Electric Vehicle Battery Swapping Research Articles

Impacts of Distribution-Level Joint Scheduling of Electric Vehicle Battery Charging and Swapping Stations on Reliability Improvement

Impacts of Distribution-Level Joint Scheduling of Electric Vehicle Battery Charging and Swapping Stations on Reliability Improvement

Synergies of variable renewable energy and electric vehicle battery swapping stations: Case study for Beijing

Battery swapping technology has emerged as a promising option for simultaneously addressing electric vehicle (EV) range anxiety and uncoordinated charging impacts, thereby enabling a renewable-powered future at the city scale. This study aims to explore the potential synergies between variable renewable energy (VRE), including wind and solar power, and the city-scale operation of battery swapping stations (BSSs) under varying levels of VRE penetration. To this end, an integrated modeling framework that combines multisource traffic data with node-based BSS deployment optimization and hourly power system dispatch simulations was developed. Beijing in 2025 was selected as the case study due to its ambitious EV development goals and the substantial need for VRE integration. The simulation results reveal that system-optimized BSS operations, particularly through bidirectional charging (V2G), can significantly enhance VRE integration, reduce net load fluctuations, and mitigate carbon emissions. Specifically, increasing VRE penetration from 30 % to 70 % reduces VRE curtailment by 1.1 TWh to 6.4 TWh and avoids 3.0 t to 6.3 t of carbon emissions per vehicle annually. The economic analysis further indicates that while current time-of-use electricity pricing leads to higher costs for BSS operations, a real-time pricing mechanism offers a more economically viable solution, benefiting both power system operators and BSS operators. The integrated modeling framework developed in this study not only advances the understanding of city-scale BSS operations but also provides a valuable tool for analyzing the complex interactions between EV infrastructure, VRE integration, and urban power grids.

GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman’s rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer’s components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs.

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.

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

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).

Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist in both the power output of new energy sources and load. To effectively harness the economic and operational potential of an Integrated Energy System (IES), this paper introduces an enhanced uncertainty set. This set incorporates N-1 contingency considerations and the nuances of source–load distribution. This framework is applied to a robust optimization model for an Electric Vehicle Integrated Energy System (EV-IES), which includes Electric Vehicle Battery Swapping Station (EVBSS). Firstly, this paper establishes an IES model of the EVBSS, and then proceeds to classifies and schedules the large-scale battery groups within these stations. Secondly, this paper proposes an enhanced uncertainty set to account for the operational status of multiple units in the system. It also considers the output characteristics of both new energy sources and loads. Additionally, it takes into consideration the N-1 contingency state and multi-interval distribution characteristics. Subsequently, a multi-time-scale optimal scheduling model is established with the objective of minimizing the total cost of the IES. The day-ahead robust optimization fully considers the multivariate uncertainty of the IES. The solution employs the Nested Column and Constraint Generation (C&CG) algorithm, based on the distribution characteristics of multiple discrete variables in the model. The intraday optimal scheduling reallocates the power of each unit based on the robust optimization results from the day-ahead scheduling. Finally, the simulation results demonstrate that the proposed method effectively reduces the conservatism of the uncertainty set, ensuring economic and stable operation of the EV-IES while meeting the demands of electric vehicle users.

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

An operational scheduling framework for Electric Vehicle Battery Swapping Station under demand uncertainty

This paper proposes a novel mathematical optimization model aimed at incorporating demand response strategies in the operational scheduling problem of Electric Vehicle (EV) Battery Swapping Stations (BSS). The primary goal of the model is to minimize the operational costs while accounting for intricacies arising from diverse charging modes, variations in battery capacities, charger availability, different states of charges (SOC) of incoming discharged batteries, time-of-use power prices, power constraints and fluctuating customer demand patterns. Furthermore, the model takes into consideration the effect of faster charging modes on battery health to ensure a sustainable operation. The proposed model is further extended to incorporate the effect of swapping demand uncertainty on the operational decision-making of the BSS, and a two-stage stochastic optimization model with a recourse approach is proposed. The proposed stochastic optimization model is tested with relevant case studies, and the results demonstrate the efficacy of the model in providing a risk-neutral solution by determining the optimal demand satisfaction pattern and calculating the number of additional batteries needed to minimize demand shortfalls across all scenarios. The proposed scheduling framework holds the promise of improving both the efficiency of the BSS operations and customer satisfaction, paving the way for accelerated EV adoption.

