the minimum variance of load are taken as the multi‐ objective function with the constraints of charging demand and rated capacity of transformer. Finally, the simulation by NSGA‐II on different numbers of electric vehicles shows that the proposed orderly charging strategy can significantly reduce the charging cost and load peak valley for a certain scale of electric vehicles charging. It is beneficial to ensure the safety and stability of the distribution network and improve users satisfaction. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Misalignment is a problem faced when electric vehicles work under the dynamic wireless charging mode using segmented transmitting coils. One kind of coil misalignment is coil lateral offset. In order to improve lateral offset tolerance, transfer efficiency and output power need to be stabilized. In this paper, first, the circuit model of a wireless power transfer (WPT) system is analyzed, from which mutual inductance is the major factor of the transfer efficiency decline caused by coils' lateral offset. The mutual inductance of two parallel square coils with lateral offset is analyzed. With the length and width of the transmitting coil being optimally designed according to the mutual inductance analysis, the mutual inductance of coils with lateral offset is optimized, and thus, transfer efficiency can be stabilized, and the system lateral offset tolerance is preliminarily improved. Moreover, in order to further enhance the lateral offset tolerance, the secondary‐only resonant series–series (S–S) topology is adopted to stabilize the system output power. Finally, the optimal design with stabilized transfer efficiency is verified by the finite element simulation and the experiment with coils being scaled down. Due to the limitation of experimental conditions, the stability of output power is verified by simulation. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

There are various activities now taking place in ITS research and development in Japan. Advanced information and communication technologies have been applied to improve public transport systems, as well as automated highway systems. In the first part of this paper, we show three examples of public transport systems recently developed in ITS environment. These transport systems are operated in local cities and towns in Japan: the travel information system for tram users in Hiroshima, the demand responsive bus system in Nakamura and the co-operative use of electric vehicle in Ebina. In the second part of the paper, we explain how we have monitored individual passenger on public transport using cellular phones for location positioning. Location positioning technology for mobile object is essential for the operation and management of ITS supported public transport systems. Furthermore, such accurate and detailed positioning data can be utilized for travel behaviour analysis in demand modeling. The mobile instrument and monitoring systems shown in this paper can be combined with any of the case studies of ITS application to public transport systems. [ABSTRACT FROM AUTHOR]

A modular cascade machine (MCM) system is employed as the driving system in electric vehicles (EVs). It is comprised of unit motors like series batteries, which can expand the high‐efficiency region effectively and can be regarded as a redundant system with excellent fault‐tolerance ability. In this paper, an improved rule‐based torque distribution strategy for the MCM system is developed. First, the simulation model of EVs using the MCM system is built with the ideology of reverse simulation, providing a platform for the study of the torque distribution strategy. Then, according to the power requirement of the EV, a rule‐based strategy of output torque distribution is proposed, which can improve vehicle economy and the motor's working conditions. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In Japan, electric vehicle (EV) is spreading, because EV has several advantages compared with gasoline car. However, EV has two main disadvantages, shorter cruising range and longer charging time. These disadvantages may cause a serious problem on an expressway, because the queue of EV waiting for charge may be long, if enough chargers are not placed at charging places. It is important to estimate the number of chargers in a charging place to avoid the long queue. This paper proposes a model to estimate the number of chargers used by EV. In this model, it is assumed that the driver knows the charge level of EV and decides to charge at charging places according to the type of charging place and charge level. [ABSTRACT FROM AUTHOR]

To reduce CO2 emissions in the residential and transportation sectors, distributed energy technologies, such as photovoltaic power generation (PV), stationary storage batteries (SBs), battery electric vehicles (BEVs), and vehicle-to-home (V2H) systems, are expected to be introduced. The objective of this study was to analyze the impact of the installed capacity of PV and SB, the type of vehicle, and their combination on the economic and environmental performance of the total energy consumption of residences and vehicles. Thus, this study developed a model to optimize the technological configuration of residential energy systems, including various vehicle types and driving patterns. The simulation results showed that it is more economically and environmentally efficient to install a BEV and a V2H system in households with longer parking times at the residence and to install an SB in addition to these technologies in households with shorter parking times at the residence. Furthermore, comparing a gasoline vehicle and an SB, the most economical combination, with a BEV and a V2H system and with a BEV, a V2H system, and an SB, estimated the carbon tax rate necessary for cost equivalence. The result indicated that the carbon tax rate needs to be increased from its current level. [ABSTRACT FROM AUTHOR]

