School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea.
Sensors (Basel). 2022 Jul 25;22(15):5536. doi: 10.3390/s22155536.
Lithium batteries are secondary batteries used as power sources in various applications, such as electric vehicles, portable devices, and energy storage devices. However, because explosions frequently occur during their operation, improving battery safety by developing battery management systems with excellent reliability and efficiency has become a recent research focus. The performance of the battery management system varies depending on the estimated accuracy of the state of charge (SOC) and state of health (SOH). Therefore, we propose a SOH and SOC estimation method for lithium-ion batteries in this study. The proposed method includes four neural network models-one is used to estimate the SOH, and the other three are configured as normal, caution, and fault neural network model banks for estimating the SOC. The experimental results demonstrate that the proposed method using the long short-term memory model outperforms its counterparts.
锂电池是二次电池，用于各种应用，如电动汽车、便携式设备和储能设备作为电源。然而，由于在其运行过程中经常发生爆炸，因此通过开发具有出色可靠性和效率的电池管理系统来提高电池安全性已成为近期的研究重点。电池管理系统的性能取决于对充电状态（SOC）和健康状态（SOH）的估计精度。因此，在本研究中，我们提出了一种锂离子电池的 SOH 和 SOC 估计方法。该方法包括四个神经网络模型，一个用于估计 SOH，另外三个被配置为正常、警告和故障神经网络模型库，用于估计 SOC。实验结果表明，使用长短期记忆模型的建议方法优于其对应方法。
