Optimizing Shared Electric Vehicle Operations Through Battery Swapping
In the rapidly evolving landscape of urban mobility, shared electric vehicles (SEVs) have emerged as a promising solution to address the growing demand for sustainable transportation. As cities worldwide strive to reduce carbon emissions and alleviate traffic congestion, SEVs offer a flexible and eco-friendly alternative to traditional car ownership. However, the successful operation of SEV systems hinges on overcoming two critical challenges: vehicle imbalance and energy replenishment. A recent study by Li Manman, Sun Jiahui, Fu Yingbin, and Zhao Boxuan from the School of Automobile at Chang’an University, published in the Journal of Chongqing University of Technology (Natural Science), presents a novel approach to tackle these issues through an integrated optimization model based on battery swapping.
The research, titled “Sharing Electric Vehicle Scheduling and Service Pricing Optimization Based on Battery Swapping,” introduces a comprehensive framework that simultaneously optimizes battery swapping station locations, battery distribution routes, vehicle scheduling, and service pricing. This holistic approach aims to maximize the profitability of SEV operators while ensuring efficient and reliable service delivery. The study leverages the centralized charging and unified distribution (CCUD) model proposed by State Grid Corporation of China in 2011, which is designed to be grid-friendly and cost-effective.
The Challenges of Shared Electric Vehicles
Shared electric vehicles have gained significant traction in recent years, driven by the convergence of technological advancements, environmental concerns, and changing consumer preferences. These vehicles provide users with the convenience of on-demand access to transportation without the financial and logistical burdens of car ownership. However, the operational complexity of SEV systems poses several challenges that must be addressed to ensure their long-term viability.
One of the most pressing issues is vehicle imbalance, where some stations experience an excess of vehicles while others face shortages. This imbalance can lead to customer dissatisfaction, as users may find it difficult to locate available vehicles when needed. Additionally, the limited range of electric vehicles necessitates frequent recharging, which can further exacerbate the imbalance problem. Traditional solutions, such as dynamic pricing and vehicle relocation, have been employed to mitigate these issues, but they often fall short in providing a comprehensive and sustainable solution.
Another critical challenge is energy replenishment. While most existing studies assume that SEVs are recharged at individual stations, this approach can be time-consuming and may not be feasible during peak demand periods. Battery swapping, on the other hand, offers a faster and more efficient method of energy replenishment. By replacing depleted batteries with fully charged ones, SEVs can be quickly returned to service, reducing downtime and improving overall system efficiency.
The Centralized Charging and Unified Distribution Model
The CCUD model, which forms the foundation of the study, is a strategic approach to managing the energy needs of SEVs. In this model, all empty batteries are transported to a centralized charging station located near a power plant. This centralized approach ensures that the charging process does not affect the voltage of the electricity distribution network, making it grid-friendly. Once fully charged, the batteries are then distributed to various battery swapping stations, where they are available for use by SEVs.
The CCUD model offers several advantages over traditional charging methods. First, it allows for more efficient use of charging infrastructure, as the centralized station can handle a large number of batteries simultaneously. Second, it reduces the need for extensive charging facilities at individual stations, lowering the overall capital and operational costs. Third, the model enables better load management, as the charging process can be scheduled during off-peak hours to minimize strain on the grid.
The Integrated Optimization Model
To address the challenges of vehicle imbalance and energy replenishment, the researchers developed a nonlinear mixed-integer programming (NMIP) model that integrates multiple aspects of SEV operations. The model is designed to maximize the profit of SEV operators by optimizing the following key components:
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Battery Swapping Station Locations: The model determines the optimal locations for battery swapping stations, taking into account factors such as demand patterns, travel distances, and the availability of charging infrastructure. By strategically placing these stations, the model aims to ensure that SEVs can access fully charged batteries whenever needed.
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Battery Distribution Routes: The model also optimizes the routes for battery distribution trucks, which transport charged batteries from the centralized charging station to the swapping stations. The goal is to minimize the total distance traveled and the associated costs, while ensuring that each station receives the required number of batteries.
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Vehicle Scheduling: The model considers the dynamic nature of SEV demand, which varies over time and across different locations. It optimizes the scheduling of vehicles to ensure that they are available where and when they are needed. This includes vehicle-passenger matching, vehicle relocation, and energy replenishment.
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Service Pricing: The model incorporates a demand-price function that reflects the relationship between the price of the service and the level of demand. By adjusting prices in real-time, the model can influence user behavior and help balance the distribution of vehicles across the network.
The NMIP model is formulated using a spatiotemporal network, which tracks the movement and energy levels of SEVs over time. This network-based approach allows for a detailed and accurate representation of the system dynamics, enabling the model to capture the complex interactions between different components of the SEV system.
