Fiscal and regulatory incentives for electric vehicle adoption in Polish micro-enterprises: economic analysis and policy recommendations

In line with the spate of technological advances, the transportation industry has also witnessed an increase in the adoption of Electric Vehicles (E.Vs). However, there has been and are still some underlying negating factors to the wide spread acceptance of these electric vehicles; one of note is the unavailability and inaccessibility of adequate charging infrastructure, long charging times, limited driving ranges, costs of the vehicles etc. These and many more characteristics lead to a trend popularly known as range anxiety. One of the major strengths of electric vehicles are their ability to be powered by electric energy via stored chemical energy in rechargeable batteries. Some electric vehicles run solely on batteries (Battery Electric Vehicle — BEVs), while others are a hybrid of the electric vehicles and the internal combustion engines (Plug-in Hybrid Electric Vehicles — PHEVs, and Hybrid Electric Vehicles — HEVs). However, since these electric vehicles lean towards reducing atmospheric pollution (Carbon monoxide, hydrocarbons etc.) caused by internal combustion engines, it also follows that the means of recharging these electric vehicles should also be geared towards reducing pollution to some degree. Hence, the concept of renewable energy sources powered recharging stations. However, before a lot of resources are committed into building such an infrastructure, a model should be developed which will take into account certain key factors such as storage capacity, type and size of the renewable sources, facility layout, policies and other identified stakeholders requirements which are evaluated and used in trade-off and decision analysis. However, the status-quo involves around a document-centric methodology of system development. This methodology carries with it challenging characteristics such as poor communication of data between and within interested parties, inability to contain complexities inherent in today's projects, stored data becoming prone to damage as a result of storage or usage and sometimes, inaccessibility of data. In line with current systems engineering practice, we propose a model-centric approach of the system development life-cycle, which will negate some of the drawbacks of the document-centric approach. In this work, a two-level approach is proposed: First, the model based systems engineering (MBSE) framework approach is implemented utilizing the systems modeling language (SysML) to formulate and show different views and architecture of the system in question. The objectives of this MBSE approach in addition to offering different views of the model are also to aid in real-time communication and collaboration of designs which links to understanding change configurations and impact, requirements verification, and traceability. In the second approach, a discrete-event simulation (Arena) tool is used as the reference simulation optimization tool for the model's architectural analysis. The discrete-event simulation (DES) models a hypothetical renewable-energy powered charging and swapping station with the objective of maximizing the electric vehicle's throughput (amount of EVs successfully recharging and swapping batteries in the facility). Certain constraints such as the allowable area for the solar panels, operating budget, amount of energy to purchase from the main grid etc., are included to account for a realistic adaptation of the facility, which is in line with the concept of the renewable energy sources powered recharging stations previously mentioned. Statistical analysis are performed on the optimized architectures to evaluate and compare the design configurations based on the stakeholder's requirements. The optimized parameters are then used to verify and validate the requirements. These categorization of events support the application of MBSE and simulation to the early stages of the system life cycle development of the high level design of an electric vehicle charging and swapping station

The Wireless Power Transfer (WPT) system that enables in-motion charging (or wireless charging) for Electric Vehicles (EVs) has been introduced to resolve battery-related issues (such as long charging time, high cost, and short driving range) and increase the wide-acceptance of EVs. In this paper, we study the WPT system with the objectives of minimizing energy consumption, travel time, charging monetary cost on the way, and range anxiety for online EVs. Specifically, we propose the Multi-Objective Route Planner system (MORP) to guide EVs for the multi-objective routing. MORP incorporates two components: traffic state prediction and optimal route determination. For the traffic state prediction, we conducted analysis on a traffic dataset and observed spatial-temporal features of traffic patterns. Accordingly, we introduce the horizontal space-time Autoregressive Integrated Moving Average (ARIMA) model to predict vehicle counts (i.e., traffic volume) for locations with available historical traffic data. And, we use the spatial-temporal ordinary kriging method to predict vehicle counts for locations without historical traffic data. Based on vehicle counts, we use the non-parametric kernel regression method to predict velocity of road sections, which is used to predict travel time and then, energy consumption of a route of an EV with the help of the proposed energy consumption model. We also estimate charging monetary cost and EV related range anxiety based on unit energy cost, predicted travel time and energy consumption, and current onboard energy. We design four different cost functions (travel time, energy consumption, charging monetary cost, and range anxiety) of routing and formulate a multi-objective routing optimization problem. We use the predicted parameters as inputs of the optimization problem and find the optimal route using the adaptive epsilon constraint method. We evaluate our proposed MORP system in four different aspects (including traffic prediction, velocity prediction, energy consumption prediction, and EV routing). From the experimental studies, we find the effectiveness of the proposed MORP system in different aspects of the online EV routing system.

