School of Rail Transportation, Shandong Jiao Tong University, Jinan, China.
School of Electrical Engineering, Shandong University, Jinan, China.
PLoS One. 2024 Jul 10;19(7):e0306165. doi: 10.1371/journal.pone.0306165. eCollection 2024.
State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.
电池的能量状态（SOE）是确保锂离子电池（LIB）系统安全可靠的重要参数。LIB 的安全性、人工智能的发展和计算能力的提高为大数据计算提供了可能性。本文研究了 LIB 的 SOE 估计问题，旨在提高估计的准确性和适应性。首先，在 SOE 估计过程中，通过迭代更新自适应容积卡尔曼滤波（ACKF）的观测噪声方程和过程噪声方程，对其进行自适应修正，增强自适应能力。同时，采用高阶等效模型进一步提高 SOE 估计的准确性和适应性。其次，引入长短期记忆（LSTM）对欧姆内阻（OIR）和实际能量（AE）进行优化，进一步提高 SOE 估计的准确性。再次，在 OIR 和 AE 估计过程中，也采用了 ACKF 的观测噪声方程和过程噪声方程的迭代更新来进行自适应修正，增强自适应能力。最后，本文建立了基于 LSTM 优化 ACKF 的 SOE 估计方法。通过仿真实验验证了 LSTM 优化 ACKF 方法，并与单独的 ACKF 方法进行了比较。结果表明，基于 LSTM 优化的 ACKF 估计方法对 LIB 的 SOE 估计误差小于 0.90%，无论 SOE 为 100%、65%还是 30%，其准确性都优于单独的 ACKF 方法。可以看出，本研究提高了 LIB 的 SOE 估计的准确性和适应性，为确保锂电池的安全性和可靠性提供了更准确的数据支持。
