Strategy For Electric Vehicles Research Articles

Environmental impacts of COVID–19 responses on passenger vehicle transport scenarios: A life cycle approach

COVID–19 has disrupted human life considerably since the beginning of 2020. The transport sector is one of the most affected sector due to restrictions imposed to curb COVID–19 pandemic. This paper evaluates the environmental impacts of COVID–19 response scenarios using life cycle assessment approach. Six regions selected for the study are Australia, China, European Union (EU), India, United Kingdom (UK), and United States of America (USA). Data for vehicle population, vehicle kilometers travelled, fleet type, and fuel efficiency were used for 2015–2020. The environmental impacts were calculated for five scenarios; Baseline–2019, Business As Usual (BAU) and COVID–19 Response Scenario (CRS) for 2020 and 2030 namely, 2020–BAU, 2020–CRS, 2030–BAU, and 2030–CRS. The results indicate that COVID–19 occurrence and related response measures did not contribute to significant environment benefits in terms of Global Warming Potential (GWP) and other pollutants in short term and long term. This implies that although immediate reductions in emissions were observed in many studies, the long term effects were minimal due to irregular policies and unmanaged demand. Further, to achieve environment benefits sustained measures should be implemented in long term with proper planning and supporting infrastructure. Although electric vehicle strategies will provide benefits; emphasis on vehicle production and fuel production technologies is also needed to achieve the emission reduction targets. The findings are relevant for policy makers while devising policies to achieve the targets.

일본 모빌리티 산업동향 및 토요타자동차 전략

Purpose: The purpose of this study is to look at trends in the Japanese mobility industry, analyze Toyota Motor’s mobility strategy, and then seek implications for the Korean government and companies. The reason for looking at Japan’s industrial trends is that not only has Japan made various efforts early on in this field, but Korea is similar to Japan in demographic structure, and so there are many things to refer to from Japan’s industrial trends. Also, looking at Toyota Motor’s case in relation to corporate strategy, we can find that the company not only clearly presents its strategy as a mobility company, but is actually implementing it by launching various products and services.
 Research design, data, and methodology: As to the Japanese mobility industry trend, we first briefly look at the Japanese government’s policies, and then look at the trends by components of the mobility industry such as electric vehicles, autonomous driving, personal mobility, and flying cars. And regarding Toyota Motor’s mobility strategy, we look at electric vehicle strategies and development organizations, and look at strategies for each field, including automobiles, MaaS, personal mobility, and flying cars.
 Results: As a result of analyzing trends in the Japanese mobility industry, the following points were confirmed. At first, the basic direction of mobility policy in the Ministry of Economy, Trade and Industry was securing mobility services that provide new value, realizing carbon neutrality and reducing traffic accidents, and strengthening the industry’s international competitiveness. In the automobile industry, we find that the electrification of automobiles had a significant impact on the industrial structure, resulting in the weakening of the vertical division of labor and the advancement of the horizontal division of labor. In the mobility field, not only automobiles but also personal mobility plays an important role. The Japanese government was promoting deregulation in this field, and startups were playing a large role in the private sector. In addition, in the field of UAM, or flying cars, the government and the private sector were cooperating to improve various standards. In addition to industry trends, this study examined the strategy of Toyota Motor Corporation, and found that the company is advancing into each field that makes up the mobility industry and pursuing business in all directions. First, in the automobile sector, the company was a little late in moving to electric vehicles, but it set a new direction by announcing a large-scale electric vehicle investment plan in December 2021. In addition to investing in electric vehicles, the company was pursuing a transformation of its automobile business from hardware-centered to MaaS-centered, the key elements of which were MSPF and Autono-MaaS. The company also actively entered the personal mobility field and the UAM or flying car business.
 Implications: This study presented the following implications for Korea through consideration of the Japanese mobility industry. First, there is a need to actively refer to Japanese policies when establishing mobility-related policies at the Korean government level. Second, Korean companies also need to actively utilize the experience of Japanese companies, especially in the service sector, when establishing mobility strategies. Thirdly, there is a need for companies in the two countries to jointly solve social problems of both countries and to explore the global market through cooperation, and to this end, the government’s efforts to revitalize exchanges between companies from both countries are needed.