Electric Vehicles (EVs) represent one of the key technologies aiming to minimize fossil fuel’s utilization in transportation sector and consequently mitigate air pollution. However, range anxiety and long charging time are still the biggest problems of the modern EVs. In addition, tripping hazards and possibility of being electrocuted associated with the conductors used in regular EV charging stations need to be addressed. One of the proposed solutions is to employ wireless power transfer techniques for charging. Generally, they can be divided into two groups, namely Dynamic Wireless Charging (DWC) and Static Wireless Charging (SWC). The latter is efficient, robust and relatively well studied, but is significantly limited in terms of charging time. On the other hand, DWC can potentially eliminate the charging time and reduce the size of the EV’s battery. However, when the charging in motion is performed the influence of the magnetic flux density on the driver’s and passengers’ safety needs to be studied. Thus, this study specifically focuses, discusses and analyzes the behavior of magnetic flux density in the DWC system for EVs. In addition, it investigates and elaborates on its relation to the mutual inductance between the transmitting and receiving coils. Finally, the issue of the passenger’s safety is studied via the simulation analysis of the magnetic flux leakage between the receiver coil and the EV’s cabin.

\nAvailability of charging infrastructure is an important factor in penetration of electric vehicles into daily transportation system. Several factors in electric vehicle industry have caused range anxiety including insufficient charging stations, limited range of electric vehicles, long charging time, inaccurate estimation of available range, and energy consumption of auxiliary in-vehicle devices. However less attention has been paid to universality in charging station networks. This paper reviews the solutions to range anxiety. With regards to availability of charging station as one of the solutions, accessibility of charging stations by electric vehicle owners is also represented as another cause of range anxiety and a possible solution is provided.\n

A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety

To enhance the management of parking–charging behaviours for electric vehicles (EVs) and promote the development of vehicle–grid interaction technology, the interrelation between parking and charging behaviours among EV users should be investigated further. This study, based in Changshu City, Suzhou, China, established a data linkage mechanism for parking–charging platforms and developed an EV parking–charging behaviour database, considering critical metrics like charging start time, initial and final state of charge, and charging duration. Employing the K–S test and K-means clustering methods, the diversity in parking–charging preferences between pure and plug-in hybrid EV users is explored. Results indicate that pure EVs’ parking–charging behaviours can be categorised into five distinct groups using a classification model, while those of plug-in hybrid EVs can be grouped into four categories. Both user groups include behaviours with low range anxiety, such as complete charging during special journeys, at the destination, or partial charging. Both groups also exhibit high-range-anxiety behaviours, with pure EV users favouring specific journey complete charging and plug-in hybrid EV users preferring complete charging. Notably, pure EV users also show a significant inclination towards nighttime complete charging. These insights are valuable for efficient planning and management of integrated EV facilities.

Trends in the electromobility industry, increasing research efforts related to alternative fueled vehicles, as well as growing environmental concerns are suggesting that the transition from the internal combustion engine technology to electric vehicles (EV) is necessary and inevitable. To ensure and enable rapid market penetration of EVs, one major obstacle needs to be addressed - range anxiety, a fear of running out of electricity before reaching another available charging station. This research employs a survey methodology to assess potential EV owners’ perception of range anxiety with the goal of quantifying and explaining it through key EV parameters: state of charge (i.e., a relative measure comparing the remaining amount of energy in the EV battery with the maximum capacity) and remaining range (i.e., how much distance the EV can still reach without re-charging). Through the survey analysis, we answered two relevant research questions that fall into the range anxiety research agenda: (i) how potential EV owners perceive the optimal distance between charging stations in comparison to traditional, well-developed gas station infrastructure; and (ii) how key EV parameters influence the decision to charge as well as the distance one is willing to travel to reach another charging station that may or may not be occupied. This research is beneficial for business makers as the knowledge about range anxiety is very important for making decisions about charging station placement, as well as for the research community since range anxiety is a variable that could and should be included in various research areas centered around EVs. Besides business makers and researchers, this work is beneficial to the society in general as it may potentially have a positive impact on raising awareness about the necessity of electrification in the transportation industry.

