To address the challenges of high complexity in multi-source heterogeneous time-series data， scarcity of anomaly samples， and strong inter-variable dependencies in lithium-ion battery fault detection for electric vehicles， a prediction-evaluation framework based on Dynamic Transformer Memory autoencoder for Anomaly Detection （DTMAD） was proposed to enhance fault identification accuracy and model generalization capability. Firstly， a joint feature encoder was designed by integrating Dynamical autoencoder for Anomaly Detection （DyAD） with Gated Recurrent Unit （GRU） to perform feature fusion and dimensionality reduction on multi-source time-series data， extracting deep cross-modal representations. Simultaneously， a pre-response encoder based on a self-attention mechanism was constructed to capture long-term dependencies in time-series data， enhancing the efficiency and accuracy of feature extraction. Furthermore， a memory parsing module was introduced， which fused predicted path with the actual response path via residual contrastive learning， improving the model's capability to detect anomalous patterns. Secondly， based on the distribution characteristics of reconstruction error， an evaluation model was designed through a collaborative anomaly detection algorithm. Finally， through the comprehensive prediction-evaluation framework， key response patterns were extracted from complex multi-source data， and latent anomalies were identified under unsupervised learning conditions. Experimental results on multi-group and multi-source electric-vehicle lithium-ion battery datasets demonstrate that the proposed framework significantly outperforms baseline methods such as AutoEncoder （AE）， Deep Support Vector Data Description （DeepSVDD）， and Graph Deviation Network （GDN） in terms of detection accuracy and model robustness. Notably， compared to the DyAD model， the DTMAD model achieves an Area Under the Receiver Operating Characteristic curve （AUROC） of 0.900 8， with result variability reduced from 0.029 to 0.026， indicating superior detection stability and generalization capability.