Zhang Zhaowei, Liu Chen, Li Tianyu, Wang Tian, Cui Yaoyao, Zhao Pengcheng
School of Mechanical and Electrical Engineering, Shijiazhuang University, Shijiazhuang, Hebei, 050035, China.
Shijiazhuang Key Laboratory of Agricultural Robotics Intelligent Perception, Shijiazhuang, Hebei, 050035, China.
Sci Rep. 2025 Aug 12;15(1):29572. doi: 10.1038/s41598-025-15597-2.
Accurately estimating the state-of-charge (SOC) of lithium-ion batteries is of great significance for the energy management and range calculation of electric vehicles. With the development of graphics processing units, SOC estimation based on data-driven methods, especially using recurrent neural networks, has received considerable attention in recent years. However, existing data-driven methods often neglect internal resistance, which is highly detrimental to the accuracy of SOC estimation. In addition, commonly used network optimization algorithms do not always maximize the convergence speed and performance simultaneously. To solve these problems, this paper describes a battery test bench for producing an effective lithium-ion battery dataset containing current, voltage, temperature, and more importantly, internal resistance measurements. To improve the estimated SOC performance, the internal resistance is considered in the construction of a data-driven model. Using a convolutional neural network (CNN) and long short-term memory (LSTM), we propose an optimization model that switches from Adam to stochastic gradient descent (SWATS). A well-known public battery dataset and an experimentally measured dataset are used to verify the feasibility of the SWATS scheme. The results show that, compared with existing data-driven methods, the proposed method is effective, especially in terms of robustness and generalization.
准确估计锂离子电池的荷电状态（SOC）对于电动汽车的能量管理和续航里程计算具有重要意义。随着图形处理单元的发展，基于数据驱动方法的SOC估计，尤其是使用递归神经网络的方法，近年来受到了广泛关注。然而，现有的数据驱动方法往往忽略了内阻，这对SOC估计的准确性极为不利。此外，常用的网络优化算法并不总是能同时最大化收敛速度和性能。为了解决这些问题，本文介绍了一种电池测试平台，用于生成包含电流、电压、温度，更重要的是包含内阻测量值的有效锂离子电池数据集。为了提高SOC估计性能，在构建数据驱动模型时考虑了内阻。使用卷积神经网络（CNN）和长短期记忆网络（LSTM），我们提出了一种从Adam切换到随机梯度下降（SWATS）的优化模型。使用一个著名的公共电池数据集和一个实验测量数据集来验证SWATS方案的可行性。结果表明，与现有的数据驱动方法相比，所提出的方法是有效的，尤其是在鲁棒性和泛化能力方面。
