Search Results (14)

This paper seeks to provide a cost benefit analysis of the implementation of a vehicle-to-grid (V2G) charging strategy relative to a smart charging (V1G) strategy from the perspective of an individual electric vehicle (EV) owner with and without solar photovoltaics (PV) located on their roof. This work utilizes a novel AC optimized power flow model (ACOPF) to produce distributed location marginal prices (DLMP) on a modified IEEE-33 node network and uses a complete set of real-world costs and benefits to perform this analysis. Costs, in the form of the addition of a bi-directional charger and the increased vehicle depreciation incurred by a V2G strategy, are calculated using modern reference sources. This produces a more true-to-life comparison of the V1G and V2G strategies from the frame of reference of EV owners, rather than system operators, with parameterization of EV penetration levels performed to look at how the choice of strategy may change over time. Counter to much of the existing literature, when the analysis is performed in this manner it is found that the benefits of implementing a V2G strategy in the U.S.—given current compensation schemes—do not outweigh the incurred costs to the vehicle owner. This result helps explain the gap in findings between the existing literature—which typically finds that a V2G strategy should be favored—and the real world, where V2G is rarely employed by EV owners. Full article

Electric vehicles (EVs) have been the most dependable and feasible choice for decarbonizing road transport over the last decade. To ensure the advancement of EVs and establish them as a sustainable alternative to internal combustion engine (ICE) vehicles, the EV sector and technological growth have largely relied on government subsidies. A significant challenge for EVs is their faster depreciation compared to ICE vehicles, primarily owing to swift technological advancements that propel the market while simultaneously rendering older EV models outdated too soon. Another factor that leads to the quicker depreciation of EVs is subsidies. The anticipated cessation of subsidies is expected to provide the required leverage to mitigate the rapid value decline in EVs, given the larger price disparity between new and used EVs. Batteries, which enable EVs to be a viable option, significantly contribute to the depreciation of EVs. In addition to the potential decline in EV battery performance, advancements in technology and reduced prices provide newer models with improved range at a more affordable cost. The used EV market accurately represents the rapid devaluation of EVs; consequently, the two topics are tightly related. Though it might not be immediately apparent, it seems evident that the pace of depreciation of EVs significantly contributes to the small size of the second-hand EV market. Depreciation is a key factor influencing the used EV market. This manuscript outlines the key aspects of depreciation and sustainability in the EV transition, especially those linked to rapid technological advancements, such as batteries, in addition to subsidies and the used EV market. The objective of this manuscript is to expose and analyze the relation between the drivers of the second-hand EV market, such as the cost of ownership, technology, and subsidies, and, on the other hand, present the interplay perspectives and challenges. Full article

Driven by growing environmental awareness and supportive regulatory frameworks, electric vehicles (EVs) are witnessing accelerating market penetration. However, a key consumer concern remains: the economic impact of battery degradation, manifesting as vehicle depreciation and diminished driving range. To alleviate this concern, EV manufacturers commonly offer performance-guaranteed free-replacement warranties, under which batteries are replaced at no cost if capacity falls below a specified threshold within the warranty period. This paper develops a symmetry-informed analytical framework to forecast time-varying aggregate warranty replacement demand (AWRD) and to design optimal battery inventory strategies. By coupling stochastic EV sales dynamics with battery performance degradation thresholds, we construct a demand forecasting model that presents structural symmetry over time. Based on this, two inventory optimization models are proposed: the Service-Level Symmetry Model (SLSM), which prioritizes reliability and customer satisfaction, and the Cost-Efficiency Symmetry Model (CESM), which focuses on economic balance and inventory cost minimization. Comparative analysis demonstrates that CESM achieves superior cost performance, reducing total cost by 20.3% while maintaining operational stability. Moreover, incorporating CESM-derived strategies into SLSM yields a hybrid solution that preserves service-level guarantees and achieves a 3.9% cost reduction. Finally, the applicability and robustness of the AWRD forecasting framework and both symmetry-based inventory models are validated using real-world numerical data and Monte Carlo simulations. This research offers a structured and symmetrical perspective on EV battery warranty management and inventory control, aligning with the core principles of symmetry in complex system optimization. Full article

In an era marked by sustainability challenges and digital transformation, organizations face heightened uncertainty in strategic decision-making. This paper applies a conceptual tool, a fuzzy-based decision model, in the appraisal of the attractiveness of electric vehicle acquisition and navigates the multifaceted complexities of integrating economic, environmental, and infrastructural factors. A concise overview of fuzzy principles highlights their relevance to strategic management in uncertain contexts. The study uses a practical example to demonstrate how fuzzy set-based decision models assess EV attractiveness by synthesizing costs, environmental impact, vehicle depreciation, and energy independence variables. The findings reveal the fuzzy set-based decision model’s potential to enhance decision clarity and efficiency, offering managers a simple but robust framework for navigating complex trade-offs. Implications for sustainable strategic management and suggestions for future research on advanced decision support systems are discussed. Full article

