As the adoption of battery EV cars accelerates globally, the number of charging piles has surged, making their metrological performance critical for user trust and industry growth. Regulatory bodies have mandated periodic verification of these piles, but traditional manual methods are inefficient for the vast scale involved. In this article, I propose an online verification method that leverages intensive standard device reuse, addressing the high costs and labor intensity of existing approaches. By integrating user participation and streamlined device design, this method enhances verification efficiency while minimizing expenses, offering a scalable solution for the evolving infrastructure of battery EV cars.
The verification of charging piles for battery EV cars typically involves comparing the electrical energy measured by a high-accuracy standard device against the pile’s displayed value under specific load conditions. Traditional manual methods require inspectors to transport bulky load equipment and verification instruments to each site, leading to significant time waste and logistical challenges. Common manual verification schemes can be summarized in the following table:
| Scheme Type | Description | Drawbacks |
|---|---|---|
| Independent Verification Instrument and Load | Separate devices for measurement and load simulation, connected sequentially to the charging pile. | Cumbersome transportation, high energy consumption during verification. |
| Integrated Verification Instrument and Load | Combined into a single portable unit for easier handling. | Still involves heavy load devices, inefficient for dispersed locations. |
| Vehicle-Mounted Integrated System | Integrated system mounted on a vehicle for mobility between sites. | Increased cost, does not reduce travel time between distant charging stations. |
These methods rely on actual load devices, which consume energy during verification. While virtual load schemes reduce energy waste, they fail to address the inefficiency of inspector travel time, especially as charging piles for battery EV cars are often widely distributed. Recent explorations into online verification involve retrofitting piles with additional metering modules for remote data transmission, but the high retrofit costs hinder widespread adoption. Therefore, I have developed a novel online verification method that circumvents these issues by reusing intensive standard devices at charging stations.
My proposed method is based on the reuse of standard devices within a charging station, utilizing the battery EV car itself as the verification load. The principle involves deploying a few intensive standard devices at each station, managed by maintenance personnel. Users of battery EV cars are incentivized to connect these devices between their vehicle and the charging pile during routine charging sessions. The standard device measures the electrical energy with high accuracy, while the charging pile’s displayed energy is captured via the operator’s app. Data from both sources are transmitted to a supervisory platform for error calculation, enabling online verification without modifying the piles. This approach decouples inspectors from the verification process, significantly reducing labor costs and travel time. The key aspects include:
- Intensive Design of Standard Device: The device must be lightweight, user-friendly, and cost-effective to encourage adoption by users of battery EV cars.
- User Incentives: Motivations such as discounts or rewards are provided to users for using the standard device, ensuring broad coverage across piles.
The verification error is computed by comparing the standard energy value \(E_s\) from the device with the displayed energy \(E_x\) from the pile. The working error \(E_{error}\) can be expressed as:
$$ E_{error} = \frac{E_s – E_x}{E_x} \times 100\% $$
where \(E_s\) represents the accumulated electrical energy measured by the standard device, and \(E_x\) is the settled energy from the charging pile’s transaction record. This formula aligns with metrological standards, ensuring accuracy within specified limits for battery EV car charging infrastructure.
To facilitate this method, I have designed an intensive standard device tailored for online verification of charging piles serving battery EV cars. The device is compact, weighing only 2.8 kg, with ergonomic handles for easy connection to both the vehicle’s charging port and the pile’s connector. It incorporates robust features like dustproofing, shock resistance, and waterproofing to enhance durability and safety during user interactions. The functional modules of the device are outlined below:
| Module | Function | Specifications |
|---|---|---|
| Electrical Energy Measurement | Accurately measures cumulative energy during charging as the standard value. | Error < 0.2%, compliant with verification regulations. |
| Communication | Transmits data (e.g., energy, location, temperature) to the supervisory platform via 5G. | Supports real-time data upload for remote monitoring. |
| Environmental Temperature Sensing | Monitors ambient temperature to ensure verification conditions are met. | Measurement error ≤ ±1°C. |
| Time and Positioning | Uses built-in BeiDou and network timing for precise timestamping and location. | Time error < ±1 s, position error < ±10 m. |
| Data Security | Implements encryption and electronic seals to prevent tampering. | Ensures integrity and confidentiality of verification data. |
The device’s operational workflow involves the user connecting it to their battery EV car and the charging pile, initiating a charging session through the operator’s app. Post-charging, the user uploads the transaction screenshot to the supervisory platform, while the standard device sends \(E_s\) via 5G. The platform computes the error using the aforementioned formula, completing the verification. This process seamlessly integrates with daily charging activities of battery EV cars, minimizing disruption.
