With the rapid growth of electric vehicles, the number of EV charging stations has increased significantly. The metering performance of EV charging stations is crucial for user trust and industry development, leading regulatory authorities to include them in mandatory verification catalogs requiring periodic checks. Traditional verification methods involve manual processes where inspectors carry load devices and calibrators, resulting in low efficiency due to the dispersed distribution of EV charging stations. Although virtual load verification methods reduce the weight of load equipment, they do not address the time inspectors spend traveling between sites. Consequently, online verification approaches have been explored, such as adding or modifying energy metering units in EV charging stations and transmitting encrypted data to supervision platforms. While effective, these methods face high retrofitting costs for the vast number of existing EV charging stations, limiting their scalability. To address these challenges, we propose an online verification method for EV charging stations based on the reuse of intensive standard devices. This approach leverages集约化 design of standard devices, station-level reuse, and data communication with servers to overcome the inefficiencies of manual verification and the high costs of retrofitting-based online methods, enabling efficient and scalable verification of EV charging stations.
Common manual verification methods for EV charging stations typically involve comparing the energy measured by a high-accuracy standard device with that of the station under test under specific load conditions. According to relevant verification regulations, standard devices and load equipment are essential components. The primary manual verification schemes include: independent field calibrators and verification loads, integrated field calibrators and verification loads, and vehicle-mounted integrated designs. These methods are illustrated in the figure below, which shows the configurations for connecting standard devices to EV charging stations and loads.
In the independent scheme, the field calibrator measures the output parameters of the EV charging station, while the load device simulates various charging conditions. This allows for precise calibration and traceability but suffers from poor portability due to separate components. The integrated design combines the calibrator and load into a single unit, improving portability but still facing challenges related to the heavy weight of load devices. The vehicle-mounted version further enhances mobility but increases costs. All these schemes use real load devices, leading to energy waste during verification. Virtual load methods, as suggested in some regulations, reduce energy consumption but do not minimize travel time for inspectors moving between dispersed EV charging stations. Thus, these methods struggle to meet the demand for verifying large numbers of EV charging stations efficiently.
To improve efficiency, some regions have explored online verification systems that avoid moving verification equipment. For instance, remote monitoring platforms combined with virtual load verification and tamper-evident seals enable data collection from IoT-modified EV charging stations. Alternatively, internal online detection modules can be added to EV charging stations to collect and transmit metering data to supervision platforms. These methods eliminate the need for load devices by using customers’ electric vehicles as loads and reduce inspector travel time through data transmission. However, the retrofitting costs for existing EV charging stations remain a significant barrier to widespread adoption.
We propose an online verification method for EV charging stations that reuses standard devices intensively. This method decouples standard devices from inspectors by deploying a few standard devices per charging station, maintained by station operators. Customers use these standard devices during charging, connecting them to their vehicles (as loads) and the EV charging stations under test. The standard device measures the energy delivered, and the data is transmitted to a supervision platform, while the station’s settlement data is uploaded by the customer. This approach reduces the need for inspector travel and lowers costs while maintaining verification accuracy. The key aspects include the intensive design of the standard device to ensure simplicity, light weight, and low cost, and user incentives, such as discounts or rewards, to encourage adoption and cover multiple EV charging stations.
The principle of the online verification method is shown in the block diagram below. The intensive standard device connects between the electric vehicle and the EV charging station, measuring the standard energy value \( E_S \) during charging. This value is transmitted via 5G IoT communication to the Electric Vehicle Charging Facility Remote Metrological Supervision Platform (EVCC). Simultaneously, the customer uploads the settlement data \( E_X \) from the charging station operator’s app to the EVCC platform. The working error of the EV charging station is calculated using the formula for relative error:
$$ \delta = \frac{E_X – E_S}{E_S} \times 100\% $$
where \( \delta \) represents the percentage error, \( E_X \) is the energy value from the EV charging station, and \( E_S \) is the standard energy value from the intensive standard device. This error calculation ensures compliance with verification regulations, typically requiring errors within ±0.5% for EV charging stations. The process integrates charging and verification, improving efficiency for large-scale deployment.
The intensive standard device is designed to be user-friendly, lightweight, and cost-effective, weighing approximately 2.8 kg. It features a handle for easy connection to vehicle charging ports and EV charging station connectors, along with dustproof, shockproof, and waterproof capabilities to enhance durability and safety. The device includes anti-tampering mechanisms and encrypted data transmission to prevent manipulation. A summary of its key specifications is provided in Table 1.
