A hybrid vehicle working condition prediction method based on meta-learning. A model training process is divided into two parts: pre-training executed offline and fine-tuning training executed online; the pre-training relates to implementing parallel training for various working conditions to obtain a base model having good generalization performance; the fine-tuning training combines multi-task training on the basis of a deep neural network, and relates to training for a specific working condition on the basis of the base model. The time cost is low, and the method can be applied to a model online correction link. In addition, on the basis of said process, further provided is a vehicle speed prediction model online application framework composed of three parts, i.e., offline training, online training, and real-time prediction, which can be applied to a working condition prediction task under actual traffic conditions.
HYBRID VEHICLE WORKING CONDITION PREDICTION METHOD BASED ON META-LEARNING