Objective The state of health is a key parameter to evaluate the state of lithium-ion batteries and it is of great significance to the safe use of lithium-ion batteries. To obtain accurate and reliable health state estimation results a health state estimation method for lithium-ion batteries based on convolutional neural networks and Transformer was developed. Using the data mining characteristics of different models the deep information of health indicators and the time series information that increases with the cycle period were extracted in parallel. Methods Three health indicators with a strong correlation with health status were extracted from the partial voltage and temperature curves of the lithium-ion battery during the discharge process as model inputs. The powerful feature extraction capability of the convolutional neural network was used to mine the local features of health indicators and the sequential processing capability of the Transformer was used to mine the time series features of health indicators. The local and time-series features of health indicators were fused with features and the health status estimates were output by convolution and global average pooling layers. Results In this study experimental validation was performed using the MIT dataset a comparative analysis was performed with convolutional neural network and long and short-term memory neural network and the root mean square error and mean absolute error of the proposed method were the lowest which were 0. 11 and 0. 08 and the minimum relative error was 0. 61%. Conclusion The experimental results show that the proposed CNN-Transformer health state estimation uses different models to mine different feature information of health indicators which can make full use of lithium-ion battery discharge data and has a good estimation effect.