Smart Planning of Highway Charging and Swapping Stations Boosts Efficiency and User Satisfaction
As the global push toward sustainable transportation accelerates, electric vehicles (EVs) are no longer a niche segment but a central pillar of the future mobility landscape. With increasing adoption rates and longer travel distances, the demand for reliable, efficient, and user-friendly charging infrastructure—especially along major transportation corridors—has become a critical challenge. Now, a groundbreaking study from North China Electric Power University introduces a novel planning methodology that leverages user behavior simulation to optimize the deployment of charging and swapping stations in highway service areas. The research, led by Hu Junjie, Li Ruzhou, and Liu Xuetao, offers a comprehensive solution that balances economic feasibility, user satisfaction, and grid stability.
Published in Power System Technology, the study presents a forward-thinking approach to infrastructure planning that goes beyond traditional models focused solely on equipment capacity or traffic volume. Instead, it integrates real-world user decision-making dynamics, recognizing that EV drivers are not passive consumers but active agents whose choices are shaped by cost, time, and convenience. By simulating how drivers choose between charging and battery swapping under varying conditions, the researchers have developed a model that predicts demand more accurately and enables smarter investment decisions.
The motivation behind this work is clear: as EV ownership grows, so does the strain on existing infrastructure. Highway service areas, once simple rest stops, are now expected to function as high-throughput energy hubs. Yet, over-investment leads to wasted capital, while under-investment results in long queues, frustrated users, and potential grid instability. The solution, the team argues, lies not in building more stations, but in building smarter ones—stations that reflect how people actually behave.
At the heart of the study is a behavioral model grounded in utility maximization theory. This framework assumes that users make decisions to maximize their personal benefit, weighing factors such as waiting time, electricity cost, and service fees. Unlike previous models that treat user preferences as static or arbitrarily assigned, this new approach captures the dynamic interplay between individual choices and system performance. For example, when a driver arrives at a service area, their decision to charge or swap depends not only on current prices but also on real-time availability of chargers and fully charged batteries. A long queue at the charging station might push a user toward swapping, even if it’s more expensive—provided a spare battery is available.
What sets this model apart is its ability to simulate the ripple effects of each decision. One user’s choice affects the system state, which in turn influences the next user’s options. This coupling of decisions over time introduces a level of realism rarely seen in infrastructure planning studies. The researchers also account for the time-varying nature of queuing, recognizing that wait times fluctuate throughout the day based on traffic patterns and service rates.
To operationalize this model, the team begins with traffic flow forecasting. Since many service areas lack real-time vehicle monitoring systems, they use a four-stage method combined with the Dijkstra algorithm to estimate electric vehicle flow. Starting from historical toll station data, they project future traffic volumes under expected growth rates tied to regional GDP. Using shortest-path analysis, they identify which origin-destination pairs pass through the target service area, allowing them to convert regional traffic data into localized EV arrival patterns.
Once the arrival profile is established, the simulation begins. Each virtual EV driver is assigned attributes such as battery state of charge (SOC), destination, and willingness to enter the service area. Drivers with low SOC are flagged as potential users of charging or swapping services. Among them, only those driving vehicles compatible with battery swapping face a choice; others default to charging. For dual-mode vehicles, the decision is made probabilistically based on the utility derived from each option.
The utility calculation incorporates both monetary and temporal costs. Waiting time is valued at a fixed rate per hour—reflecting the opportunity cost of delay—while electricity and service fees are drawn from time-of-use pricing schemes. The model also includes random utility components to capture unobserved preferences, ensuring that not all users behave identically under the same conditions.
Crucially, the simulation runs for full 24-hour cycles representing both weekdays and holidays, acknowledging that demand patterns differ significantly between these periods. Holiday traffic, for instance, tends to be heavier and more concentrated, leading to peak loads that can overwhelm under-provisioned systems. By modeling both scenarios, the planners can design infrastructure that performs well year-round, not just on average days.
With user behavior simulated, the next step is optimization. The researchers formulate a multi-objective planning problem aimed at minimizing two key metrics: annual user cost and annual station cost. The former includes the weighted average of charging and swapping expenses across all users, while the latter encompasses capital investment, operation and maintenance, and grid connection fees. Constraints ensure that equipment quantities remain within feasible limits, transformer capacity is not exceeded, and waiting times stay below acceptable thresholds.
