New Charging Strategy Tackles Mountain City EV Challenges
As electric vehicles (EVs) continue to surge in popularity across China, urban planners and energy experts face a growing challenge: how to efficiently support EV charging in cities with complex terrain. While much of the existing research and infrastructure planning has focused on flat, grid-like urban environments, mountainous cities present a unique set of obstacles that traditional models fail to address. Steep inclines, significant altitude changes, and winding roads dramatically increase vehicle energy consumption, altering both the timing and location of charging demand. A groundbreaking study led by researchers from Chongqing University of Posts and Telecommunications and State Grid Chongqing Electric Power Company has now introduced a novel approach to EV charging infrastructure planning specifically designed for mountainous urban landscapes. Their findings, published in the Electric Power System Technology journal, offer a transformative solution that could reshape how cities from Chongqing to Bogotá manage their EV ecosystems.
The research team, spearheaded by Dr. Hongyu Long, You Zhou, and Fangxing Chen from the Chongqing Key Laboratory of Complex Systems and Bionic Control, recognized a critical gap in current EV planning methodologies. Most existing models assume a two-dimensional road network, calculating energy use based on horizontal distance and average consumption rates. This works well for cities like Beijing or Chicago but falls apart in places like Hong Kong or Medellín, where a short horizontal distance can involve a massive vertical climb. In such environments, EVs consume significantly more power to overcome gravity, a factor that not only drains batteries faster but also influences driver behavior, including when and where they choose to charge. Ignoring this reality leads to inaccurate load forecasts, poorly placed charging stations, and inefficient use of grid resources.
The core of the team’s innovation lies in a comprehensive, three-dimensional modeling framework that integrates the physical realities of mountainous terrain into every stage of the planning process. Their approach moves beyond the simplistic assumption of flat roads, instead creating a detailed spatial model that accounts for elevation differences between road nodes. This allows for a far more accurate calculation of individual vehicle energy consumption. When a car travels from point A to point B in a hilly city, the model doesn’t just measure the distance on a map; it calculates the actual path length, including the vertical component of every hill and slope. This results in a “real-world” energy consumption figure that is often 1.5 times higher than predictions made by traditional, flat-earth models. This seemingly small adjustment has profound implications. It means that an EV in a mountain city might need to charge after a 20-kilometer trip, while its counterpart in a flat city could go 30 kilometers on the same battery level.
But the researchers didn’t stop at individual vehicle modeling. They understood that the true challenge lies in predicting the collective behavior of thousands of EVs across a city. To achieve this, they developed a sophisticated group charging load prediction model. This model uses real-world traffic flow data, monitored at regular intervals, to estimate how many vehicles pass through each intersection or road segment over time. By applying an EV penetration rate and a fast-charging demand ratio, they can predict the number of EVs that will need a charge during any given period. A key insight in their methodology is the recognition of data redundancy; the same vehicle might be counted multiple times as it moves through different monitoring points. To correct for this, they introduced a refinement factor based on average vehicle speed and monitoring interval, ensuring that their predictions reflect actual vehicle counts, not inflated numbers from repeated observations.
The most significant advancement, however, is their use of an improved Floyd shortest path algorithm. Unlike standard navigation systems that prioritize the shortest distance or fastest time, this algorithm is reprogrammed to find the path with the lowest energy consumption. In a mountain city, this often means choosing a longer, gentler route over a shorter, steeper one. This reflects real-world driver behavior, where range anxiety pushes users to conserve energy. By simulating millions of such trips using Monte Carlo methods, the model can predict not just how much energy will be consumed, but also where and when drivers are likely to seek a charge. This creates a dynamic, time-series forecast of charging demand across the entire city network, capturing the ebb and flow of load throughout the day.
This detailed load forecasting is then seamlessly integrated into a new charging station planning framework. The traditional approach treats planning as a one-off decision: find the best locations based on a static demand model. The Chongqing team’s method, however, is iterative and dynamic. It acknowledges a crucial feedback loop: the placement of a charging station directly influences driver behavior, which in turn alters the very load distribution the planners are trying to predict. If a station is placed in a high-traffic area, it will naturally attract more users, potentially overloading that location while leaving others underutilized. Their solution is an iterative optimization process. The model starts with a set of potential station sites, runs the load prediction, evaluates the results, and then adjusts the station locations to improve the outcome. This cycle repeats until an optimal configuration is found.