This paper examines the life-cycle greenhouse gas (GHG) emissions reduction and associated marginal costs (MC) in Japan when the government enhances the transition to battery electric passenger vehicles (BEVs) by implementing a rapid investment regime for charging stations and fossil-fueled vehicle bans. Important factors affecting BEV emissions are battery performance, battery production technology, electricity generation mix and battery minimum state of charge (SOC min). We find that in addition to a rapid investment regime, a fossil-fueled vehicles ban policy is necessary to significantly increase the uptake of BEVs and achieves the deepest reduction in life cycle GHG emissions. Under this policy, with carbon neutral electricity by 2050 and a low SOC min charging regime, life-cycle GHG emissions are reduced by 48% and the MC by 53% in 2060. A carbon neutral light transport fleet in 2050 will require carbon capture and storage to offset 24 million tonnes-CO 2 equivalent of emissions even if electricity is carbon neutral by 2050 and users adhere to a low SOC min charging regime. [Display omitted] • Enhanced infrastructure development has a little impact on the uptake of BEVs. • The more stringent vehicle ban reduces life cycle GHG emissions the most. • Battery charging regime is crucial to GHG emissions reduction and associated cost. • Improving battery and vehicle production is crucial to GHG emissions reduction. • Carbon neutral light vehicle transport by 2050 requires carbon capture and storage. [ABSTRACT FROM AUTHOR]

The impacts of renewable energy shifting, passenger car electrification, and lightweighting through 2050 on the atmospheric concentrations of PM2.5 total mass and oxidative stress‐inducing metals (PM2.5‐Fe, Cu, and Zn) in Japan were evaluated using a regional meteorology–chemistry model. The surface concentrations of PM2.5 total mass, Fe, Cu, and Zn in the urban area decreased by 8%, 13%, 18%, and 5%, respectively. Battery electric vehicles (BEVs) have been considered to have no advantage in terms of non‐exhaust PM emissions by previous studies. This is because the disadvantages (heavier weight increases tire wear, road wear, and resuspention) offset the advantages (regenerative braking system (RBS) reduces brake wear). However, the future lightweighting of drive battery and body frame were estimated to reduce all non‐exhaust PM. Passenger car electrification only reduced PM2.5 concentration by 2%. However, Fe and Cu concentrations were more reduced (−8% and −13%, respectively) because they have high brake wear‐derived and significantly reflects the benefits of BEV's RBS. The water‐soluble fraction concentration of metals (induces oxidative stress in the body) was estimated based on aerosol acidity. The reduction of SOx, NOx, and NH3 emissions from on‐road and thermal power plants slightly changed the aerosol acidity (pH ± 0.2). However, it had a negligible effect on water‐soluble metal concentrations (maximum +2% for Fe and +0.5% for Cu and Zn). Therefore, the metal emissions reduction was more important than gaseous pollutants in decreasing the water‐soluble metals that induces respiratory oxidative stress and passenger car electrification and lightweighting were effective means of achieving this. Plain Language Summary: Water‐soluble metals in PM2.5 induce respiratory oxidative stress. Gaseous pollutants increase the aerosol acidity and contribute to metal dissolution. In this study, the effects of renewable energy shifting, passenger car electrification, and lightweighting by 2050 on the atmospheric concentrations of PM2.5 total mass, Fe, Cu, and Zn and aerosol acidity in Japan were evaluated using atmospheric simulation. Since the regenerative braking system (RBS) of battery electric vehicles (BEVs) have lower brake wear emissions than those of conventional vehicles, the penetration of BEVs was effective in reducing the concentrations of Fe and Cu, which have high brake wear dependence (−13% and −18%, respectively, in urban area). Current BEVs increase tire and road wear and resuspension‐derived PM emissions due to their heavy weight, which can be avoided by lightweighting technologies through 2050. The reduction of gaseous pollutants from thermal power plants and on‐road slightly changed aerosol acidity, but the effect on water‐soluble metal concentrations was small. Therefore, it was suggested that reducing primary metal emissions is more important than gaseous pollutants in decreasing concentration of water‐soluble metals that are harmful to the respiratory system, and that vehicle electrification and lightweighting are effective means for that. Key Points: Vehicle electrification effectively reduced the concentrations of PM2.5‐Fe and Cu, which are toxic to respiratory oxidative stressThe lightweighting of battery electric vehicles reduced all non‐exhaust PM from tire, brake, road wear, and resuspensionChanges in aerosol acidity due to gaseous pollutants reduction had little effect on the water‐solubility of metals [ABSTRACT FROM AUTHOR]

