Battery Swapping Research Articles

How do supply- and demand-side dynamics and subsidies affect the prospects for electric vehicle battery swapping services? Evidence from an evolutionary analysis

The growing adoption of electric vehicles has highlighted the challenges associated with charging services. Slow charging has proven insufficient to address users’ concerns about range anxiety, and the constraints within the power grid have impeded the expansion of fast charging infrastructure. Battery-swapping services have emerged as a potential resolution to this predicament. Despite government efforts to promote the advancement of battery-swapping services, the market’s future remains to be determined. This study develops a network-based three-decision evolutionary game model to analyze the diffusion of battery-swapping services, considering subsidies along with supply- and demand-side dynamics. The result shows that: (1) Battery-swapping services win out in performance, and Charging Station (CS) only providing charging service will be gradually squeezed out by Battery Swapping Station (BSS) and Battery Charging-Swapping Station (BCSS). (2) It is more effective to subsidize station builders than users. Besides, avoiding over-subsidizing to prevent wastage and providing long-term subsidies for station builders are recommended, while user subsidies can be gradually phased out. (3) The charging/battery-swapping service price in the early stage exhibits a U-shaped effect on the decline of CS market share. (4) Proactively promoting the benefits of battery-swapping services can be a nudge.

Municipal solid waste management using electric vehicle by variable neighbourhood search algorithm

Efficient solid waste management is essential for addressing the multifaceted challenges associated with waste disposal, including environmental, economic, technical, and social aspects. Municipal solid waste management (MSWM) incurs significant costs and contributes to environmental pollution primarily due to the collection process. This research presents a comprehensive case study that focuses on optimising the routing of electric vehicles for solid waste collection. Specifically, it addresses the Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem considering Battery Recharging and Swapping (HOTDMDEVRPBRS) in the context of waste collection. This problem aims to optimise the routes taken by electric vehicles across multiple depots, considering constraints such as limited battery capacity, multiple technologies for recharging battery capacity, the impact of truckload on energy consumption, and time-dependent travel times. To tackle this problem, we propose a highly efficient Variable Neighborhood Search (VNS) meta-heuristic algorithm, which outperforms the Simulated Annealing (SA) approach. The proposed algorithm achieves an average improvement of 11.88% in solution quality and reduces execution time by 73% for last-mile delivery. We evaluate the effectiveness of the proposed approach using real-world data from New York's recycling management system. The findings of this study emphasise the potential for enhancing solution quality and operational efficiency in solid waste management.

Optimal allocation and route design for station-based drone inspection of large-scale facilities

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

Centrally-chosen versus user-selected swaps: How the selection of swapping stations impacts standby battery inventories

Swapping depleted batteries of electric vehicles promises much better driving-to-total-travel-time ratios than plug-in charging. Nonetheless, large-scale battery swapping systems have not successfully established yet. One obstacle, on top of the high infrastructure cost, is certainly the additional invest into extra standby batteries that await their swaps at stations. Existing research is focused on systems in which users decide individually where they want to swap batteries. This system requires significant standby battery inventories to protect against uncertain swapping demand. This paper evaluates another system where users must register their trips on a central platform, so that battery swaps can be coordinated based on central optimization results. To benchmark central optimization and user choice regarding their impact on standby battery inventories, we formulate the min-battery swapping problem: For a given set of vehicle trips, this optimization problem minimizes the number of standby batteries, distributes them in a given station network, and derives detailed swapping plans to feasibly power all trips. First, we present a thorough analysis of computational complexity. Then, we provide an efficient mixed-integer programming formulation that is adaptable to different swapping policies and (when fed into a default solver) solves instances with up to 200 trips to proven optimality in just a few seconds. Our computational study reveals that central optimization promises a significant reduction of standby battery inventories. This potential is shown to increase if swaps of not yet fully-charged batteries are allowed and swaps are pooled at a reduced number of swapping stations.

Fault diagnosis of driving gear in a battery swapping system based on audio features and SRC-Adaboost

Abstract Because of electrification conditions, key components of battery swapping systems (BSSs) for electric heavy trucks are always damaged by electric erosion, which presents challenges to the safety and efficiency of high-intensity transportation. Due to the special working conditions of a BSS, the fault diagnosis of its driving gear encounters several issues, including reciprocation motion, low and fluctuating speed, and complicated noises. To solve these problems, audio features, including Mel-frequency cepstral coefficients and Gammatone cepstral coefficients, are extracted from the vibration signals. Then, these features are utilized to construct an original dictionary. Next, based on data augmentation and dictionary learning, a robust dictionary is generated from the original dictionary. Finally, with the robust dictionary, sparse representation-based classification is integrated into AdaBoost to achieve accurate fault diagnosis for the driving gear in BSS. The effectiveness of the fault diagnosis scheme is validated based on the monitoring data of the BSS, and the accuracy of fault diagnosis is 99.17%.

