Electric vehicles are becoming a mainstream mobility option, and their charging needs are now tightly linked to the electricity grid. This interdependence brings efficiency gains but also opens new pathways for cyber-threats that extend beyond the traditional focus on over-the-air (OTA) software updates or the physical security of charging stations.
Researchers from Hong Kong Polytechnic University have identified a subtle yet powerful attack vector that exploits the data fed to driver-facing navigation applications. By feeding false information about charging-price and travel time, an adversary could steer a large fleet of EVs toward the same charging point, creating a sudden surge in power demand that the local distribution network may struggle to absorb.
Cross-domain manipulation of charging navigation
The proposed scenario does not require breaching the hardened communication channels that protect the power grid. Instead, the attacker targets the charging-navigation service, a less protected component that aggregates price, distance, traffic and availability data to suggest the most convenient station to drivers. By injecting a few carefully chosen false data points—what the researchers call a “sparse vector attack”—the system can be tricked into marking a particular charger as cheaper or faster than competitors.
When thousands of vehicles receive the same recommendation, they converge on the selected site within a narrow time window. The resulting concentration of load can push a local transformer or substation beyond its rated capacity, leading to voltage drops, automated load shedding, or even a temporary blackout. The authors describe this as an cross-domain cyberattack because the initial breach occurs in the transportation-information layer while the damage manifests in the power-distribution layer.
Simulation framework and key findings
To assess the feasibility of the attack, the team built a coupled model that merges a synthetic road network with a distribution-grid representation. The model reproduces realistic driver behaviour by feeding navigation-suggested routes into a traffic-flow simulator, then translates the resulting charging sessions into electrical demand profiles for the grid. By altering price signals for a single station in the simulation, the researchers observed a marked shift in vehicle routing decisions.
Results indicated that modifying as few as 2–3 percent of price entries could redirect up to 30 percent of nearby EVs to the targeted charger within an hour. In the simulated grid, this influx produced a peak load increase of 15 percent at the most vulnerable node, sufficient to trigger protective relays in a typical urban distribution system. The study, published in IEEE Transactions on Transportation Electrification, underscores that even minimal data tampering can have outsized effects on grid stability.
Proposed safeguards and monitoring strategies
The authors recommend several practical defenses that focus on the integrity of the navigation data pipeline. First, charging-price feeds should be continuously cross-checked against multiple independent sources, employing anomaly-detection algorithms that flag sudden, unexplained deviations. Second, traffic-flow and demand-prediction models can be integrated into the grid-operator’s situational awareness tools, allowing early identification of abnormal charging-station clustering.
Finally, the research emphasizes the need for end-to-end encryption and authentication between charging-network operators, navigation service providers, and the vehicles themselves. Strengthening these less-protected interfaces would raise the barrier for attackers attempting to inject false information, thereby preserving both the reliability of the power system and the user experience of electric-vehicle drivers.