Optimization of Orderly Charging/Discharging Strategy for Electric Vehicles Considering Incremental Cost of V2G

The integration of electric vehicles (EV) into demand-side response holds significant potential, which not only helps to reduce the operational expense of EVs, but also presents a viable strategy for the grid peak regulation. In this paper, the load simulation model of EVs is proposed based on the probability distribution of charging/discharging behaviours of different types of EV users from statistical data. On this basis, considering the incentive effect of hourly electricity price, an orderly charging/discharging optimization model is proposed to consider EV charging cost, discharge income and incremental battery loss cost caused by EV participating in the vehicle-to-grid (V2G) mode. This model provides a more accurate estimation on the incremental costs and market arbitrage benefits of EVs in V2G mode on the basis of conventional transportation utilization, so as to better optimize the operation strategy of the EVs for demand response. Numerical results show that in the context of tariff of usage (TOU), the orderly charge and discharge model reduces the operating cost of EV users, and can better exploit the potential of EV energy storage batteries in power grid peak regulation.

Electric Vehicle as a Service (EVaaS): Applications, Challenges and Enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system.

Factors Influencing Adoption Intention for Electric Vehicles under a Subsidy Deduction: From Different City-Level Perspectives

The popularization of electric vehicles (EVs) is beneficial to the sustainable development of energy and the environment. China’s promotion and development strategy for EVs will serve as a model for other countries. EV ownership has a significant difference between first/second-tier (FST) cities and third/fourth-tier (TFT) cities and there is a huge growth potential for the EV market in those TFT cities. This paper aims to explore the factors influencing the adoption intentions for EVs in FST and TFT cities under a subsidy deduction and to make a comparative analysis of their regional heterogeneity. Based on the extended theory of planned behavior (TPB) model, the structural equation model is used to compare the factors affecting the adoption intention for EVs of 858 respondents in China. The results show that attitude, subjective norms, novelty seeking, non-financial incentive, product cognition, and environmental concerns are positively related to intention in FST and TFT cities; however, infrastructure development only has a positive significant impact in the TFT cities. Additionally, the subsidy deduction has a more negative impact on the adoption intentions in FST cities. Our findings provide vital insights for formulating government regulations and marketing strategies depending on the diverse sizes and attributes of Chinese cities.

On-demand 4WD control of electric vehicles based on electric disconnect differential

Abstract The Electric Disconnect Differential (EDD) is a kind of newly developed mechanism in powertrain proposed to improve the driving range of electric vehicles. The EDD is ingeniously designed to integrate the dog clutch based on the ordinary bevel gear differential. Through the engagement and disengagement process of dog clutch, the torque transmission of electric axle can be decoupled. Therefore, the on-demand 4WD control based on EDD can be realized by controlling the action of the dog clutch. A double-chamfered dog clutch model and a dynamic model of driveline containing EDD are established in this paper. To improve the performance of front-wheel drive vehicles in accelerating and overtaking, an EDD-based on-demand 4WD control strategy of electric vehicles is proposed. Simulation results indicate that the on-demand 4WD control algorithm can achieve clutch engagement within 100ms. More importantly, electric vehicles equipped with EDD on the front and rear drive axles can improve the trafficability and acceleration performance through the on-demand 4WD control strategy.

Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning

With the increasing penetration of electric vehicles (EVs), the orderly charging–discharging (C–D) strategy of EVs can effectively alleviate the impact of large-scale grid-connected EVs. However, the schedule of the C–D system of EV clusters encounters the curse of dimensionality problem. In addition, the performance of the C–D control strategy faces great challenges of environmental uncertainty of user demand and electricity price. This paper proposes an EV cluster scheduling strategy considering real-time electricity prices based on deep reinforcement learning. Firstly, we establish a distributed real-time optimal scheduling structure according to the real-time price signals of distribution system operators (DSO). Furthermore, to alleviate the curse of dimensionality, we propose a C–D model of a single EV according to the C–D characteristics of EV, and we establish the C–D control model of EVs as a Markov decision process (MDP). Finally, to adapt to the uncertainty of the learning environment, we propose a model-based deep reinforcement learning to optimize the C–D behavior of EVs. After day-ahead training and parameter saving of the proposed model, the C–D scheduling strategy is generated for the real-time system state at each moment of the day. The simulation results of the C–D scheduling strategy for cost-oriented EV charging show that the proposed scheduling strategy effectively reduces the user charging cost by 133.7 dollars and the load peak–valley difference, stabilizes the load fluctuation, and achieves the win–win situation between the power grid and EV users.

