Electric Vehicle Battery Capacity Research Articles

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time Windows as Well as Partial Recharging.

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes.

Community-to-vehicle-to-community (C2V2C) for inter-community electricity delivery and sharing via electric vehicle: Performance evaluation and robustness analysis

Electric vehicles (EVs) possess untapped potential as mobile power banks for actively delivering electricity between different energy communities, known as Community-to-Vehicle-to-Community (C2V2C) service. While C2V2C represents an effective means of inter-community electricity sharing, limited research explores EVs' role in electricity delivery between locations. Suitable control approaches of EV charging for the C2V2C service are lacking, and it is unclear how robust the C2V2C service is and how its performance is affected by different factors. This paper aims to bridge these research gaps by developing an advanced control of EV smart charging/discharging to facilitate the C2V2C service. By comparing the power balance in the EVs' current-connecting and next-destination communities, the advanced control derives a target state-of-charge for the EVs in the current-connecting community, which can optimize the electricity delivery between the two communities. Then, the robustness of the C2V2C service is analyzed by evaluating its performances under different scenarios. Major factors like community combinations, renewable energy system (RES) configurations, EV battery capacity and numbers are examined for their impacts on C2V2C performance. The findings demonstrate that the C2V2C service significantly enhances energy balance across diverse community combinations, particularly in workplaces with substantial RES capacity. A large EV battery capacity is beneficial for performance improvements, but the impact diminishes at higher values due to limited surplus renewables availability. The increasing EV number enhances both electricity delivery capability and utilization of self-produced renewables. This study validated the effectiveness of the C2V2C service and provides valuable insights into optimizing its application across different scenarios.

A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape

This paper proposes an innovative approach for improving the charging efficiency of electric vehicles (EVs) by combining photovoltaic (PV) systems with AC–DC Power Factor Correction (PFC). The proposed approach employs bi-directional power flow management within the PFC system, allowing for enhanced resource utilization and EV battery capacity under a variety of environmental circumstances. A modified Lyapunov-based robust model reference adaptive controller (M-LRMRAC) is developed to provide real-time Maximum Power Point Tracking (MPPT) for the PV array. By quickly recording the MPP, this controller skilfully adjusts to shifting radiation and temperature dynamics. A noteworthy accomplishment is that the M-LRMRAC outperforms traditional Perturb and Observe (P&O) techniques by achieving quick MPP convergence (0.54 s). Additionally, the benefits of this integrated system go beyond effective MPPT. The method achieves operating at unity power factor and reduces total harmonic distortion, which results in improved power quality when charging EV Batteries (EVB). The entire solution provided by this multifaceted architecture improves the quality of electricity delivered to EV batteries while also increasing energy efficiency. This research helps to the evolution of sustainable and dependable EV charging infrastructure by solving difficulties and optimising performance. The combination of PV systems with AC–DC PFC, aided by the M-LRMRAC technology, presents a viable route for attaining efficient, clean, and high-quality EV charging, hence supporting the shift to a greener and more sustainable transportation landscape.

An electric vehicle routing model with charging stations consideration for sustainable logistics

PurposeRecently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.Design/methodology/approachThis study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.FindingsThe result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.Research limitations/implicationsThe optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.Originality/valueIn prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity

Dynamic wireless charging facilities deployed on the road network offer an effective charging method to alleviate the electric vehicle users’ range anxiety and thus facilitate the promotion of electric vehicles. The main benefit of wireless charging is that it extends EV’s driving range by enabling EV en-route recharging, which consequently reduces the required battery size for EVs. Basically, given densely distributed wireless charging lanes on the road network, smaller and less expensive batteries would be able to meet the travel demand. From the societal point of view, the reduction of battery size/capacity is capable of justifying more construction of wireless charging lanes. While previous research works on optimal deployment of wireless charging facilities ignored this benefit, this study aims to explicitly consider the tradeoff between the costs of building recharging infrastructure and manufacturing batteries from the societal point of view in the optimal configuration of dynamic wireless charging facilities. The problem is formulated into a bi-level programming model, wherein the upper-level model seeks to minimize the total social cost, and the lower-level model captures EV drivers’ choices in terms of battery size, travel route, and driving and charging behavior. To handle the problem, we first relax the path cost constraint and apply the linearization techniques to reformulate the problem into a mixed-integer linear programming, from which a lower bound of the problem can be obtained. An efficient surrogate optimization algorithm is then developed to solve the problem.