Solving this multi-objective problem requires advanced algorithms. The team employs NSGA-II, a genetic algorithm known for its ability to generate a Pareto front of optimal solutions—sets of trade-offs where improving one objective would worsen another. From this frontier, decision-makers can select a solution based on their priorities, whether that’s minimizing user cost, reducing capital expenditure, or balancing both.
To validate their approach, the researchers applied it to a real-world case study based on a simplified section of China’s highway network, featuring seven toll stations and five interchanges. After projecting 2035 traffic levels and applying an EV penetration rate of 53.49%, they found that the service area would see approximately 3,735 vehicles per day on weekdays, rising to over 7,000 during holidays. Assuming a 30% entry rate into the service area, this translates to hundreds of daily EV stops requiring energy replenishment.
Four planning scenarios were compared. The first involved only charging stations, offering no swapping option. The second and third assumed fixed proportions of users choosing swapping—70% and 20%, respectively—without modeling actual behavior. The fourth, and most sophisticated, used the full behavioral simulation.
The results were striking. When annual station costs were held constant, the scenario with simulated user behavior achieved the lowest user cost—about 2% lower than the simplified models and 4% lower than the charging-only option. Conversely, when user cost was fixed, the simulated-behavior approach required 14% less investment than the charging-only model, demonstrating superior economic efficiency.
Moreover, the optimized design significantly improved grid performance. During holiday peaks, the combined charging and swapping system reduced peak load by up to 15% compared to a charging-only setup. This “load shifting” effect occurs because swapping allows users to bypass long charging queues, effectively transferring some demand away from the electrical grid to the station’s pre-charged battery inventory. This not only enhances user experience but also reduces stress on local transformers and distribution networks.
The study also revealed important insights into user behavior. On weekdays, when traffic is lighter and queues shorter, cost tends to dominate decision-making. Many users opt for charging during off-peak hours when electricity prices are low, even if swapping is faster. On holidays, however, time becomes a more critical factor. With charging stations often crowded, users are more willing to pay a premium for the speed and convenience of swapping, leading to a higher swap adoption rate—54.4% compared to 37.7% on weekdays.
The researchers further explored how changes in pricing and vehicle compatibility affect outcomes. When the weight given to cost in the decision model was increased, more users shifted from swapping to charging, confirming that price sensitivity plays a major role. Even more telling was the impact of vehicle compatibility: as the share of EVs capable of swapping rose from 25% to 100%, the number of swap transactions increased steadily, indicating strong latent demand for this service when available.
These findings carry significant implications for policymakers and infrastructure developers. First, they underscore the importance of co-locating charging and swapping facilities. While charging will likely remain the dominant mode due to its lower infrastructure cost and broader vehicle compatibility, swapping offers a valuable complementary service that can absorb peak demand and improve overall system resilience.
Second, the study highlights the risks of oversimplified planning assumptions. Models that assume fixed user behavior or ignore temporal variations in demand may lead to suboptimal designs—either overbuilt stations with idle capacity or underbuilt ones that fail during peak periods. By incorporating behavioral realism, planners can avoid these pitfalls and achieve better outcomes with fewer resources.
Third, the research supports the case for dynamic pricing strategies. Since user choices are sensitive to cost, time-based tariffs can be used to influence behavior and smooth demand. For example, lowering swap service fees during peak hours could encourage more users to switch modes, reducing congestion at charging points.
Finally, the work points to the need for greater standardization in battery design and swapping protocols. The model assumes that all swapping-capable vehicles use the same battery type, which is currently true only for fleets within a single brand or operator. For swapping to reach its full potential, industry-wide compatibility will be essential.
Looking ahead, the authors note that their current model focuses on a single service area. Future work will extend the framework to network-level planning, optimizing the placement and sizing of multiple stations across a highway corridor. This could enable coordinated load management, where stations with excess capacity help balance those under strain, further enhancing system efficiency.
In conclusion, the research by Hu Junjie, Li Ruzhou, and Liu Xuetao represents a significant advance in the field of EV infrastructure planning. By placing user behavior at the center of the design process, they have developed a methodology that is not only more accurate but also more humane—recognizing that technology should serve people, not the other way around. As the world transitions to electric mobility, such user-centric approaches will be essential to building a charging network that is not just functional, but truly sustainable.
Smart Planning of Highway Charging and Swapping Stations Boosts Efficiency and User Satisfaction
Hu Junjie, Li Ruzhou, Liu Xuetao, North China Electric Power University, Power System Technology, DOI: 10.13335/j.1000-3673.pst.2024.0201