# State-of-Charge Estimation of Lithium-Ion Batteries Based on the CNN-Bi-LSTM-AM Model Under Low-Temperature Environments

## TL;DR

This paper introduces a new deep learning model for accurately estimating battery charge levels in cold environments, where traditional methods struggle.

## Contribution

A hybrid CNN-Bi-LSTM-AM model is proposed for improved low-temperature SOC estimation in lithium-ion batteries.

## Key findings

- The CNN-Bi-LSTM-AM model achieved MAE of 0.17–0.77% and RMSE of 0.33–0.94% under low temperatures.
- The model outperformed CNN-LSTM and CNN-Bi-LSTM benchmarks in handling voltage distortion and nonlinearities.
- Results show the model's reliability for battery management in extreme conditions.

## Abstract

Accurate state-of-charge (SOC) estimation is essential for lithium-ion battery management, especially under low temperatures where traditional methods suffer from noise sensitivity and nonlinear dynamics. In this paper, a hybrid deep learning model integrating a one-dimensional convolutional neural network (1D-CNN), bidirectional long short-term memory (Bi-LSTM), and an attention mechanism (AM) is introduced to enhance SOC estimation accuracy. The 1D-CNN extracts local features from voltage and current sequences, while Bi-LSTM captures bidirectional temporal dependencies, and the AM dynamically emphasizes critical time steps. Experiments conducted on the Panasonic 18650PF dataset at temperatures ranging from −20 to 0 degrees Celsius show that the proposed CNN-Bi-LSTM-AM model achieves a mean absolute error (MAE) of 0.17–0.77% and a root mean square error (RMSE) of 0.33–0.94% under US06 and UDDS driving cycles, outperforming CNN-LSTM and CNN-Bi-LSTM benchmarks. The results demonstrate that the model effectively handles voltage distortion and nonlinearities in low-temperature environments, offering a reliable solution for battery management systems operating under extreme conditions.