Nishanth G, Krishnan M Murali, Parandhaman Balamurugan, Harinarayanan J
School of Electrical Engineering, Vellore Institute of Technology, Chennai, India.
Sci Rep. 2025 May 3;15(1):15551. doi: 10.1038/s41598-025-99931-8.
In the rechargeable batteries lithium-ion batteries are now being utilized extensively in a variety of industries, including electric vehicles, drones, and portable electronics. When it comes to such batteries, it is extremely challenging to accurately monitor the state of charge (SOC). In this instance, the EKF method has been implemented with software and hardware demonstration, and the measured value and estimated value have both achieved an error that is within 2% of each other. As a result of executing the static capacity test under dV/dt with the discharging current set to constant and hybrid pulse power characterization test, the figures that are becoming available are being acquired. The approach of optimization assigns a SOC of 90% and 10% for two reference points in the V equation. Additionally, the technique updates the electrical model of the cell by using the derivative of the terminal voltage that was recorded for the cell. With the use of the covariance matrices in the Extended Kalman Filtering equations, the SOC of the battery may be reliably predicted with a level of accuracy that exceeds 98% when compared to conventional techniques of estimation such as coulomb counting. As part of this research, an adaptive model approach for evaluating the state of charge of Li-ion batteries that are becoming older is being developed.
在可充电电池中，锂离子电池目前正在各种行业中广泛应用，包括电动汽车、无人机和便携式电子产品。对于这类电池，准确监测荷电状态（SOC）极具挑战性。在这种情况下，已通过软件和硬件演示实现了扩展卡尔曼滤波（EKF）方法，测量值和估计值的误差均在彼此的2%以内。通过在放电电流设置为恒定的情况下执行dV/dt下的静态容量测试以及混合脉冲功率特性测试，正在获取现有的数据。优化方法在V方程中为两个参考点分配了90%和10%的SOC。此外，该技术通过使用记录的电池端电压导数来更新电池的电气模型。通过扩展卡尔曼滤波方程中的协方差矩阵，与诸如库仑计数等传统估计技术相比，电池的SOC可以以超过98%的准确度可靠地预测。作为这项研究的一部分，正在开发一种用于评估老化锂离子电池荷电状态的自适应模型方法。
