Smart Charging Strategy Balances EVs, Grid, and Traffic Flow
A groundbreaking study from Fuzhou University introduces a novel approach to managing the rapidly growing fleet of electric vehicles (EVs), aiming to resolve the complex challenges posed by their widespread adoption. As EV ownership surges, the strain on urban infrastructure—particularly traffic networks, charging stations, and the power grid—has become increasingly apparent. Uncoordinated charging behavior can lead to grid instability, traffic congestion, and long wait times at charging facilities, degrading the user experience and threatening the reliability of essential services. In response, researchers have developed an “ordered charging strategy” that leverages real-time data from vehicles, roads, charging stations, and the power grid to guide drivers to the most efficient and beneficial charging option for everyone involved.
The research, led by Associate Professor Liu Lijun of the College of Electrical Engineering and Automation at Fuzhou University, presents a comprehensive solution that moves beyond the traditional focus on optimizing a single aspect, such as grid stability or the lowest electricity price. Instead, the team’s strategy is built on a holistic “vehicle-road-station-grid” (VRSN) information coupling model. This model treats the transportation and energy systems as deeply interconnected, recognizing that the actions of an individual EV driver ripple out to affect traffic flow, the load on a local substation, and the operational efficiency of a charging station. By creating a unified framework that integrates data from all four domains, the strategy can make intelligent, real-time decisions that balance the often-competing interests of the driver, the transportation network, the charging infrastructure, and the power utility.
The core of this innovation lies in its decision-making engine, which uses a sophisticated combination of analytical methods to determine the optimal charging path for each driver. The process begins by identifying an EV’s need for a fast charge, typically triggered when the battery’s state of charge falls below a certain threshold. At this point, the system springs into action. It first calculates the shortest travel time to every available fast-charging station in the vicinity, but it does so with a critical difference from a standard navigation app. Rather than relying on static maps or average speeds, it uses a dynamic “Floyd” algorithm that incorporates real-time traffic conditions. This means the system knows the current speed on every road segment, which is influenced by the volume of traffic—data that is constantly updated as other vehicles move through the network. This dynamic model provides a far more accurate prediction of travel time, ensuring the driver is not misled by a route that looks short on a map but is currently gridlocked.
However, the journey time is only one factor. The true complexity of the problem is in predicting what happens when the driver arrives at a charging station. Will they have to wait in a long queue? This is where the VRSN model shines. The system doesn’t just look at the physical distance; it evaluates a suite of “charging decision factors” for each potential destination. These factors include the predicted waiting time at the station, which is calculated based on the current number of vehicles charging and the station’s capacity. It also assesses the impact of the driver’s choice on the broader system. For instance, the strategy quantifies the “traffic status index change,” a measure of how much the driver’s chosen route will contribute to congestion on the roads. It evaluates the “charging station equipment utilization,” ensuring that no single station is overwhelmed while others sit idle, promoting a more balanced and efficient use of infrastructure. Finally, it considers the “voltage offset change,” a critical metric for the power grid, which measures the potential drop in voltage at the distribution network node where the station is connected. A large, sudden influx of charging power can cause voltage to sag, potentially affecting other customers and requiring costly grid reinforcements.
To make a final decision from these multiple, and sometimes conflicting, factors, the researchers employed a multi-criteria decision-making technique known as Topsis. This method is designed to find the “best compromise” solution among a set of alternatives. What makes this application particularly robust is the way it determines the importance, or weight, of each decision factor. Instead of relying on arbitrary assumptions, the team used a “combination weighting” approach. This method blends a subjective component, derived from the Analytic Hierarchy Process (AHP), which can incorporate user preferences—for example, a driver who prioritizes minimizing wait time over a slightly longer drive. It is combined with an objective component, using an improved version of the CRITIC method, which analyzes the actual data to determine which factors have the most significant variation and conflict, indicating their real-world importance. This synthesis of subjective user needs and objective system data ensures that the final recommendation is both personalized and systemically sound.
The result is a navigation system that doesn’t just find the nearest charger, but the one that offers the best overall outcome. It might guide a driver to a slightly more distant station if it means a much shorter wait, less impact on traffic, and a more stable grid. This intelligent routing is a key differentiator from previous strategies, which often focused on financial incentives like time-of-use pricing. While pricing is a powerful tool, it operates on a slower timescale and requires user participation. This VRSN strategy, in contrast, can provide real-time, automated guidance that works seamlessly in the background, improving the experience for all users without requiring any change in their behavior.