The adoption of a new technology is well described by an S-curve. It starts with a slow initial introduction, faster growth, and a final low-pace stage that corresponds to saturation. Once the innovation is introduced and progressively adopted, prior to saturation, some of the initial owners will begin selling their initially owned goods for different reasons, including lack of satisfaction, upgrading to a newer model, or other special unrevealed reasons. In a given market, new and second-hand products will coexist that will find new owners. The evolution of the two qualities of the same product will progress to a given equilibrium and a final ratio specific to each market. With the hypothesis of second-hand goods viewed as a new technology for lower budgets in the market, their adoption can also be described by the S-curve. The questions to be answered will relate to the dynamics of adoption of the two technologies, the ratio at equilibrium between new and used products in a market, and the delay required before equilibrium is achieved. In this manuscript, a realistic model is presented to approach and analyze the adoption of electric vehicles (EVs) with the mix of new and used vehicles with new registrations. The EV transition is presented with an adoption represented by the S-curve; the ratio of new to used EVs with new registrations is also presented in a context of high demand of used EVs and a context of rapid depreciation of EVs corresponding to lower demand of pre-owned EVs. The model predicts the number of years required before an equilibrium is reached in the ratio between used and new EVs in new registrations for a given market. Full article

Electric vehicles (EVs) are revolutionizing road transport. They represented the most reliable and realistic option to decarbonize road transport in the last 10 years and look to be holding a promising future. EVs are in competition with internal combustion engine (ICE) vehicles, but they still have a lower performance, particularly in range, and they remain more expensive. To guarantee the EV development and make it a sustainable substitution to ICE vehicles, the EV industry and technology development had been mostly supported by governments’ subsidies. One of the main issues EVs are facing is that they depreciate much faster than ICE vehicles, principally due to rapid technological progress that drives the market on the one hand and, on the other, makes older EV models prematurely obsolete. The other variable that contributes to faster EV depreciation is subsidies. It is expected that the end of subsidies will bring the necessary leverage to slow down EVs fast depreciation due to the wider price gap between new and pre-owned EVs. Batteries, which make EVs a practical reality, play a major role in EV depreciation. Besides the possible degradation of EV batteries, the technology development and price drop give newer models better range at a lower cost. The second-hand EV market is a fair reflection of the fast depreciation of EVs; naturally, the two subjects should be studied correlatively. It may not be obvious to draw an obvious correlation, but it seems clear that the fast depreciation of EVs is one of the major reasons why the second-hand EV market is still minor. Depreciation is a major driver of the second-hand EV market. In this manuscript are presented the main aspects of EV depreciation, particularly those related to fast technological evolution, including batteries and subsidies, as well as the second-hand EV market. Full article

Throughout this article, an alternative depreciation method for electric vehicles (EVs) is presented, addressing the challenge of information asymmetry—a common issue in secondary markets. The proposed method is contrasted with traditional models, such as the Straight-Line Method (SLM), the Declining Balance Method, and the Sum-of-Years Digits (SYD) method, as these classic approaches fail to adequately consider key factors such as mileage and secondary aspects like battery degradation and rapid technological obsolescence, which critically impact the residual value of used EVs. The presented approach employs an adverse selection model that incorporates buyers’ and sellers’ perceptions of vehicle quality from the information recorded on e-commerce platforms, improving the depreciation estimation. The results show that the proposed method offers greater accuracy by leveraging asymmetric information extracted from web portals. Specifically, the method identifies a characteristic intersection point, marking the moment when the model aligns most closely with the data obtained through traditional methods in terms of precision. The analysis through the density of price estimations by vehicle model year indicates that, beyond 1.8 months, the proposed model provides more reliable results than traditional methods. The proposed model allows buyers to identify undervalued assets and sellers to obtain a fair market value, mitigating the risks associated with adverse selection, reducing uncertainty, and increasing market transparency and trust. It fosters equitable pricing between buyers and sellers by addressing the implications of adverse selection, where sellers—possessing more information about the vehicle’s condition than buyers—can dominate market transactions. This model restores balance by ensuring fairer valuation based on vehicle usage, primarily addressing the lack of critical data available on e-commerce platforms, such as battery certifications, among others. Full article

This work presents three demos, which include Electric Buses (EBs) from four various brands with lengths of 12 m and 18 m and an Electric Truck (E-truck) for refuse collection. The technical operation of these EVs were analyzed to implement further operational cost optimization on the demo vehicles. The Electric Vehicles (EVs) were tested against superfast-charging solutions based on Pantograph (Type A & Type B) on the route lines (and depots) and based on Combined Charging System Type 2 (CCS2, Combo2) from various brands to validate the interoperability among several vendors and support further EV integration with more affordable solutions. The optimization includes the calculation of the EBs’ consumption at various seasons and under various operating conditions in order to use optimum battery system design, heating system, optimum EB fleet operation and size and to find the charging solutions properly. The results showed that the EB consumption increases in some cases by 64.5% in wintertime due to heating systems, and the consumption in urban areas is more than that on the route lines outside cities. In the E-truck demo, where the electric heater was replaced with a heat-pump to optimize the energy consumption, it was found that the consumption of the heat-pump is about half of the electric heater under certain operating conditions. Under strict EB schedule, Pantograph charging solutions with power ratings of 300–600 kW have been adopted to charge the batteries of the EBs within 4–10 min. In order to minimize the cumulative costs of energy, (pantograph) charging infrastructure depreciation and battery degradation, as well as depot charging (at the bus operator’s depot), was adopted with a power level of 50–350 kW based on CCS2 and pantograph. Full article