Hybrid Energy Storage System Optimization With Battery Charging and Swapping Coordination

Hybrid Energy Storage System Optimization With Battery Charging and Swapping Coordination

Battery Charging and Swapping System Involved in Demand Response for Joint Power and Transportation Networks

Battery Charging and Swapping System Involved in Demand Response for Joint Power and Transportation Networks

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Research on the charging strategy of battery swapping stations under the optimization of peak-valley electricity price of electrical technology

Online optimal scheduling for battery swapping charging systems with partial delivery

Battery swapping–charging system (BSCS) is a promising operating paradigm to provide centering charging and battery swapping service for electric vehicles in transportation electrification. Facing real-time information on battery demand under the limited transporting trucks, flexible online optimization of battery delivery and transportation routing is essential for meeting practical requirements. This paper investigates the real-time scheduling problem in BSCS, considering the battery partial delivery, energy demand, delivery deadline, and vehicle routing. Considering the non-deterministic polynomia hardness of battery transportation, the offline BSCS is a time-consuming task and is unsuitable for the online setting. A Lagrangian relaxation-based Benders decomposition is proposed for parallel and real-time implementation, improving the scheduling efficiency. To tackle future information such as battery demands and delivery deadlines, by introducing the dummy copy, the offline algorithm is embedded within a rolling horizon framework to solve in real-time repeatedly. Finally, case studies using real road maps in Shanghai and Belgium have verified the validity of the proposed online framework and confirmed the necessity of considering partial delivery in enhancing the operation flexibility of BSCS. The computational efficiency of the proposed algorithm is studied under different scales of the road network, and the profit from partial delivery and online implementation are highlighted.

Service capability evaluation of electric vehicle charging and battery swapping stations: A game theory‐based combination weighting method

Service capability evaluation of electric vehicle charging and battery swapping stations: A game theory‐based combination weighting method

Exploring the key technologies needed for the commercialization of electric flying cars: A levelized cost and profitability analysis

Flying cars, also known as Vertical Takeoff and Landing aircraft (VTOLs), can significantly improve transportation efficiency, and are expected to play an important role in future transportation. However, the technical requirements to make electric flying cars economically feasible remains insufficiently investigated. In this study, with detailed cost calculation model, the levelized cost and profit of electric flying car operation are estimated. The results show that under the base case (250 Wh/kg battery, baseline cost, battery swapping, no unmanned autonomous driving systems), the levelized cost is $0.61/passenger-km, about one-third higher than on-road taxi cost. Under passenger fare of $1.00/passenger-km, the net present value is $0.46 million, which implies an investment return cycle of 5 years, comparable to the airline industry. Advanced technology deployment can significantly improve profitability. With battery specific energy increased to 400 and 600 Wh/kg, the net present value increases by 60 % and 72 %. The investment return cycle can be reduced to 3 and 2 years, making electric flying car operation a high-profitability industry comparable to ride-sharing. The use of unmanned autonomous driving systems can drive net present value up to 4.2 times that under the base case, implying an investment return cycle of 2 years. Compared with battery swapping, charging as the energy supplementing approach leads to lower operation efficiency, but can be compensated by fast-charging and the reduction of battery capacity. The results suggest that the electric flying car industry could take full advantage of on-road-vehicle battery technology development. Efforts should be made in establishing a complete air-ground joint management system to facilitate unmanned autonomous driving.

Comparing costs and climate impacts of various electric vehicle charging systems across the United States

The seamless adoption of electric vehicles (EVs) in the United States necessitates the development of extensive and effective charging infrastructure. Various charging systems have been proposed, including Direct Current Fast Charging, Battery Swapping, and Dynamic Wireless Power Transfer. While many studies have evaluated the charging costs and greenhouse gas (GHG) intensity of EVs, a comprehensive analysis comparing these systems and their implications across vehicle categories remains unexplored. This study compares the total cost of ownership (TCO) and GHG-intensity of EVs using these charging systems. Based on nationwide infrastructure deployment simulations, the change to TCO from adopting EVs varies by scenario, vehicle category, and location, with local fuel prices, electricity prices, and traffic volumes dramatically impacting results. Further, EV GHG-intensity depends on local electricity mixes and infrastructure utilizations. This research highlights the responsiveness of EV benefits resulting from technology advancements, deployment decisions, and policymaking.

Review on Electric Vehicles Battery Swapping Technology

The goal of research and development organizations is to intelligently design the architecture of battery swapping stations (BSSs), with the hope of offering a reliable platform for the successful installation of a large fleet of electric vehicles (EVs) and hybrid electric vehicles (HEVs). The BSS can calibrate its subsystem for the deployment of electric vehicles (EVs) by implementing a similar concept to those found in current gas stations, which involve exchanging or swapping out discharged batteries for partially or fully charged ones after a short while. Because it gives the relevant stakeholders a more comprehensive understanding of the business prospects, the BSS approach has emerged as a promising technological alternative to the conventional EV recharging station approach. This paper addresses the introduction to BSS, covering its methods, infrastructure, advantages over charging stations, and main obstacles. Index Terms: Battery Swapping Station (BSS), Battery Charging Stations (BCS), Electric Vehicles (EVs), Hybrid Electric Vehicles (EHVs).

