Multi-condition Temperature Detection of UAV Batteries Based on Least Squares Fitting Algorithm

Conventional multi-operating condition temperature detection methods for UAV batteries are mainly based on temperature characterization, which do not take into account the relationship between temperature and operating conditions, thus affecting the accuracy of temperature detection. Therefore, a multi-condition temperature detection method for UAV batteries based on the least squares fitting algorithm is designed. Multi-point temperature sensors are arranged on the surface and inside of the battery to collect the temperature distribution data on the surface and inside of the UAV battery to ensure the accuracy of the subsequent temperature detection. Based on the least squares fitting algorithm, a battery condition-temperature detection model is constructed, and the temperature change pattern of the battery under each condition is analyzed to obtain the optimal detection results. The temperature detection error of UAV battery under multiple conditions is step-corrected, and the relationship between the temperature transient and the pulse function under any condition is obtained according to the Duhamel integral, so as to avoid the problem of temperature detection error. Comparison experiments are used to verify that the method is more accurate in temperature detection and can be applied in real life.