The degradation of lithium-ion cells is an important aspect, not only for quality management, but also for the customer of the application like, e.g., scooters or electric vehicles. During the lifetime of the system, the overall health on the battery plays a key role in its depreciation. Therefore, it is necessary to monitor the health of the battery during operation, i.e., cycle life, but also during stationary conditions, i.e., calendar aging. In this work, the degradation due to calendar aging is analyzed for six different cell chemistries in terms of capacity degradation and impedance increase and their performance are being compared. In a new proposed metric, the relative deviations between various cells with the exact identical aging history are being analyzed for their degradation effects and their differences, which stands out in comparison to similar research. The capacity loss was found to be most drastic at 60 °C and at higher storage voltages, even for titanate-oxide cells. LiNiMnCoO₂ (NMC), LiNiCoAlO₂ (NCA) and Li₂TiO₃ (LTO) cells at 60 °C showed the most drastic capacity decrease. NMC and NCA cells at 60 °C and highest storage voltage did not show any open circuit voltage, as their current interrupt mechanism triggered. The effect of aging shows no uniform impact on the changes in the capacity variance when comparing different aging conditions, with respect to the evaluated standard deviation for all cells. The focus of this work was on the calendar aging effect and may be supplemented in a second study for cyclic aging. Full article

This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles, have the most influence on consumers’ vehicle choices. A graphical interface was created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions. Full article

In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647. Full article

In order to explore the impact of using electric vehicles on the cost and environment of logistics enterprises, this paper studies the optimization of vehicle routing problems with the consideration of carbon trading policies. Both the electric vehicle routing model and the traditional fuel vehicle routing model are constructed aiming at minimizing the total costs, which includes the fixed costs of vehicles, depreciation costs, penalty costs for violating customer time window, energy costs and carbon trading costs. Then a hybrid genetic algorithm (HGA) is proposed to address these two models, the advantages of greedy algorithm and random full permutation are combined to set the initial population, at the same time, the crossover operation is improved to retain the excellent gene fragments effectively and the hill climbing algorithm is embedded to enhance the local search ability of HGA. Furthermore, a case data is used with HGA to carry out computational experiments in these two models and the results indicate that first using electric vehicles for distribution can indeed reduce the carbon emissions, but results in a low customer satisfaction compared with using fuel vehicles. Besides, the battery capacity and charge rate have a great influence on total costs of using electric vehicles. Second, carbon price plays an important role in the transformation of logistics companies. As the carbon price changes, the total costs, carbon trading costs, and carbon emissions of using electric vehicles and fuel vehicles are affected accordingly, yet the trends are different. The changes of carbon quota have nothing to do with the distribution scheme and companies’ transformation but influence the total costs of using electric and fuel vehicles for distribution, and the trends are the same. These reasonable proposals can support the government on carbon trading policy, and also the logistics companies on dealing the relationship between economic and social benefits. Full article

The economic operation of battery swapping stations (BSSs) is significant for the promotion of large-scale electric vehicles. This paper develops a linear programming model to maximize the daily operation profits of a BSS by considering constraints of the battery swapping demand of users and the charging/discharging balance of batteries in the BSS. Based on the BSS configuration and data from electric taxis in Beijing, we simulate the operation situation and charging/discharging load of the BSS in nine scenarios with two ordered charging and discharging strategies. The simulation results demonstrate that the model can achieve the maximum daily profits of the BSS. According to the sensitivity analysis, the battery swapping price for batteries is the most sensitive, followed by the number of batteries in the BSS, while the operation-maintenance costs and battery depreciation costs are least sensitive. In addition, the charging and discharging of batteries in the BSS can be coordinated by increasing the battery quantity of the BSS and formulating the ladder-type battery swapping price. Full article

The plug-in electric vehicle (PEV) has attracted more and more attention because of the energy crisis and environmental pollution, which is also the main shiftable load of the residential users’ demand side management (DSM) system in the future smart grid (SG). In this paper, we employ game theory to provide an autonomous energy management system among residential users considering selling energy back to the utility company by discharging the PEV’s battery. By assuming all users are equipped with smart meters to execute automatic energy consumption scheduling (ECS) and the energy company can adopt adequate pricing tariffs relating to time and level of energy usage, we formulate an energy management game, where the players are the residential users and the strategies are their daily schedules of household appliance use. We will show that the Nash equilibrium of the formulated energy management game can guarantee the global optimization in terms of minimizing the energy costs, where the depreciation cost of PEV’s battery because of discharging and selling energy back is also considered. Simulation results verify that the proposed game-theoretic approach can reduce the total energy cost and individual daily electricity payment. Moreover, since plug-in electric bicycles (PEBs) are currently widely used in China, simulation results of residential users owing household appliances and bidirectional energy trading of PEBs are also provided and discussed. Full article