To solve the problems of high cost and high waveform deviation factor of the electromotive force (EMF) of the rare earth permanent magnet (REPM) generator for extended‐range electric vehicle (EREV), a consequent‐pole REPM generator is proposed. Based on the model of the conventional 18‐slot/12‐pole REPM generator for the range extender, three models are proposed, and the optimized model of REPM generator is obtained by optimizing the rotor structure. Finite element software is used for simulation analysis. The results show that the amplitude of the fundamental wave of EMF of the optimized REPM generator model accounts for 93.7% of that of the conventional model, the waveform deviation factor of EMF accounts for 35.8% of that of the conventional model, and the amount of REPM materials accounts for merely 55.2% of that of the conventional model. Finally, the rationality of the design of the consequent‐pole REPM generator for the range extender is verified by experiments. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Highlights • Material structure significantly affects lifecycle vehicle energy consumption. • Vehicle energy efficiency increases in the order of EV, FCV, CV, and HV. • Transition of material structure may not significantly mitigate energy consumption. • Overall vehicle energy efficiency may deteriorate in 2050. Abstract Given a fundamental role of automobiles in human society, evaluation of vehicle energy efficiency is of utmost importance. Various reports have been published hitherto concerning well-to-wheel (WTW) fuel consumption at the vehicle operation phase. On the other hand, WTW energy consumption at other lifecycle phases has been scarcely integrated in the assessment of vehicle energy efficiency. Particularly, WTW energy consumption for material structure is significantly associated with fuel economy. As such, this paper firstly analyzes the lifecycle WTW vehicle energy efficiency from the perspective of both material structures at the manufacture phase and fuel consumption at the operation phase for conventional vehicle (CV), electric vehicle (EV), hybrid vehicle (HV) and fuel cell vehicle (FCV). Then, an expected transition of vehicle weight and energy consumption arising from material structural shift through the replacement of steel with aluminum is evaluated. Finally, the overall vehicle energy efficiency in Japan in 2020–2050 is projected. It is discovered that the inclusion of energy consumption for material structure has a significant impact on the determination of the vehicle energy efficiency, particularly for new generation vehicles. WTW analysis at the multiple lifecycle phases may be of use in establishing more comprehensive principles of vehicle energy efficiency. [ABSTRACT FROM AUTHOR]

The concept of resilience is a crucial part in crafting visions of desirable futures designed to withstand the widest variety of external shocks to the system. Backcasting scenarios are widely used to envision desirable futures with a discontinuous change from the present in mind. However, less effort has been devoted to developing theoretical frameworks and methods for building backcasting scenarios with a particular focus on resilience, although resilience has been explored in related sustainability fields. This paper proposes a method that helps design backcasting scenarios for resilient futures. A characteristic of the method is to delineate “collapse” futures, based upon which resilient futures are described to avoid the various collapsed states. In the process of designing backcasting scenarios, fault tree analysis (FTA) is used to support the generation of various risk factors and countermeasures to improve resilience. In order to test the effectiveness of the proposed method, we provide a case study to describe resilient energy systems for a Japanese community to 2030. Four expert workshops involving researchers from different disciplines were organized to generate diversified ideas on resilient energy systems. The results show that three scenarios of collapsed energy systems were described, in which policy options to be taken toward achieving resilient energy systems were derived. [ABSTRACT FROM AUTHOR]

