As the global focus on sustainable development intensifies, electric vehicles have emerged as a representative of eco-friendly transportation, with their rapid growth becoming a significant marker of the new energy vehicle industry’s transformation and upgrading. This expansion has led to a sharp increase in the number of EV charging stations. However, existing EV charging station equipment often suffers from deficiencies in safety and monitoring capabilities, primarily due to the use of 4G network communication modes that face issues like high latency and poor security, potentially posing risks to both the charging equipment and the electric vehicles. Therefore, there is an urgent need for an efficient and secure monitoring system for EV charging stations to ensure the safety of the charging process and the stable operation of the devices.
In response to this, I have designed a comprehensive monitoring system for EV charging stations based on microcontroller technology, incorporating power acquisition devices, environmental acquisition devices, relays, and RedCap communication modules. This system enables the real-time monitoring of key electrical parameters such as operating voltage, current, power, and frequency of the EV charging station, while also collecting environmental data like device temperature, ambient temperature and humidity, and atmospheric pressure. When any monitored parameter exceeds a predefined safety threshold, the system immediately controls a relay to cut off the power output and sends an alarm message to a server, thereby effectively protecting the charging equipment and the electric vehicle. Additionally, the system utilizes RedCap network technology, which supports non-cell modes and network slicing, ensuring low-latency and high-security data transmission, significantly enhancing the timeliness and reliability of EV charging station monitoring and management. The system also features dynamic adjustment of safety thresholds based on environmental conditions, further improving its flexibility and adaptability, contributing to the safe development of the electric vehicle industry.
The overall design of the EV charging station monitoring system comprises several key components: a control module, power acquisition devices, environmental acquisition devices, a display module, an alarm device, a communication module, as well as base stations and an IoT platform. In this system, the power acquisition devices are responsible for collecting parameters such as output voltage, current, power, power factor, and frequency from the EV charging station. The collected data is transmitted via a serial interface to the control module. Environmental acquisition devices gather data on the operating temperature of the EV charging station equipment, along with ambient temperature, humidity, atmospheric pressure, and altitude. Temperature and humidity data are sent to the control module through a single-wire bus, while atmospheric pressure and altitude data are transmitted via an I2C bus. The control module processes the received data and displays it on the display module. Additionally, the control module normalizes the data, accumulates the processed values, and determines the current status based on the accumulated value, including states such as reminder, warning, alarm, and emergency control. Depending on the status, the system uses audible and visual alarms to alert users and transmits data to the IoT platform via the communication module for remote warnings and data statistics. Users can also remotely set parameters and control the charging status through the IoT platform.
The hardware design of the EV charging station monitoring system primarily includes an STM32F103 microcontroller control system, an IM1281 power acquisition module, a DS18B20 temperature module, a DHT11 temperature and humidity module, a 0.96-inch OLED display module, a BMP280 atmospheric pressure module, a RedCap communication module, and an audible and visual alarm module. The STM32F103 microcontroller is a high-performance 32-bit microcontroller based on the ARM Cortex-M3 core, featuring 128 KB of flash memory and 20 KB of SRAM, with a maximum operating frequency of 72 MHz, meeting the demands of real-time processing and control. This microcontroller is equipped with rich peripheral interfaces, including multi-channel 12-bit analog-to-digital converters (ADC), three serial communication interfaces (USART, SPI, and I2C), and PWM outputs, greatly facilitating interaction and control with other devices. Moreover, the STM32F103 microcontroller offers excellent power performance, supporting multiple low-power modes, making it suitable for battery-powered applications.
The IM1281 power acquisition module is a power monitoring module that uses an industrial-grade dedicated energy metering SoC chip, with dual isolation for voltage and current, high integration, and a compact size, making it easy to integrate into various embedded systems. This module can measure AC voltage, current, power, power factor, frequency, and other data in the range of 45–65 Hz, employing RMS measurement methods for high accuracy, and supports energy data storage during power outages. The power acquisition module communicates with the MCU via a serial interface using the Modbus protocol, ensuring good compatibility and ease of development. The RedCap communication circuit is designed using China Mobile IoT’s MR880A module, which incorporates advanced RedCap technology tailored for IoT devices requiring low power consumption, low cost, and high performance. The module supports 5G NR and LTE networks, providing high-speed data transmission services while maintaining low power consumption, making it ideal for IoT applications. Under 5G NR, it achieves maximum downlink speeds of up to 226 Mb/s and uplink speeds of up to 120 Mb/s; under LTE, maximum downlink speeds reach 200 Mb/s and uplink speeds 100 Mb/s. The maximum transmit power can reach 23±2 dBm across multiple frequency bands, ensuring signal coverage.
The software design begins with system initialization, after which the system collects parameters such as voltage, current, power, frequency, and power factor from the EV charging station, along with environmental information like temperature, humidity, atmospheric pressure, and altitude. By calculating the difference between the device temperature and the ambient temperature, the system obtains the temperature rise data of the EV charging station. The detected values are compared with set thresholds; if a value exceeds the threshold, an alarm is triggered, and the alarm data is sent to the OneNET IoT platform. The monitoring software for the EV charging station involves reading the electrical parameters and environmental parameters of the EV charging station equipment, calculating the temperature rise of the EV charging station, and acquiring voltage, current, power, frequency, and power factor. The default thresholds are adjusted based on environmental parameters: when atmospheric pressure is low, the threshold is reduced by 10%; at high altitudes, the threshold is reduced by 10%; in high humidity, the threshold is reduced by 10%; if all three conditions are met, the overall threshold is reduced by 30%. The detected values are then compared to the adjusted thresholds. When a detected value reaches 70% of the adjusted threshold, a reminder is issued; at 80%, a warning is triggered; at 90%, an alarm is activated; and at 100%, emergency control measures are taken. The detected data and alarm information are transmitted to the OneNET platform via the RedCap communication module.