Evaluation study of peak shaving potential of battery swapping stations factoring in service capacity and resource tunability

Evaluation study of peak shaving potential of battery swapping stations factoring in service capacity and resource tunability

Integrated truck drone delivery services with an optimal charging stations

Battery-powered drones, or unmanned aerial vehicles (UAVs), present significant market potential for faster, economical, and eco-friendly urban delivery solutions. Many firms are investing in drone logistics ventures to capitalize on their capabilities. However, the limited range of drone deliveries, dictated by battery capacity, poses a significant challenge. Hybrid delivery systems combining trucks and drones have gained attention to overcome this challenge. Traditional models assume trucks park at customer sites while drones handle additional deliveries, yet this approach may hinder drone efficiency due to constraints in parking availability. Alternative approaches like battery swapping and mobile charging stations on trucks have been explored but exhibit limitations such as blind spots and substantial battery reserves. We propose establishing dedicated drone charging stations and optimizing drone routing for efficient deliveries to address these issues We present a MINLP (Mixed Integer Non-Linear Programming) model aimed at identifying the most cost-effective solution that optimizes both transportation efficiency and charging infrastructure investment. Quantitative experiments demonstrate the efficacy of this approach, alongside sensitivity analysis aiding economic decision-making across various operational scenarios. Notably, our experiments show a 15% reduction in total costs when extending the operational lifespan of charging stations from 2 to 4 years.

A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology

Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.

Electric vehicle routing problem with time windows, battery swapping van, and energy consumption (EVRPTW-BSV-EC)

Electric vehicle routing problem with time windows, battery swapping van, and energy consumption (EVRPTW-BSV-EC)

A flexible variable neighbourhood search algorithm for different variants of the Electric Vehicle Routing Problem

This manuscript presents a flexible variable neighbourhood search (VNS)-based (Flexi-VNS) algorithm to tackle both the classical Electrical Vehicle Routing Problem (EVRP) and the Battery Swap Station Location-routing Problem with Capacitated Electric Vehicles (BSS-EV-LRP). The latter has been addressed in just a few studies. It incorporates into the EVRP the battery swapping stations (BSS) location requirement to be jointly taken with decisions on the routing of electric vehicles. The Flexi-VNS algorithm includes a Randomised Variable Neighbourhood Descent (RVND) method as the local search procedure, which incorporates a new intra-RVND procedure called every time a solution is modified and applied only to those routes that have been modified. We assess the performance of Flexi-VNS on EVRP and BSS-EV-LRP benchmark instances and compare it with some algorithms in the literature. The statistical test did not guarantee the existence of a statistical difference between the proposed algorithm and the best algorithm from the literature for each problem, considering a 95% confidence level. However, despite this, computational results showed the Flexi-VNS efficiency. It improved several of the best-known solutions and reduced the number of constructed battery swap stations. More specifically, Flexi-VNS was able to equal or improve the BKS values of 15 out of a total of 20 instances with available results in the literature for BSS-EV-LRP and 47 out of 52 instances available for the EVRP, totalling 75% of the BSS-EV-LRP instances and 90.38% of the EVRP instances. Furthermore, compared to the exact algorithms, the optimal solutions were found using only a fraction of the computational times reported. Additionally, Flexi-VNS was the first to provide solutions for some BSS-EV-LRP instances.

Electrifying heavy-duty truck through battery swapping

Electrifying heavy-duty truck through battery swapping

End User Incentive-based Approach for Fast Charging Station

The development of electric vehicles (EVs) is influenced by various factors such as cost, autonomy, charging speed, and infrastructure. The objective of this research paper is to model an EV fast-charging station that provides incentives to end users in terms of money. The charging station incorporates a renewable energy source (solar) and an energy storage system, taking into account the demand for EVs, state of charge (SOC), and vehicle arrival and departure times. This approach aims to increase the revenue of the station and reduce the high energy demand from the grid. To incentivize end users, the vehicle-to-grid (V2G) and battery swapping modes have been implemented. The Monte Carlo approach is used to model the demand for EVs and the production of renewable energy, considering hourly intervals. Subsequently, the installation and utilization of the EV fast-charging station (EVCS) are optimized using the Adaptive Harris Hawk Optimization (AHHO) algorithm. The system is analyzed with and without V2G and battery swapping modes. The comparison between the two modes reveals that the system with V2G and battery swapping provides both revenue and incentives to end users.