A regional meteorology–chemistry model was used to evaluate the impact of battery electric vehicles (BEV) penetration on summer O3 concentrations in Kanto (Japan's most populous region). When all passenger cars shifted to BEVs, daytime ozone (O3) concentrations decreased over a wide area. The reduction of vehicle exhaust reduced O3 in inland suburban areas (NOx‐limited) and the reduction of gasoline fuel evaporation from vehicles and gas stations reduced O3 in urban areas (volatile organic compound (VOC)‐limited). The maximum location of maximum daily 8‐hr average O3 (MDA8hO3) sensitivity (−5%; −5 ppb) was at midway between urban and suburban areas, to which the reduction of exhaust and fuel evaporation contributed equally, respectively. The additional emissions from thermal power plants due to BEV's night‐charging could cause up to ±5% sensitivity to next‐day surface O3 concentrations topically. Depending on the O3 sensitivity regime (NOx‐ or VOC‐limited), additional NOx‐rich plumes from rural (urban) power plants tended to increase (decrease) the next day's O3. However, the spatial distribution of the regime varies temporally depending on such as NOx/VOC emission ratios and meteorological conditions. This study indicated that the distribution of negative (positive) O3 sensitivity by NOx‐rich plume is consistent with that of small (large) H2O2/HNO3 concentration ratios, indicating that H2O2/HNO3 works well as an indicator to discriminate O3 sensitivity regimes. Utilization of the H2O2/HNO3 indicator would enable regime distribution predictions without sensitivity simulations with varying NOx and VOC emissions, which would contribute to reducing computational costs. Plain Language Summary: Ozone (O3), which causes respiratory health effects, is formed in the atmosphere by its precursors NOx and volatile organic compound (VOC). Conventional gasoline vehicle exhaust is a major source of NOx and VOC, gasoline fuel VOC evaporate from vehicles and gas stations and thermal power plants emit mainly NOx. This study evaluated the impact of BEV penetration on summer O3 concentrations in Kanto using numerical simulation. When all passenger cars shifted to BEVs, daytime O3 concentrations decreased over a wide area in Kanto (up to −5%). The additional emissions from thermal power plants due to BEV night‐charging had a ±5% impact on next‐day O3 concentration topically. O3 has a NOx‐ or VOC‐sensitive region (called O3 sensitivity regime), and different effective NOx‐VOC emission measure. The above sensitivity was explained by the emission change by BEV shifting and the O3 sensitivity regime distribution. The spatial distribution of the regime varies temporally depending on NOx/VOC emission ratios and meteorological conditions. This study also indicated that H2O2/HNO3 is a useful indicator for its discrimination. Utilization of the H2O2/HNO3 indicator would enable regime distribution predictions without sensitivity simulations with varying NOx and VOC emissions, which is originally required, and would contribute to reducing computational costs. Key Points: The impact of battery electric vehicle (BEV) penetration on summer O3 concentrations in the Kanto region of Japan was evaluatedDaytime O3 decreased in both urban and inland suburban areasAdditional NOx emission from power plants due to night‐charging for BEV affected next day daytime O3 depending on meteorological conditions [ABSTRACT FROM AUTHOR]

Electric vehicles (EV) are treated as a breakthrough technology in the automotive market. The novelty of this technology also implicates that the incidence of these vehicles worldwide is still low. An important issue regarding EVs is the existence of proper charging infrastructure as waiting at charging stations due to an inadequate number of chargers can discourage EV owners. However, as the number of EVs and charging stations are low at present, real world experience is not available, so computer simulations are required for the planning of such charging stations. We developed a stochastic model in this paper that includes driving and charging behaviour of EV owners in Japan. The model is based on Monte Carlo methods and was implemented in MATLAB. We conducted simulations with this model to find out whether the existing infrastructure is adequate for the charging of a large number of EVs. The results indicate that Japan is well prepared for an increase in plug-in vehicles (PHEVs) in the near future: currently the country has 6 fast chargers for 100 electric cars and for this ratio - on average -, waiting probability at DC (direct current) fast chargers ranges lower than 5%, which is an acceptable value for EV owners. If, however, the ratio decreases, waiting probability increases exponentially. [ABSTRACT FROM AUTHOR]

