Bharathraj Sagar, Lee Myeongjae, Adiga Shashishekar P, Mayya K Subramanya, Kim Jin-Ho
Next Gen Projects, SAIT-India, Samsung Semiconductor India Research SSIR- Bangalore, Bangalore, India.
Battery Material TU, SAIT, Samsung Electronics, Suwon, Republic of Korea.
iScience. 2023 Apr 10;26(5):106636. doi: 10.1016/j.isci.2023.106636. eCollection 2023 May 19.
Li-ion battery mishaps are primarily attributed to short circuits, which missed early detection. In this study, a method is introduced to address this issue by analyzing the voltage relaxation, after initiating a rest period. The voltage equilibration arising from solid-concentration profile relaxation is expressed by a double-exponential model, whose time constants, τ & τ, capture the initial, rapid exponential contour and the long-term relaxation, respectively. By tracking τ, which is very sensitive to small leakage currents, it is possible to detect a short early on and estimate the short resistance. This method, validated with experiments on commercial batteries induced with short circuits of varying extents, has >90% prediction accuracy and enables clear differentiation between different short severities, while factoring in the influence of temperature, state of charge (SOC), state of health (SOH), and idle currents. The method is applicable across different battery chemistries and form factors, offering precise and robust nascent-stage short detection-estimation for on-device implementation.
锂离子电池事故主要归因于短路，而短路未能被早期检测到。在本研究中，引入了一种方法来解决这个问题，即通过在开始静置期后分析电压弛豫。由固体浓度分布弛豫引起的电压平衡由双指数模型表示，其时间常数τ和τ分别捕捉初始的快速指数轮廓和长期弛豫。通过跟踪对小泄漏电流非常敏感的τ，可以早期检测到短路并估计短路电阻。该方法通过对不同程度短路的商用电池进行实验验证，预测准确率>90%，能够在考虑温度、荷电状态（SOC）、健康状态（SOH）和空闲电流影响的情况下，清晰区分不同的短路严重程度。该方法适用于不同的电池化学组成和外形尺寸，为设备上的实施提供精确且可靠的新生阶段短路检测-估计。
