由兩個相等方波生成的低頻阻抗譜作為不需電池弛豫的狀態估計簡單工具

Abstract

電化學阻抗譜（EIS）是一項實驗技術，能提供關於電池內部阻抗的豐富信息，其低頻部分與電池狀態有高度相關性。傳統的EIS (T-EIS) 因其所需的高昂設備成本和長時間的測量過程，並不適合快速監控。為了克服這些限制，我們的研究證明了低頻範圍內的方波EIS（Sq-EIS）是一個成本效益更高且更高效的選擇，它能夠在電池處於非弛豫態時也生成與T-EIS相媲美的低頻阻抗譜，並具有低於0.5 mΩ的均方根誤差（RMSE）。我們對50秒周期方波的數量、幅度和採樣率進行了徹底的研究，顯示出具有1A幅度和超過50 Hz採樣率的兩週期方波能在不同情境下，包括恆流充電/放電和動態放電情況下，實現Sq-EIS和T-EIS之間的最佳相似性。30秒和10秒等不同周期的方波也能有效達到這種相似性。基於這些發現，通過在電池充電或動態放電後立即應用兩個10秒周期的相等方波，Sq-EIS數據能使機器學習模型在估算電池的充電狀態和健康狀態時，RMSE在每種情況下均小於2%。

Electrochemical impedance spectroscopy (EIS) is an experimental technique that reveals battery impedances, notably with its low-frequency components exhibiting significant correlations with battery states. However, traditional EIS (T-EIS) necessitates expensive instrumentation and extended battery relaxation periods, rendering it impractical for rapid state estimation applications. To overcome these two shortcomings, square wave EIS (Sq-EIS) in the low-frequency range, generated using a simple two-cycle square wave, emerges as a more cost-effective and time-efficient alternative, capable of achieving results comparable to low-frequency T-EIS. Even when the battery is at unrelaxed states, the total root mean square error (RMSE) between Sq-EIS and T-EIS can be less than 0.5 mΩ. We conduct thorough investigations into the number, amplitude, and sampling rate of 50-s period square waves, showing that a two-cycle square wave with an amplitude of 1 A and a sampling rate above 50 Hz can achieve optimal similarity between Sq-EIS and T-EIS across different scenarios, including constant current charging/discharging and dynamic discharging. Square waves of different periods, such as 30 s and 10 s, also effectively achieve this similarity. Based on these findings, by applying two equal 10-s period square waves right after battery charging or dynamic discharging, the Sq-EIS data enables machine learning models to estimate the battery's state of charge and state of health with an RMSE of less than 2%.