Mobile Charging Stations Revolutionize E-Logistics with Smart Routing
In a world increasingly defined by sustainability and digital transformation, the logistics industry stands at the forefront of change. As cities grow denser and environmental regulations tighten, companies are under mounting pressure to decarbonize their operations. Electric vehicles (EVs) have emerged as a pivotal solution for last-mile delivery, promising lower emissions and quieter urban environments. However, the widespread adoption of EVs in logistics has been hindered by persistent challenges—limited driving range, long charging times, and the scarcity of fixed charging infrastructure. Now, a groundbreaking study from researchers at Hebei University of Technology and Nankai University introduces a novel approach that could reshape how e-commerce companies manage their fleets: mobile charging stations dynamically positioned to match delivery routes.
Led by Ma Yanfang, Xue Jinzhao, Li Baoyu from Hebei University of Technology’s School of Economics and Management, and Yang Yifu from Nankai University’s Research Center of Logistics, the research presents a two-stage optimization algorithm that seamlessly integrates mobile charging station placement with vehicle routing. Published in Computer Engineering and Applications, the study offers a practical framework for logistics firms seeking to maximize efficiency while minimizing carbon footprints under China’s ambitious “dual carbon” goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060.
The innovation lies not in the technology of the vehicles themselves, but in the intelligence behind their operation. Traditional EV routing models assume static charging infrastructure, forcing drivers to detour to fixed stations, which increases travel distance, delivery time, and energy consumption. This inefficiency undermines the very benefits EVs are meant to provide. The research team recognized that the future of urban logistics isn’t just about electrification—it’s about adaptability. Their solution? Mobile charging units that can be repositioned before each delivery cycle to align precisely with the day’s optimal delivery paths.
Unlike previous concepts involving roving charging vans that follow EVs in real time—a model that introduces significant operational complexity and cost—the model proposed by Ma and her colleagues envisions a more practical, cost-effective system. The mobile charging stations are deployed once per delivery cycle, placed in strategic locations based on anticipated routes, and remain stationary during the delivery process. This eliminates the need for dynamic coordination between moving chargers and delivery vehicles, reducing both logistical overhead and energy expenditure.
The core of the study is a mathematical model designed to minimize total distribution distance while respecting critical constraints such as vehicle load capacity, battery limits, and charger service capabilities. The problem, known as the Electric Location-Routing Problem (ELRP), is inherently complex—classified as NP-hard, meaning that finding an exact solution becomes computationally infeasible as the number of variables increases. To tackle this, the researchers developed a two-phase algorithm combining the strengths of genetic algorithms and a newly designed Recharge Station Insertion (RSI) method.
The first phase employs a genetic algorithm to generate an initial set of efficient delivery routes, similar to solving a classic Capacitated Vehicle Routing Problem (CVRP). This phase focuses on optimizing delivery sequences and vehicle assignments without considering battery constraints. The result is a near-optimal route plan that minimizes travel distance under load and capacity limits.
The second phase is where the innovation shines. The RSI algorithm takes these initial routes and intelligently inserts mobile charging stations where they are most needed. Rather than forcing vehicles to detour to fixed locations, the algorithm identifies optimal placement zones along the route—areas where a charging stop would not add unnecessary distance. When multiple vehicles require charging, the algorithm explores the possibility of consolidating charging stops, allowing several vehicles to share a single mobile station. This consolidation reduces the total number of stations required, lowering infrastructure costs and improving resource utilization.
One of the most compelling findings of the study is the minimal impact on total delivery distance. Across a series of benchmark tests using standard CVRP datasets, the insertion of mobile charging stations increased total travel distance by an average of less than 5%. In practical terms, this means that even with the need to stop for charging, delivery fleets can maintain high levels of efficiency. For logistics managers, this small trade-off is often justified by the ability to complete longer or more complex delivery cycles without vehicle downtime or the need for battery swaps.
The researchers also conducted a comparative analysis against other optimization methods, including Particle Swarm Optimization (PSO) and Simulated Annealing (SA). The results were clear: the genetic-RSI two-stage approach outperformed both alternatives, achieving an average improvement of -4.04% over PSO and -3.65% over SA in terms of total distance minimized. This superior performance underscores the effectiveness of decomposing the problem into two specialized stages—route planning followed by strategic charging insertion—rather than attempting to solve both simultaneously with a single heuristic.