The benefits of this approach are profound and multi-faceted. For the individual EV driver, the most immediate improvement is a dramatic reduction in charging wait times. In the study’s simulations, the proposed strategy reduced average queue times by a remarkable 75% compared to a scenario where drivers simply chose the nearest charger. This transforms the charging experience from a potential source of frustration and wasted time into a quick and reliable service. The analysis showed that over 90% of drivers using the new strategy could charge within ten minutes, compared to only 75% under the uncoordinated approach. This level of service is essential for encouraging broader EV adoption, as “range anxiety” is increasingly being replaced by “charging anxiety.”
The impact on traffic flow is equally significant. By intelligently distributing charging demand across multiple stations, the strategy prevents the formation of traffic bottlenecks around popular charging hubs. The simulations demonstrated a measurable decrease in the “traffic status index change,” indicating a reduction in overall network congestion. This not only benefits EV drivers but also improves conditions for all road users, reducing travel times and emissions for the entire transportation system. It represents a shift from a reactive model, where traffic jams are managed after they occur, to a proactive one, where demand is managed to prevent them in the first place.
For charging station operators, the strategy offers a path to more efficient and profitable operations. By balancing the load across their network of stations, operators can ensure that their expensive charging equipment is being used more uniformly. The research showed a significant improvement in the “charging station network equilibrium,” meaning the difference between the most and least utilized stations was greatly reduced. This prevents the costly scenario of some stations being overworked and prone to breakdowns while others are underutilized and not generating revenue. It allows operators to maximize the return on their infrastructure investment and provide a more consistent, high-quality service to their customers.
The implications for the power grid are perhaps the most critical. The uncontrolled, simultaneous charging of thousands of EVs can create massive spikes in electricity demand, straining distribution networks and causing voltage drops. The study found that the proposed strategy significantly reduced the overall voltage offset, a key indicator of grid stress. This means a more stable and reliable power supply for all consumers in the area. However, the researchers identified a crucial secondary challenge: slow charging. While the strategy effectively manages fast charging, the overnight “trickle charging” of EVs at homes and workplaces can still create a secondary peak in the evening when drivers return and plug in simultaneously. To address this, the team introduced a complementary “slow charging optimization strategy.”
This second phase of the strategy focuses on EVs that have returned to their starting point, typically their home or office. Instead of charging immediately, the system analyzes the local grid load throughout the driver’s rest period. It then identifies the time window with the lowest electricity demand and schedules the slow charging to occur during that period. This simple yet powerful idea turns millions of parked EVs from a potential problem into a solution. By shifting their charging to off-peak hours, they help to “fill the valley” in the daily load curve, a process known as “valley filling.” This flattens the overall demand profile, reducing the strain on power plants and transmission lines, lowering operational costs for utilities, and increasing the grid’s capacity to accommodate even more EVs in the future. It is a form of demand-side management that leverages the flexibility of EV charging to provide a valuable service to the entire power system.
The validation of this strategy was conducted through a detailed simulation that coupled a standard 33-node power distribution network with a 29-node urban traffic network. The simulation included a diverse fleet of 1,200 EVs, categorized as private cars, taxis, and official vehicles, each with different driving patterns, departure times, and charging needs, reflecting real-world complexity. The results were unequivocal. Compared to an uncoordinated charging scenario, the full strategy—combining intelligent fast-charging navigation with optimized slow charging—led to substantial improvements across all metrics. It achieved the lowest average queue times, the most balanced charging station utilization, the smallest impact on traffic, and the most stable grid voltage. It also demonstrated a significant reduction in overall power losses and the duration of voltage violations on the grid.
One of the most compelling aspects of this research is its practicality and computational efficiency. The authors compared their Topsis-based decision method to powerful optimization algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). While these algorithms are excellent at finding optimal solutions, they are notoriously slow, requiring thousands of iterations. In the simulation, the GA took over twelve hours to compute a solution, which is completely impractical for a real-time navigation system where decisions must be made in seconds. In contrast, the proposed method reached a high-quality decision in just over two minutes. This speed is critical for real-world deployment, as a driver waiting for a charging recommendation cannot afford a delay of more than a few seconds. The study concluded that the combination of speed and solution quality makes this approach uniquely suited for practical implementation.
This research represents a significant leap forward in the integration of electric vehicles into our urban infrastructure. It moves the conversation from simply adding more chargers to intelligently managing the entire ecosystem. The “vehicle-road-station-grid” model provides a powerful framework for understanding the deep interdependencies between transportation and energy systems. By using real-time data and sophisticated decision-making, it transforms EVs from passive consumers of electricity into active participants in a smarter, more resilient, and more efficient network. The strategy not only improves the user experience but also enhances the stability and sustainability of the cities we live in. As the world races toward electrification, solutions like this one from Fuzhou University will be essential for ensuring a smooth and successful transition.
Liu Lijun, Chen Chang, Hu Xin, Lin Yufang, College of Electrical Engineering and Automation, Fuzhou University. High Voltage Engineering, DOI: 10.13336/j.1003-6520.hve.20231078