This paper investigates the development of heavy duty electric battery vehicles through analysing research papers and patents and identifies emerging technology areas by using a generative probabilistic model, Latent Dirichlet Allocation (LDA). The focus of the analysis is on literature and patents published since 2010 up to date, summing up altogether more than 25 000 references. We divide the references into eight topics: operating an electric vehicle, its control, motor operations, thermal management, battery module, battery technologies, electric vehicle (EV) infrastructure and charging EV. Because battery technologies are of strategic importance in technological competition for companies and relate to the areal raw material base, we take a more detailed look at this field. The results from publication analysis are presented for China, Europe, Japan and USA. The distribution of raw materials required for heavy duty vehicles shows interesting correlations with the national development strategies. China holds reserves and/or mine production for all key raw materials categories (battery, magnet and electric cabling) needed in heavy duty electric vehicles. In addition to having an extensive raw materials base, China has protected intellectual property rights in many areas thus defending the raw materials also by controlling the access to the technologies. USA has some raw material reserves and/or production among all three raw material categories also, but their global contribution is significantly lower. Japan, with very narrow natural resource base, has very limited patenting or scientific publishing in any of the studied sub-areas of heavy duty electric vehicle development. [ABSTRACT FROM AUTHOR]

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles' energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles' energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination (R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R 2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicles (EVs) are the most important components of smart transportation systems. Limited driving range, prolonged charging times, and inadequate charging infrastructure are the key barriers to EV adoption. To address the problem of prolonged charging time, the simple approach of developing a new charging station to enhance the charging capacity may not work due to the limitation of physical space and strain on power grids. Prediction of precise EV charging time can assist the drivers in effective planning of their trips to alleviate range anxiety during trips. Therefore, this study employed four different ensemble machine learning (EML) algorithms: random forest, extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting machine, for predicting EVs' charging time. The prediction experiments were based on 2 years of real‐world charging event data from 500 EVs in Japan's private and commercial vehicles. The study emphasized predicting charging time for different charging modes, that is, normal and fast charging operations. The results indicate that EML models performed well under various scenarios, with the XGBoost model having the highest accuracy. Moreover, we also employ the newly developed Shapley additive explanation (SHAP) approach to tackle the non‐interpretability issues of the ML algorithm by interpreting the XGBoost model outputs. The obtained SHAP value plots demonstrated the nonlinear relationship between explanatory variables and EV charging time. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cell vehicles can complement other electric vehicle technologies as a zero-emission technology and contribute to global efforts to achieve the emission reduction targets. This article spotlights the current deployment status of fuel cells in road transport. For this purpose, data collection was performed by the Advanced Fuel Cells Technology Collaboration Programme. Moreover, the available incentives for purchasing a fuel cell vehicle in different countries were reviewed and future perspectives summarized. Based on the collected information, the development trends in the last five years were analyzed and possible further trends that could see the realization of the defined goals derived. The number of registered vehicles was estimated to be 51,437 units, with South Korea leading the market, with 90% of the vehicles being concentrated in four countries. A total of 729 hydrogen refueling stations were in operation, with Japan having the highest number of these. The analysis results clearly indicate a very positive development trend for fuel cell vehicles and hydrogen refueling stations in 2021, with the highest number of new vehicles and stations in a single year, paralleling the year's overall economic recovery. Yet, a more ambitious ramp-up in the coming years is required to achieve the set targets. [ABSTRACT FROM AUTHOR]