Beyond algorithmic performance, the study offers valuable managerial insights for logistics companies considering the adoption of mobile charging solutions. One key takeaway is the importance of input quality. When the RSI algorithm was fed high-quality initial routes—such as known optimal solutions from CVRP benchmarks—it required fewer charging station consolidations and produced better final outcomes. This suggests that companies investing in robust route planning systems will see amplified benefits when integrating mobile charging infrastructure.
Another critical insight involves the balance between charging infrastructure investment and operational efficiency. The study found that increasing the number of available mobile charging stations reduces total delivery distance, but only up to a point. Beyond a certain threshold—typically around three to four stations for the tested scenarios—additional units yield diminishing returns. At that stage, the marginal cost of deploying another station outweighs the marginal gain in route efficiency. This has direct implications for business models: logistics firms should not aim to deploy as many chargers as possible, but rather optimize the number based on customer density, route complexity, and daily delivery volume.
The research also examined the impact of vehicle energy consumption rates. Intuitively, one might assume that more energy-efficient vehicles would always lead to better performance. However, the study revealed a more nuanced relationship. While lower energy consumption extends vehicle range and reduces the frequency of charging stops, it can also lead to suboptimal charging station placement when consolidation is required. Vehicles with longer ranges may be routed to share a distant charging station, resulting in longer detours. Conversely, vehicles with higher energy consumption, while requiring more frequent charging, are constrained to use stations closer to their routes, minimizing detour distances. This counterintuitive finding highlights the need for holistic system design rather than isolated component optimization.
From a sustainability perspective, the mobile charging model supports the broader goals of urban decarbonization. By enabling longer and more efficient EV delivery cycles, it reduces reliance on internal combustion engine vehicles in city centers. Moreover, because the charging stations are mobile, they can be powered by renewable energy sources or deployed in areas where grid upgrades are not feasible, increasing the flexibility of green infrastructure deployment.
The practical implications extend beyond e-commerce giants. Urban delivery services, food logistics, and even municipal waste collection could benefit from this approach. For example, a city sanitation department could deploy mobile charging units at strategic transfer stations, allowing electric garbage trucks to recharge during mid-shift operations without returning to a central depot. Similarly, food delivery platforms could use mobile chargers near high-demand restaurant clusters, ensuring that electric scooters and bikes remain operational throughout peak hours.
The study also opens new avenues for collaboration between logistics firms and infrastructure providers. Instead of bearing the full cost of charging infrastructure, companies can adopt a service-based model, purchasing mobile charging as a service from specialized vendors. This shifts capital expenditure to operational expenditure, making electrification more accessible for small and medium-sized enterprises. The research suggests that such partnerships are viable as long as companies are willing to accept a modest increase in travel distance—up to 8%—in exchange for reliable charging access and reduced vehicle downtime.
Looking ahead, the researchers identify several directions for future work. One is the integration of time windows and partial charging strategies, which would make the model more applicable to real-world scenarios where customers expect deliveries within specific time frames. Another is the extension of the algorithm to handle heterogeneous fleets, including vehicles with different battery capacities, load limits, and energy consumption profiles. Additionally, incorporating real-time traffic data and stochastic demand patterns could further enhance the model’s robustness.
The publication of this research in Computer Engineering and Applications marks a significant step forward in the field of sustainable logistics. It demonstrates that the challenges of EV adoption are not insurmountable, but require innovative thinking and interdisciplinary collaboration. By reimagining charging infrastructure as a dynamic, route-responsive system rather than a static network, Ma Yanfang, Xue Jinzhao, Li Baoyu, and Yang Yifu have provided a blueprint for the next generation of urban delivery.
Their work underscores a fundamental truth: the future of logistics is not just electric—it is intelligent. The success of EV fleets will depend not only on battery technology but on the sophistication of the systems that manage them. As cities continue to grow and environmental pressures mount, solutions like mobile charging station optimization will become essential tools for building resilient, sustainable, and efficient urban supply chains.
For logistics executives, policymakers, and technology developers, the message is clear: the path to sustainable delivery is not a straight line. It requires detours, recalculations, and sometimes, a charging stop along the way. But with the right algorithms and strategic planning, those detours can be minimized, and the journey made cleaner, faster, and smarter.
Ma Yanfang, Xue Jinzhao, Li Baoyu, Yang Yifu. Hebei University of Technology, Nankai University. Computer Engineering and Applications, doi:10.3778/j.issn.1002-8331.2303-0263