Professor Zhixi Wan
21 January 2026
In last week’s column, I did an in-depth analysis of the structural contradictions confronting Hong Kong’s electric vehicle (EV) charging market: acute scarcity of land and limitations in power capacity. Pursuing extensive growth by simply “piling up hardware” is decidedly ill-advised. The urgent priority is to develop a heterogeneous charging infrastructure network, precisely configured to different scenarios and user needs, i.e. blending fast and slow charging with targeted alignment to market demand.
Undoubtedly, the charging network is of paramount importance. The coexistence of fast and slow charging tackles the issue of resource allocation in physical space, while precise alignment hinges on the data flow within the information space.
Turning challenges into resources
Promoting intelligent charging technologies to facilitate demand response can convert charging behaviour from being a burden on the power grid into a dispatchable resource. As defined by the International Renewable Energy Agency, intelligent charging uses data connectivity to enable EV charging cycles to adapt to power system conditions and vehicle owners’ energy needs. This technological pathway can be divided into two main phases. One is V1G, i.e. unidirectional smart charging. Through unidirectional power control, the grid can remotely control charging time and rate, e.g. delaying charging or reducing power. The another one is V2G, i.e. vehicle to grid. Currently still in the stage of commercial exploration, it is aimed at equipping EVs not only to draw power from the grid, but also to feed stored energy from their batteries back into the grid.
The most direct way to understand the practical value of intelligent charging is to examine leading enterprises in ultra-large-scale markets. Mainland China has the largest and most competitive public charging market in the world. As of November 2025, the number of public charging piles had already exceeded 4.625 million. This scale effect has given rise to operators that are global leaders in technology, operations, and safety management.
Among the first to play a vital role are map service providers that provide integrated information. Represented by Amap and Baidu Maps, these internet platforms benefit from the comprehensive, multi-dimensional integration of underlying geographic data to serve as the main gateway for the vast majority of EV users in Mainland China to access complete, real-time information. Leveraging their advantage in users’ navigation habits, the map platforms have successfully prompted mainstream charging operators to supply the real-time data required for integration. As a result, instead of switching repeatedly among apps from different charging operators to make comparison, EV users simply search for a charging station in a map app. They can instantly obtain detailed information, from station scale, equipment status, and real-time prices to user reviews, and complete a seamless closed loop from locating and navigating a charging pile, all the way through to payment.
At present, Hong Kong’s charging market is highly fragmented. Although the Environmental Protection Department has launched the “EV-Charging Easy” mobile app, which integrates public charging information across the city, most private operators do not provide real-time information, while their payment interfaces and user account systems are not integrated and interconnected. Whether by encouraging market-oriented operations through policies and regulations to promote data integration, or by leveraging data trusts to achieve industry integration, it is necessary to establish a sufficiently integrated, standardized data platform. At the practical level, the Personal Data (Privacy) Ordinance strictly regulates data collection and use, especially with regard to physical tracking. How to establish a multi-party EV charging data platform that both meets compliance requirements and serves Hong Kong car owners remains a challenge that calls for active resolution.
Lessons from the Mainland’s experience
While map service providers should optimize the real-time integration of information, leading charging operators are committed to efficiently solving the problem of spatiotemporal matching. In this domain, Mainland platforms such as TELD, StarCharge, and Orange Charging showcase how artificial intelligence algorithms and digital technologies can be used to forecast, respond to, and regulate charging demand. Among them, Orange Charging, an offshoot of China’s largest ride-hailing platform DiDi Chuxing, is a bottom-up operator natively driven by market competition and massive data.
According to data published on its official website, as of the end of 2024, Orange Charging had provided more than one billion charging sessions. Its most core and frequent users are ride-hailing drivers, who have stable demand and are the most sensitive to the time cost of charging and to price. The DiDi platform analyses and forecasts the spatiotemporal distribution of operational vehicles and charging demand through its massive vehicle trajectory data. Through algorithms, limited-time offers in real time are sent to ride-hailing drivers’ apps, attracting them to go to designated charging stations to recharge.
This kind of intelligent operation uses pricing leverage to guide demand towards idle charging piles, thereby achieving network-wide optimization. With operational capabilities in forecasting and real-time dispatch, advanced charging operators can develop demand-response-based virtual power plants, enabling them to participate in electricity market trading and settlement. Hence, they not only serve energy consumers, but also become active participants in the electricity market, engaging in peak shaving, valley filling, and ancillary services.
Opportunities created by the transformation megatrend
In the case of Hong Kong, both taxis and minibuses are undergoing electrification. With relatively fixed routes and regular, high-frequency charging needs, these vehicles are an ideal form of predictable load. The SAR Government should guide the market to develop smart charging platforms for private cars, taxis, ride-hailing vehicles, and minibuses, using real-time data and AI to optimize their charging routes, reduce their use of fast-charging resources during peak hours, and pave the way for future V2G pilot projects.
Establishing Hong Kong as an international showcase of the EV industry is not simply about incentivizing the installation of more, advanced charging piles, but about creating a closed-loop ecosystem that integrates “infrastructure + algorithms + data”. Hong Kong’s electrification transition has already entered deep waters where opportunities and challenges coexist. From the initial rollout of charging hardware to solve the problem of finding a charging spot to today’s systemic predicament of long waits for charging, a healthy charging ecosystem should be an organism deeply integrated with the urban fabric and the rhythm of city life. Beyond recognizing and respecting different needs across scenarios, it should also support energy and public resources to serve society as a whole more effectively.