The optimization is guided by two primary objectives: minimizing temporal load fluctuations and achieving spatial load balance. The first goal aims to smooth out the peaks and valleys in charging demand over time. A station that sees a massive spike in usage during the morning rush hour, followed by hours of inactivity, is inefficient and stressful for the grid. By distributing demand more evenly throughout the day, the system becomes more stable and easier to manage. The second goal ensures that no single station is overwhelmed while others sit idle. An equitable distribution of load across multiple stations leads to better user experience, reduced wait times, and a more resilient infrastructure. The model also incorporates practical constraints, such as ensuring that no driver is ever more than their remaining battery range from a charging station, and that each station has enough charging points to handle its projected demand without excessive queuing.
To test their model, the researchers applied it to a real-world network of 20 nodes and 31 road segments in a southwestern Chinese mountain city. They used historical traffic data from 2012, projected it forward to a 2022 planning horizon, and compared the results of their mountain-aware model against a traditional, flat-terrain model. The differences were stark. The optimal station locations were completely different. The traditional model recommended placing stations at nodes 4 and 8, which might be central in a two-dimensional map. In contrast, the mountain-aware model identified nodes 5 and 14 as the best locations, areas that better serve the high-energy-consumption routes and balance the overall load.
The performance of the optimized network was dramatically superior. In the traditional model’s best plan, one station faced a peak load of 3,180 kW while the other peaked at 2,580 kW, a significant imbalance. In the mountain-aware model’s optimal plan, the two stations had peak loads of 3,180 kW and 3,000 kW, a much more balanced distribution. More importantly, the load curves for each station were smoother, with smaller differences between peak and off-peak hours. This reduction in load fluctuation is critical for grid operators, as it reduces stress on transformers, minimizes voltage fluctuations, and lowers the need for expensive grid upgrades. The model also showed that the required number of charging points could be more accurately sized, preventing both under-provisioning and costly over-investment.
The implications of this research extend far beyond Chongqing. Cities around the world with challenging topography—from Lisbon and Pittsburgh to Sapporo and Denver—can benefit from this more nuanced approach. As the global push for electrification accelerates, it is clear that a one-size-fits-all solution for EV infrastructure is no longer viable. Urban environments are too diverse, and the consequences of poor planning are too severe. An overloaded charging station in a mountainous area isn’t just an inconvenience; it can strand drivers on steep hills, create dangerous traffic bottlenecks, and undermine public confidence in EV technology.
The work of Long, Zhou, Chen, and their colleagues provides a blueprint for a smarter, more adaptive future. Their methodology shifts the paradigm from reactive to proactive planning. Instead of building stations based on guesswork or outdated models, city planners can now use a data-driven, physics-informed approach that accounts for the true cost of driving in a hilly environment. This leads to infrastructure that is not only more efficient but also more equitable, ensuring that all parts of a city are served fairly.
Moreover, the study highlights the importance of interdisciplinary collaboration. It brings together expertise in complex systems, bionic control, power engineering, and transportation science. This holistic view is essential for solving the multifaceted challenges of the modern energy transition. The integration of advanced algorithms like the genetic algorithm for optimization demonstrates the power of computational tools in tackling real-world problems. It’s not just about building more chargers; it’s about building the right chargers, in the right places, at the right time.
Looking ahead, this research opens the door to even more sophisticated models. Future iterations could incorporate real-time traffic data, weather conditions (which also affect EV range), and dynamic pricing signals to further refine load management. The model could also be adapted for different vehicle types, from compact city cars to heavy-duty electric trucks, each with their own energy consumption profiles on steep grades. The ultimate goal is a fully integrated, intelligent transportation and energy system where charging infrastructure seamlessly adapts to the needs of the city and its drivers.
In conclusion, the study by Long, Zhou, Chen, Xiaorui Hu, Tingting Xu, and Yi Long from the Chongqing Key Laboratory of Complex Systems and Bionic Control and the State Grid Chongqing Electric Power Company Marketing Service Center presents a major leap forward in EV infrastructure planning. By rigorously accounting for the unique challenges of mountainous terrain, they have developed a method that produces more accurate load forecasts, more balanced station utilization, and a more stable grid. This is not just an academic exercise; it is a practical, scalable solution that can help cities around the world build a more sustainable and resilient electric future. As the world continues its journey toward decarbonization, such innovative, context-sensitive research will be essential for ensuring that the transition is not only green but also smart and equitable.
Hongyu Long, You Zhou, Fangxing Chen, Xiaorui Hu, Tingting Xu, Yi Long, Chongqing Key Laboratory of Complex Systems and Bionic Control, State Grid Chongqing Electric Power Company Marketing Service Center, Electric Power System Technology, DOI: 10.19725/j.cnki.1007-2322.2022.0195