Abstract: Policymakers as well as corporate managers want to know how to gain the lead and benefit from a paradigmatic shift in technology. This paper develops and uses a combined policy and firm-level theoretical framework to derive policy implications from a case study of the development of battery, hybrid and fuel cell electric vehicles (BEVs, HEVs and FCVs) in Japan. Among the implications of the study, it is argued that Japanese national policy has so far had a limited direct role in the electrification of vehicles; this has been very largely decided and carried out in-house at the automakers. Policymakers need to consider this as well as the inherently international nature of the automotive industry. One key factor behind Toyota''s and Honda''s early and sustained lead in the electrification trajectory is the intense and in some aspects quite specific type of competition on the domestic market, which has nurtured firms with a strong product development capability. Finally, it is argued that the proposed theoretical framework contributes to a more balanced view of the role of policy in this potential paradigmatic shift in technology in a mature industry, compared to traditional policy or firm-level approaches. [Copyright &y& Elsevier]

DC‐link capacitor is one of the most vulnerable passive components in the drive system of electric vehicle, so the condition monitoring of DC‐link capacitors can significantly improve the reliability of drive system and even driving safety. The existing monitoring methods are subject to the low accuracy, added hardware and the irreversible impact on system. Therefore, based on Back Propagation (BP) neural network with Improved Gray Wolf Optimization (IGWO), a parameter identification method for the DC‐link capacitor in electric vehicle inverter is proposed. In this method, the capacitance (C) is taken as health parameter. The A‐phase current and DC‐link capacitor voltage in electric drive system are taken as inputs, and the capacitance (C) is taken as output for condition monitoring. IGWO algorithm can be applied to obtain the optimal weights and thresholds. Ultimately, under four actual working conditions in electric drive system, the condition monitoring test is carried out. The results are compared and analyzed, which show that the monitoring using IGWO‐BP neural network has better performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

• We examine the preferences of Japanese citizens for hydrogen fuel cell vehicles. • We use multinomial, nested, and mixed logit models on SP discrete choice data. • Non-financial incentives, vehicle attributes, and socio-demographic characteristics are crucial. After the successful adoption and deployment of electric vehicles in Norway with the support of generous government-led incentives, several other countries began introducing policy incentives for environmental-friendly vehicles. In light of Japan's goal of becoming a hydrogen society, this paper examines the preferences of Japanese citizens for hydrogen fuel cell vehicles (HFCVs) through a stated preference discrete choice experiment involving carefully chosen vehicle attributes and incentives. To this end, it uses mixed logit model on the choice scenario data to elicit the behavioral responses. Governmental incentives such as free public parking and free public transport significantly impact the preferences for HFCVs. In terms of socio-demographic characteristics, education and apartment parking remarkably affect the adoption of HFCVs. Although the preference of Japanese consumers for HFCVs is significantly lower than that for conventional vehicles, a well-designed package of policy incentives involving free public parking and public transport can drive Japan's push for HFCVs in the long run. [ABSTRACT FROM AUTHOR]

To achieve broad installation of electric vehicles (EVs), it is important to deploy fast EV charging stations at many points without distribution network reinforcement. This study develops a methodology for evaluating the hosting capacity for fast EV charging loads under medium‐ and low‐voltage (MV and LV) distribution networks with bus‐voltage and line‐current constraints; hence, the promising combination of fast EV charging and rooftop photovoltaic (PV) generation in distribution networks is analyzed. In particular, on a sunny day, the active power output supplied by rooftop PVs can be used to mitigate the voltage drop problem for end‐use residential customers. On a cloudy day, even though the active power output is smaller, the residual inverter capacities of rooftop PVs can be used to generate reactive power from those systems. In addition, in order to evaluate the spatial equalization of the EV hosting capacity at fast charging stations, an equalization factor of the spatial distribution is proposed, which is obtained as a percentage of the EV hosting capacity in the equally maximized case over that in the simply maximized case. The equalization factor can be improved from approximately 33 to 70% using the active and reactive power outputs from residential rooftop PVs. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Many countries are making strategic plans to replace conventional vehicles (CVs) with electric vehicles (EVs), with the motivation to curb the growth of atmospheric CO2 concentration. While previous publications have mainly employed social-economic based models to predict CO2 emission trends from vehicles over the years, they do not account for the dynamics of engine and motor efficiency under different driving conditions. Therefore, this study utilized an experimentally validated vehicle dynamic model to simulate the consumption of gasoline and electricity for CVs and EVs, respectively, under eight driving cycles for different countries/regions. The CO2 emissions of CVs and EVs through 2040 were then calculated, based on the assumptions of the improvement of engine efficiency and composition of power supply chain over the years. Results reveal that, assuming that the current projections and assumptions remain valid, China would have the highest CO2 emission for EVs, followed by Japan, world average and the EU, mainly determined by the share of fossil fuels in the power grid. As for the influence of road conditions, the CO2 emission of CVs was found to be always higher than that of EVs for all countries/regions over the years. The difference is around 10–20% under highway conditions, and as high as 50–60% in crowded urban driving conditions. [ABSTRACT FROM AUTHOR]

