Abnormal situations inside the vehicle battery cannot be predicted and warned in time， which leads to electric vehicle accidents and brings serious threats to drivers and passengers’ life and property safety. Aiming at the above problem， a Contrastive Transformer Encoder Decoder （CT-ED） model was proposed for multivariate time series anomaly detection. Firstly， different views of an instance were constructed through data augmentation， and the local invariant features of the data were captured by contrastive learning. Then， based on Transformer， the data were encoded from two perspectives of time dependence and feature dependence. Finally， the data were reconstructed by the decoder， and the reconstruction error was calculated as the anomaly score to detect anomalies of the machine under the actual operating conditions. Experimental results on SWaT， SMAP， MSL three public datasets and Electric Vehicle power battery （EV） dataset show that compared to the suboptimal model， the F1-scores of the proposed model increase by 6.5%， 1.8%， 0.9%， and 7.1% respectively.The above results prove that CT-ED is suitable for anomaly detection under different operating conditions， and balancing the precision and recall of anomaly detection.