Artificial Intelligence
Research on Intelligent Power Conditioning Method of Charger Based on Thermal Path Model
Fast charging technology plays a crucial role in advancing the development of electric vehicles. However, the operating environment of an electric vehicle is complex, and the internal temperature can become extremely high under direct sunlight. If the vehicle charger operates at a high power level for extended periods, the internal power components may experience significant heat generation, potentially leading to failures. Therefore, it is essential for the charger to implement temperature control mechanisms in high-temperature environments to reduce thermal stress on the power devices and enhance the overall safety and reliability of the system.
To address this challenge, researchers worldwide have started exploring intelligent power control technologies [1]. These methods typically use a temperature closed-loop control strategy to monitor the real-time temperature of the power components and adjust the input power accordingly, thereby improving the reliability of the device. However, when the vehicle is driven on uneven roads, it becomes difficult to directly measure the temperature rise of the power components, making intelligent power control challenging. To overcome this, references [2–3] introduced a transient measurement technique. This approach builds a thermal path model by measuring the transient temperature rise of the power components. While the model focuses on the dynamic temperature response, it does not account for the relationship between the operating current and ambient temperature, which limits its effectiveness in thermal protection.
In response to these challenges, this paper proposes an intelligent power adjustment method based on a centralized parameter thermal path model of the power device. Unlike traditional approaches that require direct temperature measurements, this method allows for thermal protection based solely on the ambient temperature and input power. This makes it particularly suitable for real-time applications where direct temperature monitoring is impractical.
The core principle of the proposed method involves identifying the worst-case power component in terms of temperature rise and establishing a thermal path model offline. The model enables the implementation of a closed-loop control strategy that adjusts the input power based on the temperature limit of the device, ensuring safe operation even under varying environmental conditions.
The thermal path model simplifies the power device and heat sink as a single thermal unit, assuming that the thermal resistance of the heat sink dominates over that of the power device. Using thermoelectric analogy principles, the model effectively predicts the steady-state temperature of the power device based on ambient temperature, input power, and efficiency. Parameters of the model are estimated using a least-squares method, allowing for accurate predictions of temperature behavior.
The intelligent power adjustment strategy integrates both an outer loop for power regulation and an inner loop for current control. In high-temperature environments, the system reduces the output power to prevent overheating, while in cooler conditions, it optimizes performance without exceeding safe temperature thresholds. A PID controller is used to manage the error between the target and actual temperatures, ensuring stable and efficient operation.
Experimental results validate the effectiveness of the proposed method. A dedicated testing platform was designed to simulate various ambient temperatures and input powers. The MOSFET was identified as the most vulnerable component due to its highest temperature rise. The thermal model was validated through multiple test scenarios, showing a relative error of less than 2%, confirming its accuracy.
When applied to a 1000 W charger, the intelligent power adjustment method successfully limited the MOSFET temperature to within the safe operating range, even as ambient temperatures increased. The output power was reduced to 930 W, preventing overheating and enhancing the reliability of the charging system.
In conclusion, this paper presents an effective solution for managing thermal risks in electric vehicle chargers. By leveraging a centralized thermal path model and intelligent power control, the method ensures safe and reliable operation under diverse environmental conditions, offering a promising approach for future fast-charging systems.
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