State of charge estimation of lithium-ion battery based on state of temperature estimation using weight clustered-convolutional neural network-long short-term memory

State of charge (SOC) plays a vital role in the safe, efficient, and stable operation of lithium-ion batteries. Since the difference between the surface temperature and core temperature of batteries under severe conditions can reach 5–10 ​°C, using the surface temperature as input feature of SOC estimation is unreasonable. Due to the high requirement for storage space, SOC estimation methods based on deep learning methods are limited to implement in embedded devices. In this paper, to achieve reasonable and high accuracy SOC estimation and provide support for battery thermal management, SOC estimation based on state of temperature (SOT) is implemented. And weight clustered-convolutional neural network-long short-term memory (WC-CNN-LSTM) is proposed to achieve high accuracy SOT and SOC estimation with small model sizes. A self-established dataset is used to verify the effectiveness of the proposed method and model. The WC-CNN-LSTM model with the number of clusters of 400 could achieve comparative accuracy with the baseline model with a 52.98% smaller model size and 25.08% more time consumption for model training on SOT estimation. And it could also achieve consistent and even better accuracy on SOC estimation with the baseline model with a small model size. The framework of the proposed SOT-based SOC estimation. • Weight clustered-convolutional neural network-long short-term memory is proposed. • Pearson correlation analysis is adopted for input feature selection. • Average step size is optimized to achieve better SOT performance. • SOC estimation is implemented based on the estimated core temperature. • A self-established dataset is employed for methods verification.