Analysis and Research on Power Supply Strategies of Electric Vehicles Based on Wind Farms

The widespread growth of electric vehicles could pose significant grid and charging infrastructure challenges, especially in areas with underdeveloped infrastructure. This has affected the ease of charging electric vehicles. In this paper, I design a power supply strategy for electric vehicle charging facilities based on wind farm power supply. In this strategy, a preliminary selection of line conductors is carried out, and several schemes are preliminarily determined. Further comparative analysis is made from the three aspects of conductor, tower type, and bus. Through the PowerWorld software, a simulation model is established for each scheme, and an optimal strategy that takes into account economy, security, and system stability is obtained (AAC 31.5 mm double split, tower A and bus 5). This can assist in the transformation of electric vehicle power supply and the construction of wind farm power supply facilities.

Evaluation of Toyota’s Strategy for Electric Vehicles in Counteracting Platformers —Based on the Theories of Dynamic Managerial Capabilities and Dynamic Platform Strategy—

Evaluation of Toyota’s Strategy for Electric Vehicles in Counteracting Platformers —Based on the Theories of Dynamic Managerial Capabilities and Dynamic Platform Strategy—

Research on a PMSM control strategy for electric vehicles

This paper proposes a novel state estimation based permanent magnet synchronous motor (PMSM) control method for electric vehicle (EV) driving. Firstly, a state feedback decoupling control with disturbance feed-forward (SFDCDF) is described. As motor angular speed and rotary angle are key information for the proposed control algorithm and park’s transformation, a novel observer based angular speed estimator (OBASE) is proposed for angular speed estimation. Moreover, an extended Kalman filter (EKF) based rotary angle estimator (EBRAE) is used for rotary angle estimation with information of the estimated angular speed. The convergence of angular speed estimation is proven through Lyapunov stability theory. Simulation results also indicate that the proposed algorithms can control PMSM torque, current, and angular speed to accurately follow reference values without severe fluctuation. In addition, in order to provide SFDCDF with load torque information, the OBASE is slightly modified to work as a vehicle load estimator (VLE) so PMSM responds more rapidly and speed fluctuates more slightly when the load suddenly changes. Then a series of hardware in the loop (HIL) simulations are carried out. Results indicate that the proposed control strategy can precisely estimate PMSM’s angular speed and rotor angle. Also, it can improve the driving performance of PMSM used on EVs.

Congestion-Preventing Routing and Charging Scheduling Mechanism for Electric Vehicles in Dense Urban Areas

Traffic congestion in metropolitan areas all over the world has become a critical issue that governments mustdeal with effectively. Traffic congestion during rush hours causes vehicle drivers to arrive late at their destinations,resulting in significant economic losses. Although researchers have proposed solutions to the traffic congestionproblem, little research work has presented a joint route and charging planning strategy for electric vehicles(EVs) that alleviates traffic congestion problems simultaneously. Accordingly, a congestion-preventing route and charging planning mechanism for EVs is proposed in this work to tackle the complicated route and charging optimizationproblems of EVs. The route and charging planning proposed in this work analyzes the information providedby EVs, the charging points, and road traffic information simultaneously, and mediates the traffic jammingby means of a route and charging reservation mechanism. Possible occurrence of traffic congestion is detectedin advance and traffic regulation is carried out by allocating an elastic range to the traveling period for late-bookingEVs, to avoid moving during rush hours. EV owners are also encouraged to provide rideshare services forlate-booking EV users during rush hours. The simulation results reveal that the proposed work can satisfy thepreferred route and charging demands of EV users and alleviate traffic congestion effectively.