With the rapid growth of society's technology and economy, the energy and pollution problems brought by motor vehicles are gradually exposed to people. At this time, electric vehicles seem to be a good solution. However, due to the limited number of charging piles, long charging time and inadequate supporting facilities, people may feel uneasy about the range of electric vehicles. Accurately obtaining the range of a vehicle can effectively alleviate this "range anxiety". To obtain the accurate range, we need to predict the energy consumption on the planning path. In this paper, we build a two-level energy consumption prediction model based on the speed characteristics as a bridge to obtain accurate energy consumption prediction for electric vehicles. Firstly, using the experimentally obtained road traffic information and v-t data, the road and traffic characteristics parameters, vehicle speed characteristics parameters and energy consumption of each segment within the segment are calculated, and the characteristics parameters suitable as intermediaries of the secondary energy consumption prediction model are selected from the vehicle speed characteristics parameters. Secondly, the BP neural network for energy consumption prediction is established with the vehicle speed characteristic parameter as the input quantity and the energy consumption as the output quantity; the BP neural network for vehicle speed characteristic parameter prediction is established with the road and traffic characteristic number as the input quantity and the vehicle speed characteristic parameter as the output quantity. Lastly, the sequences of road and traffic feature parameters are extracted from the experimental data and input into the secondary energy consumption prediction model to obtain the predicted energy consumption and compare it with the actual energy consumption. The verification shows that the secondary energy consumption prediction model has a high accuracy.

In a bid to reduce greenhouse gas emissions, several countries worldwide are implementing policies to promote electric vehicles (EVs). However, contrary to expectations, the diffusion speed of EVs has been rather slow in South Korea. This study analyzes consumer preferences for the technological and environmental attributes of EVs and derives policy and environmental implications to promote market diffusion of EVs in South Korea. We conduct a choice‐based conjoint survey of 1,008 consumers in South Korea and estimate the consumer utility function using a mixed logit model considering consumer heterogeneity. Based on the consumer utility function, we analyze consumers' willingness‐to‐pay (WTP) for EV attributes such as driving range, charging method, charging time, autonomous driving function, carbon dioxide (CO2) reduction rate, and purchase price. The results indicate that the current low acceptance of EVs is due to their relatively high price and lack of a battery charging technology that satisfies consumers' expectations of the charging method and time. One interesting finding is that Korean consumers have a relatively higher WTP for the CO2 reduction rate of EVs than consumers in other countries; however, they do not consider CO2 reduction over other technological attributes when choosing EVs. This implies that the rate of CO2 reduction of EVs is not an important factor for South Korean consumers when buying EVs. We also calculate the effect of CO2 reduction with the market penetration of EVs and find that CO2 reduction through the diffusion of EVs depends on the country's electricity generation mix.

Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers' range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator's profits and customers' waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator's daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate pricing policy. In order to avoid a long queue build-up at the FCS, a new pricing policy is then proposed. The intuition behind the scheduled pricing policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS.