Three motors that were developed for icebreakers epitomize the history of electric propulsion motor development in Japan. Direct current (dc) motors were developed for Fuji, commissioned in 1965, and Shirase I, commissioned in 1982, and induction motors were developed for Shirase II, commissioned in 2009. Three separate groups in Japan are actively developing superconducting motors for marine propulsion. The Tokyo University of Marine Science and Technology group developed the world’s first axial-gap coreless high-temperature superconducting motor (HTS) with a 100-kW output for a liquid neon thermosiphon cooled superconducting machine over a period from 2004 to 2007. The IHI Corporation group has developed four types of axial-type HTS motors since 2004. Using a cooling system based on liquid nitrogen, they achieved the world’s highest output at 365 kW in 2007. The Kawasaki Heavy Industries group has been developing radial-type HTS motors since 2007. In 2010, the group developed a 1-MW motor that achieved an output of 450 kW. In 2013, the group also developed a 3-MW motor that achieved an output of 3 MW. Furthermore, the group has developed key hardware technologies for a 20-MW motor. [ABSTRACT FROM PUBLISHER]

In this study, a novel system named the third-generation wireless in-wheel motor (WIWM-3), which has a dynamic wireless power transfer (DWPT) system, is developed. It can extend the cruise range, which is one of the key specifications of electric vehicles. DWPT also reduces CO2 emission as the driving resistance is reduced due to light weight of the batteries. In this study, CO2 emission by an internal combustion vehicle, a long range drivable electric vehicle with the same cruise range, and an electric vehicle with WIWM-3 equipped with the DWPT system are analyzed using actual measurement data and calculated data based on actual measurement or specification data. A WPT system with WIWM-3 achieves 92.5% DC-to-DC efficiency as indicated by an actual measurement at the nominal position. Thus, the electric vehicle with DWPT can reduce up to 62% of CO2 emission in internal combustion vehicles, and the long-range drivable vehicle emits 17% more CO2 than the electric vehicle with DWPT. Moreover, it is expected that by 2050, electric vehicles with DWPT will reduce CO2 emissions from internal combustion vehicles by 95% in Japan. DWPT systems make electric vehicles more sustainable and, hence, more acceptable for consumers. [ABSTRACT FROM AUTHOR]

We propose a model for the integration of electric vehicles (EVs) into the grid power system in Japan. The potential of the switchover from conventional vehicles to EVs and the incurred charging loads for the EV fleet were evaluated based on the results of a Japanese road traffic census. Furthermore, an EV battery operation model was incorporated into the production cost analysis model, which is capable of determining the optimal electricity supply and demand, considering the existing interconnector power flows. The potential economic and environmental contributions of EV charge and discharge controls, with the ultimate goal of realizing the introduction of a massive renewable energy source in the future, were also evaluated. We found that EVs can greatly contribute to expanding the use of renewable energy and reducing system cost by charging and discharging not only at the owner's home but also at his/her workplace. [ABSTRACT FROM AUTHOR]