The intensive standard device’s design leverages advanced metrology and communication technologies. Its energy measurement module utilizes a high-precision chip that samples voltage \(V\) and current \(I\) to compute instantaneous power \(P(t)\) and accumulated energy \(E_s\). The power calculation is given by:
$$ P(t) = V(t) \times I(t) $$
where \(V(t)\) and \(I(t)\) are sampled values over time. The accumulated energy \(E_s\) is then:
$$ E_s = \int_{t_0}^{t_f} P(t) \, dt $$
with \(t_0\) and \(t_f\) denoting the start and end times of charging, respectively. This integration ensures accurate measurement for varying load conditions typical of battery EV cars. The device’s communication module employs a multi-mode system combining 5G, Bluetooth, and BeiDou, enabling reliable data transmission even in diverse environments. Data security is reinforced through cryptographic protocols, where transmitted data \(D\) is encrypted as \(D_{enc} = \text{Encrypt}(D, K)\) using a secure key \(K\), preventing unauthorized access or manipulation.
In practice, the online verification method offers substantial benefits over traditional approaches. By reusing standard devices at charging stations, it reduces the need for multiple expensive instruments and minimizes inspector involvement. The table below compares key metrics between traditional manual verification and the proposed online method:
| Aspect | Traditional Manual Verification | Proposed Online Method with Device Reuse |
|---|---|---|
| Cost per Verification | High due to equipment transport and labor. | Low, leveraging user participation and device reuse. |
| Verification Efficiency | Limited by travel time between dispersed piles. | High, as verification occurs during user charging sessions. |
| Scalability for Battery EV Car Growth | Poor, requires proportional increase in inspectors and devices. | Excellent, easily scales with charging station deployments. |
| Environmental Impact | Energy waste from load devices during verification. | Minimal, uses the battery EV car as a natural load. |
| User Convenience | None, as verification is separate from charging. | Enhanced, with incentives for participation in verification. |
This method not only streamlines verification but also fosters trust among users of battery EV cars by ensuring metrological accuracy without intrusive modifications. The intensive design of the standard device further supports adoption, as its lightweight nature and simple operation align with user expectations for convenience in charging infrastructure.
Looking ahead, the integration of this online verification method with smart grid technologies could enhance its utility. For instance, data from standard devices could be analyzed to predict maintenance needs for charging piles, improving reliability for battery EV cars. Additionally, the method’s reliance on user participation introduces a collaborative element, where incentives can be dynamically adjusted based on verification coverage rates. The error calculation can be extended to include uncertainty analysis, where the combined standard uncertainty \(u_c(E_{error})\) is derived from uncertainties in \(E_s\) and \(E_x\):
$$ u_c(E_{error}) = \sqrt{ \left( \frac{\partial E_{error}}{\partial E_s} \right)^2 u^2(E_s) + \left( \frac{\partial E_{error}}{\partial E_x} \right)^2 u^2(E_x) } $$
where \(u(E_s)\) and \(u(E_x)\) are the standard uncertainties of the standard device and charging pile measurements, respectively. This refinement ensures metrological rigor in long-term deployments.
In conclusion, the online verification method based on intensive standard device reuse presents a viable solution for the mass verification of charging piles in the era of battery EV cars. By combining user-centric design, cost-effective device reuse, and advanced data communication, it overcomes the limitations of traditional manual methods and high-cost retrofitting schemes. This approach not only boosts verification efficiency but also supports the sustainable growth of charging infrastructure, ultimately benefiting consumers and regulators alike. As the adoption of battery EV cars continues to rise, such innovative metrological strategies will be essential for maintaining accuracy and trust in the evolving transportation ecosystem.