Electric vehicle (EV) potential has broadly been highlighted in providing ancillary services in a microgrid, such as grid reserve and regulation support. However, uncertain behaviors of EV charging raise crucial concerns for both the utilities and EV owners. In this paper, the impacts of EV charging uncertainties for EV charging power control participating in supplementary frequency stabilization are assessed separately based on the two perspectives, i.e., power capacity for the utility perspective and expected EV energy for the EV owner perspective. On the one hand, the power capacity accessed by the utility directly relates to the stabilization capability, which depends on the number of EVs that are willing to participate in the frequency stabilization program and the rated charging power of EV. On the other hand, the variance of expected EV energy realized by the EV owners is considered in terms of the remaining state of charge (SoC), energy capacity, and available charging time. Besides, a variance-based global sensitivity analysis (GSA) is essentially applied to identify the influential parameters of these uncertainties. The simulation studies are conducted using a microgrid environment via DIgSILENT Powerfactory software to reveal such impacts of EV charging uncertainties based on the two perspectives. The results indicate that the number of participating EVs is the most influential parameter for frequency stabilization capability, followed by the rated charging power of EV. From the EV owner’s perspective, the energy capacity is the dominant parameter affecting the expected EV energy variance, followed by the remaining energy and available charging time.

Electric vehicles (EV) are widely seen as a sustainable alternative to gasoline vehicles to decrease emissions and dependence on fossil fuels. However, range anxiety, limited range, long charging time, and insufficient charging infrastructure have affected EVs’ adoption. High-end batteries and chargers can decrease the charging demand and charging time, respectively, and increase the EVs’ market share. Based on a recently developed intercity model, this study investigates the potential impact of different battery and charger technologies on the optimal configuration of charging infrastructure for the Michigan intercity network in 2030. The major contribution of this study is not only considering the effects of a variety of technology advancements on infrastructure requirements but also developing a realistic modeling framework with the consideration of the intercity network of Michigan, using realistic assumptions and parameters calibrated through multiple stakeholders’ meetings. Therefore, the parameters and findings of this study can be used for future studies requiring realistic data. This study finds that the location of charging stations merely depends on the battery capacity while the charging power dictates the required number of chargers. Furthermore, high-tech charging infrastructures showed to be the cheaper option compared to low-tech ones.

The emergence of Electric Vehicles (EVs) is a turning point in decarbonizing the road transport sector. In spite of the various apprehensions of the customers, such as range anxiety, long charging times, higher costs, and the lack of charging infrastructures, EVs have managed to considerably penetrate into the market. Appreciable subsidies in EV purchase and possibilities of renewable energy-based local charging equipment have encouraged more and more people to own EVs. Electrifying road transport also calls for scaling up of all stages of the supply chain as it involves a lot of raw materials and critical metals used for battery technology. One of the most important factors determining the range of an EV is the energy density of the battery, which has reached over 300 Wh/kg, from 100-150 Wh/kg a decade ago. This clearly means that the same vehicle can travel double the distance with the same mass. Understanding and modeling the energy consumption in an EV is quintessential in alleviating the fear of range anxiety. This paper presents a detailed mathematical equation-based energy consumption analysis of a particular EV model for Indian roads. Very few researchers have worked on drive cycles suitable for India. The novelty of the current work is that the energy consumption calculation can be worked out for any EV model or vehicle type through simple mathematical equations.

Electric vehicles (EVs) form an important part of the energy internet, as they connect a transportation network with an electricity network. EV uptake largely depends on the optimization strategies of charging infrastructures such as battery swapping stations (BSSs). These stations can potentially reduce the upfront expenses of EV owners, range anxiety, long charging times and electricity grid strain. Currently, the major challenge in BSSs is the creation of robust business strategies. This chapter proposes BSS stochastic optimization strategies that consider EV uptake uncertainties and power distribution company decisions. Two stochastic optimizations involving two stages are investigated: (a) optimization with recourse and (b) bilevel optimization. The recourse optimization recommends initial battery investment even before the station visits are known in the planning stage and recommends battery allocations in the operation stage. This optimization links a transport network to a distribution line network, providing energy arbitrage and curtailment tractability. The bilevel optimization further links the transport network to a transmission line network using aggregated EV batteries as a form of flexible load to compensate for intermittent renewable source generation. The flexible load is a lower-level decision made by distribution company operators, and the same flexible load is a constraint in the upper-level decisions made by BSS owners. Furthermore, this optimization can link the transportation and electricity networks to a gas network in the presence of gas as a power source with varying marginal prices. The proposed strategies provide a pathway for integrating EVs in the energy internet.

Toward Mass Adoption of Electric Vehicles: Impacts of the Range and Resale Anxieties