Business Model Innovation of Off Grid Public Electric Vehicles Battery Swap Station with Solar Powered

Energy transition is Indonesia's national agenda to maintain energy security and realize the use of green energy. The use of electric vehicles (EVs) is one strategy to maintain primary energy security and support the reduction of carbon emissions. To support the growth of the EV ecosystem, Public Electric Vehicle Battery Swap Stations (Stasiun Penukaran Baterai Kendaraan Listrik Umum, SPBKLU) have been developed and installed in several locations. Currently, SPBKLU still uses electricity sources from the PLN (Perusahaan Listrik Negara, Indonesia’s electricity state-owned company) grid, where the majority of the PLN grid itself still relies on gas and coal power plants. Hence, this work proposed a PV-powered battery swap station and presented the Business Model Canvas and financial and SWOT analysis. Two business model schemes are proposed, the BPCOSP (Battery Provider, Cabinet Owner, Solar PV Provider) and BPCOSL (Battery Provider, Cabinet Owner, Solar PV Lease) schemes. From these two business schemes, the results of the financial feasibility analysis for the SPBKLU swap rate were obtained at IDR 18,241 for the BPCOSP scheme and IDR 21,500 for the BPCOSL scheme. From a SWOT analysis perspective, the BPCOSP and BPCOSL business schemes have the opportunity to increase the Solar PV business value chain and support government programs in achieving Net Zero Emissions. However, there are still weaknesses in terms of tariffs which are still not economical compared to the fuel costs of conventional two-wheeled vehicles and the current SPBKLU swap tariff which still uses electricity from the PLN grid.

A study on the demographic differences in visual impact assessments of the external space of electric vehicle battery swapping stations

The construction of electric vehicle (EV) battery swapping stations (BSS), which exerts an increasingly significant impact on urban landscapes. In addition, the external space of BSS is an important means to display corporate image and brand publicity. In this experiment, a sample of nine representative BSS in China, characterized by diverse architectural forms, was selected for the examination of their exterior spaces. The study focused on the physical factors of the BSS, specifically the exterior contour, number and position of entrances, façade material, height-width ratio of front elevation, logo area ratio, and hue as key factors under investigation. Meanwhile, five demographic features, namely, gender, age, education level, EV owner or potential owner, and income level, were set as variables to study the influence of the six physical factors of external space on people's visual impact assessment. The photo stimulation method was used in this experiment. The results show that the above six physical factors affect people's visual impact assessment of the external space of BSS, and people with different demographic features render different visual impact assessments. Therefore, we propose several design recommendations for BSS exterior spaces. In BSS exterior space design, greater consideration should be given to using vertical facade aspect ratios between 1/3 and 1/2, facades combining metal and glass, smaller logo area proportions, and designs combining warm and neutral color tones to meet the visual preferences of a broader audience. Different exterior design schemes should also be adopted when BSSs are targeted at different primary user groups.

Optimal Scheduling of Batteries for Electric Vehicle Battery Swapping Stations in Beijing

Optimal Scheduling of Batteries for Electric Vehicle Battery Swapping Stations in Beijing

Optimal Priority Rule-Enhanced Deep Reinforcement Learning for Charging Scheduling in an Electric Vehicle Battery Swapping Station

For a battery swapping station (BSS) with solar generation, N charging bays, and an inventory of M batteries, we study the charging scheduling problem under random EV arrivals, renewable generation, and electricity prices. To minimize the expected weighted sum of charging cost (sum of electricity and battery degradation costs) and EV owners’ waiting cost, we formulate the problem as a Markov decision process with unknown state transition probability. Under a mild heavy-traffic assumption, we rigorously establish the optimality of the Less Demand First (LDF) priority rule under arbitrary system dynamics: batteries with less demand shall be charged first. The optimality result enables us to integrate the LDF rule into a state-of-the-art deep reinforcement learning (DRL) method, proximal policy optimization (PPO), reducing the dimensionality of its output from O(M+N) to O(1), without loss of optimality in the heavy-traffic scenario. Numerical results (on real-world data) demonstrate that the proposed LDF enhanced PPO approach significantly outperforms classical DRL methods and FCFS (first come, first served) priority rule based DRL counterparts.