Model Transformation and Solution Methods
The original NMIP model is a nonlinear non-convex problem, which is computationally challenging to solve. To make the model more tractable, the researchers applied an inverse transformation to the demand-price function, converting the model into a nonlinear convex optimization problem. This transformation simplifies the solution process and improves the computational efficiency of the model.
To solve the transformed model, the researchers compared three different algorithms: the secant-based outer approximation algorithm, the tangent-based outer approximation algorithm, and the Lagrangian relaxation algorithm. The secant-based outer approximation algorithm was found to be superior in terms of both solution quality and computational time. This algorithm works by approximating the nonlinear objective function with a series of linear segments, gradually refining the approximation until the desired level of accuracy is achieved.
The tangent-based outer approximation algorithm, while similar in concept, uses tangents instead of secants to approximate the objective function. However, this approach was found to be less effective, as it required significantly more computational time to converge to a solution. The Lagrangian relaxation algorithm, which decomposes the problem into smaller subproblems, was also evaluated. Although this method can handle large-scale problems, it was found to be less efficient than the secant-based outer approximation algorithm, particularly in terms of solution quality.
Numerical Experiments and Sensitivity Analysis
To validate the effectiveness of the proposed model and solution methods, the researchers conducted a series of numerical experiments. The experiments were based on a hypothetical SEV system with seven stations and five battery distribution trucks. The potential demand for SEV services was generated using a uniform distribution, and other parameters were set based on realistic values.
The results of the numerical experiments demonstrated that the integrated optimization model could effectively address the challenges of vehicle imbalance and energy replenishment. The model was able to generate a feasible solution that maximized the profit of the SEV operator while ensuring high levels of service availability and customer satisfaction. The sensitivity analysis revealed that both the potential demand for SEV services and the availability of electricity, represented by the battery capacity and the load capacity of the battery distribution trucks, had a significant impact on the performance of the system.
For example, increasing the battery capacity from 10 kWh to 40 kWh led to a substantial increase in the number of trips served and the overall profit. Similarly, increasing the load capacity of the battery distribution trucks from 5 to 35 blocks resulted in a significant improvement in the system’s ability to meet demand. These findings highlight the importance of carefully managing the energy resources of SEV systems to ensure their optimal performance.
Implications for Industry and Policy
The findings of this study have important implications for both the SEV industry and policymakers. For SEV operators, the integrated optimization model provides a powerful tool for improving the efficiency and profitability of their operations. By adopting the CCUD model and using advanced optimization techniques, operators can better manage vehicle imbalance and energy replenishment, leading to higher customer satisfaction and lower operational costs.
For policymakers, the study underscores the need for supportive infrastructure and regulatory frameworks to facilitate the widespread adoption of SEVs. This includes investing in the development of battery swapping stations and ensuring that the electricity grid is capable of handling the increased demand for charging. Additionally, policies that encourage the use of renewable energy sources for charging can further enhance the environmental benefits of SEVs.
Future Research Directions
While the current study provides a robust framework for optimizing SEV operations, there are several areas for future research. One potential direction is to extend the model to consider the daily utilization of battery distribution trucks, which could further improve the efficiency of the system. Another area of interest is the development of intelligent heuristic algorithms that can handle larger-scale problems and provide real-time solutions.
Additionally, future research could explore the integration of SEVs with other modes of transportation, such as public transit and bike-sharing systems, to create a more comprehensive and multimodal urban mobility ecosystem. This would require the development of new models and algorithms that can handle the complexities of integrating multiple transportation modes.
Conclusion
The study by Li Manman, Sun Jiahui, Fu Yingbin, and Zhao Boxuan from the School of Automobile at Chang’an University represents a significant advancement in the field of shared electric vehicle operations. By developing an integrated optimization model based on the CCUD model, the researchers have provided a comprehensive solution to the challenges of vehicle imbalance and energy replenishment. The model’s ability to optimize battery swapping station locations, battery distribution routes, vehicle scheduling, and service pricing has the potential to significantly improve the efficiency and profitability of SEV systems.
The findings of this study not only contribute to the academic literature but also have practical applications for SEV operators and policymakers. As the demand for sustainable transportation continues to grow, the insights gained from this research will be crucial in shaping the future of urban mobility. By leveraging advanced optimization techniques and innovative energy management strategies, the SEV industry can overcome its operational challenges and realize its full potential as a key component of a sustainable and resilient urban transportation system.
Li Manman, Sun Jiahui, Fu Yingbin, Zhao Boxuan, School of Automobile, Chang’an University, Journal of Chongqing University of Technology (Natural Science), doi: 10.3969/j.issn.1674-8425(z).2024.04.023