Optimal operation of microgrid with consideration of V2G's uncertainty

AbstractThe aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs’ behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs’ V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA.

A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.

Design and Optimization of Real-Time Strong Coupling Coil of Dynamic Wireless Power Transfer for Electrical Vehicle

Dynamic wireless power transfer (DWPT) can solve the limitation of battery capacity of electric vehicles, and has a good application prospect. In order to solve the problem of weak coupling power supply in a DWPT system with segmented coil mode, an improved real-time strong coupling coil arrangement mode is proposed in this paper. First, the characteristics of the typical coil arrangement mode is analyzed by establishing a DWPT model, in which the mutual inductance decreases sharply in a certain range. In order to ensure the stability of mutual inductance, an improved long receiving coil arrangement mode is proposed, and its advantage of the low coil cost is analyzed theoretically. Further, the optimal process of the improved coil mode is designed based on the genetic algorithm, and an application case of the electric bus is given. Finally, the feasibility of the coil design process is verified by simulation, and the performance advantages of the proposed scheme are verified by a 30 kW WPT platform.

Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm

Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.

Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

Electric vehicle batteries alone could satisfy short-term grid storage demand by as early as 2030

The energy transition will require a rapid deployment of renewable energy (RE) and electric vehicles (EVs) where other transit modes are unavailable. EV batteries could complement RE generation by providing short-term grid services. However, estimating the market opportunity requires an understanding of many socio-technical parameters and constraints. We quantify the global EV battery capacity available for grid storage using an integrated model incorporating future EV battery deployment, battery degradation, and market participation. We include both in-use and end-of-vehicle-life use phases and find a technical capacity of 32–62 terawatt-hours by 2050. Low participation rates of 12%–43% are needed to provide short-term grid storage demand globally. Participation rates fall below 10% if half of EV batteries at end-of-vehicle-life are used as stationary storage. Short-term grid storage demand could be met as early as 2030 across most regions. Our estimates are generally conservative and offer a lower bound of future opportunities.

Machine learning for predicting battery capacity for electric vehicles

Machine learning for predicting battery capacity for electric vehicles

Model predictive control and deep reinforcement learning based energy efficient eco-driving for battery electric vehicles

Automated self-driving vehicles not only allow of improved energy saving but also better traffic flow. In particular, with the rapid technological advance of autonomous self-driving, technical development of efficient energy consumption for fully electric vehicles (EVs) is becoming progressively important for due to the limited battery capacity and power of EVs. This article proposes a hybrid approach using Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC) for improving the energy economy of the EVs . In this study, the MPC algorithm is used to solve the optimization problem for the speed control of the EVs for minimization of energy consumption in a receding horizon, where the cost value of the horizon is fed into the DRL networks as observed state. Thus, the terminal cost of a state is learned by nearest neighbors and the terminal condition is constrained in a state observed before. The proposed scheme was tested virtually with the high fidelity car simulator (CarSim) and the remaining power of the battery source was sensed during tracing the given track in real-time. Two algorithms for performance evaluation are quantitatively explored and compared in terms of battery energy-saving. The simulation results show that the eco-driving strategy for energy saving of the EVs can be established on road driving in real-time. The performance of energy-saving about average 1% and 17% under simple uphill climbing and multiple ascents and descents, respectively. The results, as an energy efficient planning strategy of the future autonomous and intelligent eco-driving for the EVs, might be possible for guiding and significant practice.