A secure system for electric vehicle battery swap stations using blockchain

As electric vehicles become more popular, battery swap stations are gaining attention as a new type of charging facility. However, the charging process for electric vehicles involves privacy information such as user location and charging mode, which can be easily stolen or leaked, posing security risks and personal privacy concerns for users. Therefore, protecting the privacy of electric vehicle battery swap station users has become an important issue. This paper aims to study a privacy protection system for electric vehicle battery swap stations using blockchain technology. First, the basic principles and application scenarios of blockchain technology are introduced. Second, potential privacy leaks in electric vehicle battery swap stations are analysed, and a privacy protection scheme based on blockchain is proposed, including anonymous identity authentication, zero-knowledge proof, and encrypted communication. Third, a blockchain-based privacy protection system for electric vehicle battery swap stations is designed and implemented, and its performance is experimentally evaluated and compared with traditional privacy protection schemes in terms of security and efficiency. This paper demonstrates that the blockchain-based privacy protection scheme for electric vehicle battery swap stations possesses high levels of security and reliability, effectively safeguarding users' privacy information. Furthermore, this scheme exhibits promising application prospects and potential for widespread adoption. With the continuous development and utilization of blockchain technology, the privacy protection scheme for electric vehicle battery swap stations using blockchain is expected to provide users with more secure, reliable, and convenient charging services.

Inventory Policy for Electric Vehicle Battery Swapping Stations in Beijing

Inventory Policy for Electric Vehicle Battery Swapping Stations in Beijing

Application of Improved Sparrow Search Algorithm in Electric Battery Swapping Station Switching Dispatching

The demand for power exchange has risen dramatically as the number of electric vehicle (EV) users has increased, and large-scale disorderly charging will increase the operating costs of battery swapping stations and increase the risk to the power grid. Through experimental comparison with other algorithms on 23 test functions, the results demonstrate that the convergence accuracy and speed of this improved algorithm are superior to those of other algorithms. Furthermore, in solving the optimization problem of EV battery swapping station scheduling, by reasonably allocating the battery pack charging time and establishing the forecasting model of switching demand, the average variance and peak-to-average ratio of the grid are reduced by 29.55% and 13.2%, respectively, based on meeting the user's switching demand. Approximately a 12,500 RMB reduction in the cost of charging lowers the operation cost and risk of battery swapping stations and enhances the user experience.

Techno-Economic Framework for Electric Vehicle Battery Swapping Stations

Electric vehicle (EV) battery swapping stations (BSWS) are an important aid in rapid transport electrification, especially in developing countries where per capita income is low and expensive battery prices discourage EV penetration. Consequently, a BSWS model, where the BSWS owns the battery, can help in EV penetration and rapid transport electrification. This paper presents a new EV BSWS model to obtain a suitable trade-off between swapping time and the quality of battery health indicators. The state of charge (SOC), battery abuse, and battery degradation are measured/estimated online, while the state of health (SOH) is determined offline. The cost of the swapping cycle is determined by considering multiple cost components and penalties. The modeling approach not only enables determining a fair and affordable price for each swapping cycle for EVs but also helps to keep a check on the battery’s SOH and remaining useful life (RUL) during swapping without significantly increasing the battery swapping times compared to the average refueling time of internal combustion engine (ICE) vehicles. The swapping cost of the BSWS model is also compared with home and commercial charging station (HCS & CCS) models under different assumptions. The analysis suggests that the battery can be swapped in 6 minutes for light vehicles, comparable to the complete tank refueling of ICE vehicles. Moreover, with appropriate consideration of electric vehicle opportunity profit (EVOP), the BSWS remains feasible compared to HCS and CCS even at a high-profit margin of 750%. Also, battery degradation is considered using the economic model’s degradation cost component, and its link with different SOC strategies is explored. Again, results suggest that SOC strategies can be a helpful way for BSWS to maximize battery life and increase its profits. Moreover, considering EVOP along with battery degradation and SOC strategies further highlights the feasibility of BSWS.

LSTM-based forecasting on electric vehicles battery swapping demand: Addressing infrastructure challenge in Indonesia

This article aims to design a model for forecasting the number of vehicles arriving at the battery swap station (BSS). In our case, we study the relevance of the proposed approach given the rapid increase in electric vehicle users in Indonesia. Due to the vehicle electrification program from the government of Indonesia and the lack of supporting infrastructure, forecasting battery swap demands is very important for charging schedules. Forecasting the number of vehicles is done using machine learning with the long short-term memory (LSTM) method. The method is used to predict sequential data because of its ability to review previous data in addition to the current input. The result of the forecasting using the LSTM method yields a prediction score using the root-mean-square error (RMSE) of 2.3079 x 10-6 . The forecasted data can be combined with the battery charging model to acquire predicted hourly battery availability that can be processed further for optimization and scheduling.

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

Short-term electric vehicle battery swapping demand prediction: Deep learning methods

Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance.