Lithium battery state of health and remaining useful life joint prediction method based on adversarial learning

Abstract

The application discloses a lithium battery health state and residual service life joint prediction method based on adversarial learning, and belongs to the lithium battery life prediction field. The application firstly performs simple preprocessing on original aging data, then utilizes a shared feature extractor and a specific feature extractor composed of a convolution network module and a residual network module to extract task-shared features and task-specific features of the health state and the residual service life based on adversarial learning, and improves feature discrimination; after the task-shared features and the task-specific features of the health state and the residual service life are fused, the fused features are input into a health state predictor and a residual service life predictor, model negative optimization caused by feature confusion is avoided, and finally, health state and residual service life prediction results are obtained. The application carries out the lithium battery health state and residual service life joint prediction based on adversarial learning, the joint prediction model has low requirements for input, the prediction error is low, and the aging state and the life state of the lithium battery in an actual use scenario can be effectively monitored, and the safety of equipment is improved.

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