| Feature | Specification |
|---|---|
| Weight | 2.8 kg |
| Energy Measurement Error | < 0.2% |
| Communication | 5G, Bluetooth,北斗 |
| Temperature Measurement Error | ±1 °C |
| Time Accuracy | ±1 s |
| Position Accuracy | ±10 m |
| Data Security | Encryption, Electronic Seals |
The functional block diagram of the intensive standard device comprises several modules: an energy metering module, a communication module, an environmental temperature measurement module, a time and position module, and a data security module. The energy metering module uses a precision metering chip to sample voltage and current, calculate power and energy, and control the metering process. The energy value \( E_S \) is computed as:
$$ E_S = \int P \, dt = \int V \cdot I \, dt $$
where \( P \) is the instantaneous power, \( V \) is the voltage, and \( I \) is the current. The measurement error is maintained below 0.2% through high-accuracy components and calibration. The communication module supports 5G, Bluetooth, and北斗 for data transmission, enabling real-time updates to the EVCC platform. The temperature module ensures accurate environmental monitoring, which is critical for compensating measurements under varying conditions. The time and position module uses北斗 for synchronization and localization, with errors within ±1 s and ±10 m, respectively. Data security is enforced via encryption and electronic seals to prevent unauthorized access or modification.
The user workflow for verification with the intensive standard device involves the following steps: First, the user connects the standard device to the electric vehicle’s charging port and then attaches the EV charging station’s connector to the device. Next, the user scans the EV charging station with the operator’s app to initiate charging. After charging, the app provides a settlement record including \( E_X \), which the user screenshots and uploads to the EVCC platform via a dedicated app. The standard device automatically transmits \( E_S \) to the platform, and the error is calculated. Finally, the user disconnects the device, completing the verification. This process is summarized in Table 2.
| Step | Description |
|---|---|
| 1 | Connect intensive standard device to electric vehicle and EV charging station. |
| 2 | Use operator app to start charging and record settlement data \( E_X \). |
| 3 | Standard device measures and transmits standard energy \( E_S \) to EVCC platform. |
| 4 | User uploads settlement screenshot to EVCC platform. |
| 5 | Platform calculates error \( \delta \) using \( \delta = \frac{E_X – E_S}{E_S} \times 100\% \). |
| 6 | Disconnect device; verification complete. |
To encourage user participation, incentive mechanisms are essential. For example, users who employ the standard device at designated EV charging stations could receive credits, discounts, or loyalty points. This not only increases adoption rates but also ensures broader coverage of EV charging stations within a station. The intensive design and reuse model reduce the per-unit cost, making it feasible to deploy multiple devices across various EV charging stations without significant investment.
In terms of performance, the intensive standard device achieves high accuracy in energy measurement, with errors derived from the uncertainty components. The combined standard uncertainty \( u_c \) for energy measurement can be expressed as:
$$ u_c = \sqrt{u_{\text{cal}}^2 + u_{\text{temp}}^2 + u_{\text{time}}^2} $$
where \( u_{\text{cal}} \) is the uncertainty from calibration, \( u_{\text{temp}} \) from temperature variations, and \( u_{\text{time}} \) from time measurement. For instance, if \( u_{\text{cal}} = 0.1\% \), \( u_{\text{temp}} = 0.05\% \), and \( u_{\text{time}} = 0.02\% \), then \( u_c \approx 0.113\% \). Expanding this with a coverage factor \( k=2 \) gives an expanded uncertainty of approximately 0.226%, which is within the required limits for EV charging station verification.
Comparative analysis with traditional methods highlights the advantages of this online approach. As shown in Table 3, the intensive reuse method significantly reduces costs and time while maintaining accuracy. For example, manual methods may require hours per EV charging station due to travel, whereas online verification can be completed in minutes during normal charging operations. This efficiency is crucial for scaling to the millions of EV charging stations expected in the future.
| Method | Cost | Time per Station | Accuracy | Scalability |
|---|---|---|---|---|
| Manual with Independent Devices | High | 1-2 hours | High | Low |
| Integrated Manual Device | Medium | 1 hour | High | Medium |
| Vehicle-Mounted | Very High | 30-60 minutes | High | Medium |
| Retrofit-Based Online | High (retrofit) | Near real-time | High | Medium |
| Intensive Reuse Online | Low | During charging | High | High |
The data transmission in this method relies on secure protocols to protect integrity. The standard device sends data packets containing \( E_S \), timestamp \( t \), position \( p \), and temperature \( T \) to the EVCC platform. The packet structure can be modeled as:
$$ \text{Packet} = \{ E_S, t, p, T, \text{signature} \} $$
where the signature is generated using cryptographic hash functions to prevent tampering. The platform verifies the data upon receipt, ensuring that only valid measurements are used for error calculation. This security is vital for maintaining trust in the verification results for EV charging stations.
In conclusion, the online verification method for EV charging stations based on intensive standard device reuse offers a cost-effective and efficient solution to the challenges of traditional manual verification and high-cost retrofitting approaches. By leveraging集约化 design, station-level reuse, and user incentives, it enables scalable verification without compromising accuracy. The intensive standard device, with its lightweight and secure features, facilitates easy adoption by users, while the integration with supervision platforms ensures reliable error calculation. This method not only reduces operational costs but also supports the growing infrastructure of EV charging stations, contributing to the sustainable development of the electric vehicle ecosystem. Future work could focus on optimizing the device for wider environmental conditions and integrating artificial intelligence for predictive maintenance of EV charging stations.