OneNET is a PaaS IoT open platform developed by China Mobile, assisting developers in quickly connecting devices and achieving data acquisition, storage, and display for IoT devices. Data from the EV charging station is collected by sensors and sent to the control system, which then transmits it to the OneNET platform via the RedCap communication module, using the MQTT protocol for data transmission between the control system and the IoT platform. First, corresponding product information needs to be created on the OneNET platform, selecting the IoT open platform; after successful product creation, a product ID and access-key are automatically generated. Then, devices are added to the device list, and the access-key information and product ID are added to the control system. Once successfully added, the data streams uploaded by the control system are displayed in the device list, showing specific parameter values and upload times, with an option for real-time data refresh.
After system debugging, tests were conducted on the EV charging station, during which the monitoring system demonstrated efficient and accurate real-time monitoring capabilities. Through continuous monitoring of electrical parameters such as voltage, current, power, frequency, and power factor of the EV charging station, combined with perception of environmental parameters like temperature, humidity, and atmospheric pressure, the system effectively identified potential safety risks. When detected data exceeded preset safety ranges, the system promptly initiated corresponding alarm mechanisms, including reminders, warnings, alarms, and even emergency control, effectively ensuring the safe operation of the EV charging station and electric vehicles. The specific test results are summarized in the table below, where “Normal” indicates all parameters are within safe ranges; “Reminder” means some parameters exceed 70% of the threshold; “Warning” indicates parameters exceed 80%; “Alarm” means parameters exceed 90%; and “Emergency Control” is triggered when parameters exceed 100%.
| Voltage (V) | Current (A) | Power (W) | Frequency (Hz) | Power Factor | Temperature Rise (°C) | Humidity (%RH) | Atmospheric Pressure (hPa) | Result |
|---|---|---|---|---|---|---|---|---|
| 220 | 10 | 2200 | 50 | 0.95 | 25 | 50 | 1013 | Normal |
| 225 | 12 | 2700 | 52 | 0.92 | 27 | 55 | 1010 | Reminder |
| 230 | 14 | 3220 | 54 | 0.90 | 30 | 60 | 1007 | Warning |
| 235 | 16 | 3760 | 56 | 0.87 | 33 | 65 | 1004 | Alarm |
| 240 | 18 | 4320 | 58 | 0.84 | 36 | 70 | 1000 | Emergency Control |
In this monitoring system for EV charging stations, the adjustment of safety thresholds based on environmental conditions can be mathematically represented. Let \( T_{\text{default}} \) be the default threshold for a parameter. The adjusted threshold \( T_{\text{adjusted}} \) is calculated as follows based on environmental factors: atmospheric pressure \( P \), altitude \( A \), and humidity \( H \). The reduction factors are defined as: \( \alpha = 0.1 \) if \( P < P_{\text{normal}} \), \( \beta = 0.1 \) if \( A > A_{\text{normal}} \), and \( \gamma = 0.1 \) if \( H > H_{\text{normal}} \), where \( P_{\text{normal}} \), \( A_{\text{normal}} \), and \( H_{\text{normal}} \) are normal reference values. The overall adjustment is given by:
$$ T_{\text{adjusted}} = T_{\text{default}} \times (1 – (\alpha + \beta + \gamma)) $$
If all three conditions are met, the total reduction is 30%, so \( T_{\text{adjusted}} = T_{\text{default}} \times 0.7 \). For instance, if the default threshold for temperature rise is 40°C, and all environmental conditions are adverse, the adjusted threshold becomes 28°C. This dynamic adjustment enhances the safety and adaptability of the EV charging station monitoring system.
The power parameters monitored by the system, such as voltage \( V \), current \( I \), and power \( P \), are related by the formula for AC power:
$$ P = V \times I \times \text{PF} $$
where PF is the power factor. The system continuously computes these values to ensure they remain within safe limits for the EV charging station. Additionally, the temperature rise \( \Delta T \) is calculated as:
$$ \Delta T = T_{\text{device}} – T_{\text{ambient}} $$
where \( T_{\text{device}} \) is the temperature of the EV charging station equipment and \( T_{\text{ambient}} \) is the ambient temperature. This parameter is critical for preventing overheating in the EV charging station.
In conclusion, I have designed a monitoring system for EV charging stations based on microcontroller technology, power acquisition devices, environmental acquisition devices, relays, and RedCap communication modules. By integrating RedCap communication technology with microcontroller control solutions, efficient and intelligent monitoring of EV charging stations is achieved. The application of RedCap technology makes data transmission more stable and reliable while reducing power consumption, making it suitable for large-scale deployment. This monitoring system can efficiently and accurately perform real-time monitoring of EV charging stations, ensuring the safety and reliability of electric vehicle charging facilities and effectively reducing the risk of accidents. Furthermore, the system features dynamic adjustment of safety thresholds based on environmental conditions, further enhancing its flexibility and adaptability. Future work will explore more advanced algorithms and technologies to improve the intelligence level of the monitoring system, such as using machine learning to predict potential failures in EV charging stations. For EV charging stations operating in different regions and climatic conditions, more refined safety standards and threshold adjustment strategies will be developed. Combined with user historical charging data, personalized charging suggestions and services can be provided. These improvements will help promote the development of monitoring technology for EV charging stations, offering safer and more convenient services for the electric vehicle industry.