Optimal two-level active and reactive energy management of residential appliances in smart homes

The optimal performance of the smart home energy management system (SHEMS) in coordinating and integrating the equipment in the house plays a significant role in increasing the efficiency and economic benefits of the smart home. This paper proposes a two-level optimization algorithm for the energy management of residential appliances within a smart home, including interruptible, uninterruptible, thermostatically controlled, and non-schedulable loads, as well as the charging/discharging strategies of electric vehicles (EVs) and energy storage systems (ESSs) in the presence of distributed energy resources (DERs). In the first level of optimization, to minimize the cost of electricity, the operating time of smart home components is determined by SHEMS, and active power is exchanged between equipment. At this level, the impact of demand response (DR) constraints on the cost and load factor (LF) of the smart home have also been investigated. In the second level, considering the optimal cost obtained in the first level, in addition to the active power, the reactive power required by loads is also provided by using the additional capacity of EV and ESS inverters and improves the power factor (PF) of the house at the connection point to the grid. Day-ahead electricity costs are calculated depending on different pricing signals (TOU, RTP) with the power exchange capacity (buying/ selling) with the grid. The effectiveness of the proposed strategy is examined on a typical smart home over a 24-h. By reviewing the numerical results obtained for the cost and the LF in different scenarios, and observing the increase of the PF of home from 0.65 to 0.94 and also examining the performance of the equipment during the planning time, the efficiency of the model is proved.

Distribution Network Hierarchically Partitioned Optimization Considering Electric Vehicle Orderly Charging with Isolated Bidirectional DC-DC Converter Optimal Efficiency Model

The access of large-scale electric vehicles (EVs) will increase the network loss of medium voltage distribution network, which can be alleviated by adjusting the network structure and orderly charging for EVs. However, it is difficult to accurately evaluate the charging efficiency in the orderly charging of electric vehicle (EV), which will cause the scheduling model to be insufficiently accurate. Therefore, this paper proposes an EV double-layer scheduling model based on the isolated bidirectional DC–DC (IBDC) converter optimal efficiency model, and establishes the hierarchical and partitioned optimization model with feeder–branch–load layer. Firstly, based on the actual topology of medium voltage distribution network, a dynamic reconfiguration model between switching stations is established with the goal of load balancing. Secondly, with the goal of minimizing the branch layer network loss, a dynamic reconstruction model under the switch station is established, and the chaotic niche particle swarm optimization is proposed to improve the global search capability and iteration speed. Finally, the power transmission loss model of IBDC converter is established, and the optimal phase shift parameter is determined to formulate the double-layer collaborative optimization operation strategy of electric vehicles. The example verifies that the above model can improve the system load balancing degree and reduce the operation loss of medium voltage distribution network.

Optimally Clocking the Low Carbon Energy Mile to Achieve the Sustainable Development Goals: Evidence from Dundee’s Electric Vehicle Strategy

Dundee City has been successful in installing green infrastructure for charging electric vehicles (EVs). This intervention matches the Sustainable Development Goals (SDGs) of affordable clean energy (SDG 7), sustainable cities and communities (SDG 11) and climate action (SDG 13) of the United Nations General Assembly Agenda 2030 (Transforming our World: the 2030 Agenda for Sustainable Development). Local authorities can align interventions with SDGs according to needs. The purpose of this paper is to consider whether Dundee’s EV strategy represents the most viable and equitable intervention that could be adopted given the city’s context. We adopt a positive review and value argumentation approach to determine the extent to which the strategy satisfies the criteria of “level of urgency”, “systemic impact” and “policy gaps”, which have been employed in the extant literature as the basis for a multi-criteria analysis (MCA). We eclectically review elements of the strategy against the city’s peculiar physical and socio-economic environment, as well as argue their fit against these criteria. We interpret these criteria based on the complementarity and benefits of the strategy from the lenses of SDG 7, SDG 11 and SDG 13. Additionally, we consider the alignment of the EV strategy with the other SDGs. The criteria also allow us to evaluate the strategy based on the localisation principles of equity, acceptability and affordability of the intervention. Our review shows that the EV strategy represents a sustainable and community life-enhancing intervention that aligns with some key SDGs. However, the outcome raises concerns about the equitability of the strategy. Smaller, similar or bigger cities could utilise this approach. However, we recommend the evaluation of local priorities to improve alignment with the SDGs and the provision of clear justifications for selecting an intervention from a range of responses.