Variable Neighborhood Search Algorithms to Solve the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery

This paper addresses the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery (EVRP-SPD), in which electric vehicles (EVs) simultaneously deliver goods to and pick up goods from customers. Due to the limited battery capacity of EVs, their range is shorter than that of internal combustion vehicles. In the EVRP, in addition to the depot and the customers, there are also charging stations (CS) because EVs need to be charged when their battery is empty. The problem is formulated as an integer linear model, and an efficient solution is proposed to minimize the total distance traveled. To create a feasible initial solution, Clarke and Wright’s savings algorithm is used. Several variants of variable neighborhood search are tested, and the reduced-variable neighborhood search algorithm is used to find the best solution in a reasonable time. Computer experiments are performed with benchmark instances to evaluate the effectiveness of our approach in terms of solution quality and time. The obtained results show that the proposed method can achieve efficient solutions in terms of solution quality and time in all benchmark instances.

Collaborative strategy of dynamic wireless charging electric vehicles and hybrid power system in microgrid

Dynamic wireless charging can strengthen the connection between in-motion electric vehicles and microgrids. To ensure the coordination between electric vehicles with the dynamic wireless charging mode (DWCEVs) and hybrid power system in the microgrid, this paper proposes a collaborative strategy consisting of the two-layer control structure. The objective of the upper layer controller is to minimize the power exchange between the main grid and microgrid, while calculating the reference power of the lower layer controller. Then, the lower layer controller allocates charging power to individual DWCEVs according to the reference value optimized by the upper layer controller, while taking into account the charging requirements of different DWCEVs. In addition, we consider the uncertainties of photovoltaic arrays power generation, traffic flow, electric vehicle battery capacity, and consumed state of charge as well as the corresponding processing methods. According to the number of control variables, the upper controller adopts the regular optimal control, and the lower controller adopts the stochastic model predictive control. Finally, the case studies demonstrate the effectiveness of the proposed collaborative strategy.

Combined Approach for Power Loss Minimization in Distribution Networks in the Presence of Gridable Electric Vehicles and Dispersed Generation

Increased electric vehicles (EVs) penetration into the distribution network burdens the system during the charging process. But the same turns out to be advantageous if the EVs are used to support the utility based on the requirement. In this work, an attempt is made to efficiently schedule both the grid-to-vehicle and vehicle-to-grid operational modes of EVs with the objective of reducing power loss in the distribution system in presence of distributed generation (DG). The EVs are modeled by considering the dominating factors, such as EV State of Charge (SoC), trip conditions, EV battery capacity, charging/discharging levels. An improvement to the existing grasshopper optimization technique is also suggested in this article, which enhances the exploring capability of the metaheuristic algorithm. The presented methodology is a combination of smart charging method, voltage stability index, and enhanced grasshopper optimization algorithm (EGOA), which decides the size of DGs to be placed in the system. The proposed hybrid approach tries to schedule both the EVs and DGs so as to achieve reduced power loss and improved voltage profile. In order to test the robustness of the proposed strategy, various analyses considering uncertainty in EVs and load conditions are performed.

Robust optimization model for the electric vehicle routing problem under battery energy consumption uncertainty with arc segmentation

As the greenhouse gas emission regulations have strengthened, establishing a sustainable transportation system has become more essential. Thus, studies on the transportation system using electric vehicles have received more research attention. However, operation using electric vehicles has obstacles such as technical limitations of vehicle batteries and insufficient number of charging stations, which can be much affected by the traffic flow changes. Therefore, we propose a robust electric vehicle routing model under road traffic flow uncertainty. An arc segmentation method is used to apply the road traffic flow uncertainty more realistically and mitigate the conservativeness of the solutions. We also use the genetic algorithm with three-layer chromosome encoding schemes. Three-layer chromosome encoding schemes guarantee the feasibility of the solutions by checking the product and battery capacity of electric vehicles. The experiments show that the solutions of the robust electric vehicle routing model tend to visit charging stations more often due to the uncertain road traffic flow. However, simulation experiments show that the robust electric vehicle routing model provides better performance.

Evaluation of Quick Charging Frequency considering Distribution of Daily Travel Distance and Battery Capacity of Electric Vehicles

Evaluation of Quick Charging Frequency considering Distribution of Daily Travel Distance and Battery Capacity of Electric Vehicles