This paper proposes a lithium battery State of Health (SOH) prediction method that combines a diffusion model and a Transformer to address the issue of low accuracy in predicting the State of Health (SOH) of lithium batteries under complex operating conditions. By integrating the Nonlinear Auto Regression With Exogenous Input (NARX) mechanism with a structured state space model

a dynamic model consisting of convolutional neural network

Mamba model

Transformer decoder

and conditional spread model is constructed to improve the prediction accuracy and generalization performance of lithium battery State of Health (SOH). Firstly

stack the multi cycle lithium battery data into a three-dimensional tensor and standardize it. Use a dual path feature input module to extract multidimensional temporal features and historical SOH correlation information separately. The main path captures the long-range dependencies of capacity decay through convolutional layers and Mamba state space models

while the auxiliary path uses historical SOH values to enhance temporal features. Then

the Transformer decoder will read the dynamic behavior of the battery based on the NARX method

predict the SOH of the battery at cycle t

and complete the NARX feedback loop to enable the model to merge its past predictions. A conditional diffusion model was designed at the output of the model

which guides the diffusion process through conditional features

effectively suppressing the accumulation of prediction errors and improving robustness to noise. The experimental results show that the proposed model can achieve accurate prediction of battery State of Health (SOH) and effectively integrate cross cycle degradation features

achieving high-precision prediction of lithium battery SOH under complex operating conditions.