Economic dispatch of household PV-BS-EV microgrid under TOU tariffs

Economic dispatch is a hotspot issue of household hybrid energy microgrid. This paper gives modellings of a typical household microgrid integrated with solar PV, battery energy storage system (BS) and electric vehicle (EV). The charging/discharging strategy of EV and its coordinated dispatch strategy with supply side units are presented to solve the minimum system operation objective under time of use (TOU) tariffs. The simulation results show that the proposed method is effective and feasible.

A Drive System Global Control Strategy for Electric Vehicle Based on Optimized Acceleration Curve

A Drive System Global Control Strategy for Electric Vehicle Based on Optimized Acceleration Curve

Research on charging strategy of electric vehicle considering user and load curve

With the increasing popularity of electric vehicles, the disordered charging of large-scale electric vehicles will have a great impact on the safe operation of regional distribution network. In order to solve the security problems that may occur in the power grid, this paper uses the time-sharing pricing time division method for EV charging to meet the needs of EV users. Based on this method, a multi-objective optimization model is established, which takes the electric vehicle charging capacity and power as the constraints, and based on the minimum user charging cost and the minimum load curve variance. Then, the model is solved by non-dominated sorting genetic algorithm (NSGA -Ⅱ), and the optimal compromise solution is extracted by using fuzzy set theory. Finally, the correctness of the proposed model is verified by the example.

Optimal Charging Strategy of Electric Vehicles with Consideration of Battery Storage

The high penetration of electric vehicles (EVs) will increase burden of a power grid. However, the expansion of capacity of distribution facilities is not always possible, especially in some old residential community. This paper proposes to use an optimal charging strategy of EVs with additional battery energy storage (BES) to improve the charging capabilities in a residential community. By modeling the EV charging behavior, the required charging capacity is evaluated using Monte Carlo method and the BES size is determined as the difference between the required capacity and the distribution capacity. An optimal charging strategy is then proposed to reduce the charging cost and ensure the safe running of distribution network.

Impact of Different Charging Strategies for Electric Vehicles in an Austrian Office Site

Electric vehicles represent a necessary alternative for wheeled transportation to meet the global and national targets specified in the Paris Agreement of 2016. However, the high concentration of electric vehicles exposes their harmful effects on the power grid. This reflects negatively on electricity market prices, making the charging of electric vehicles less cost-effective. This study investigates the economic potential of different charging strategies for an existing office site in Austria with multiple charging infrastructures. For this purpose, a proper mathematical representation of the investigated case study is needed in order to define multiple optimization problems that are able to determine the financial potential of different charging strategies. This paper presents a method to implement electric vehicles and stationary battery storage in optimization problems with the exclusive use of linear relationships and applies it to a real-life use case with measured data to prove its effectiveness. Multiple aspects of four charging strategies are investigated, and sensitivity analyses are performed. The results show that the management of the electric vehicles charging processes leads to overall costs reduction of more than 30% and an increase in specific power-related grid prices makes the charging processes management more convenient.

Trajectory tracking algorithm in a hierarchical strategy for electric vehicle driven by four independent in-wheel motors

ABSTRACT Trajectory tracking is a key technology for precisely controlling autonomous vehicles. The characteristics of an independent drive electric vehicle (IDEV) are used as the basis for designing a trajectory tracking algorithm using a hierarchical strategy. While the upper controller tracks the desired trajectory based on model predictive control (MPC), the middle controller adjusts the speed using the fuzzy theory to optimize the parameters of the proportional integral (PI) controller and generates an additional yaw control moment based on sliding mode control (SMC). Finally, the lower controller distributes the torque to each wheel according to stability and adhesion conditions. A co-simulation platform is established to verify the performance of the proposed algorithm under typical scenarios. Results indicate that the proposed algorithm can provide good dynamic driving ability at high speeds and on low-adhesion roads. The accuracy of trajectory tracking using the proposed algorithm is better than that of the traditional preview–follower driver model and the performance of the proposed torque distribution algorithm is superior to that of the traditional even torque distribution algorithm.