Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Abstract

Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

“…where t represents the current number of the iteration, and N represents the maximum number of iterations. α is (10…”

“…Measuring the OCV directly is to estimate the SOH through the intrinsic relationship between the OCV curve and capacity [8], while measuring the current directly to estimate the SOH is achieved by integrating the current flowing into or out of the battery [9]. On the other hand, measuring the impedance directly is to estimate the SOH considering the principle that the more serious the battery aging, the greater internal resistance at low frequency [10]. Electrochemical impedance spectroscopy (EIS) is the most commonly used method.…”

Effect of Sample Interval on the Parameter Identification Results of RC Equivalent Circuit Models of Li-ion Battery: An Investigation Based on HPPC Test Data

“…The time constant τ II1 is the product of the resistor R II1 and capacitor C II1 in the first RC branch, and the time constant τ II2 is the product of the resistor R II2 and capacitor C II2 in the second RC branch. Time constant τ II1 represents the electrochemical polarization at the interfaces of electrode material and battery separator, while the time constant R II2 typically refers to the degree of concentration polarization within the LIB [23][24][25]. In order to offer more detailed and accurate information on mass transfer and electrochemical behaviors with the LIB, the 2-RC circuit model uses two independent resistors (RC).…”

The Spatio-Temporal Variation of Isothermal Water Mass in Jiaozhou Bay

“…High-Temperature Water Mass. In April, in the northeastern of Jiaozhou Bay, at station C3 in the coastal waters of the estuary of Loushan River and station C5 in the coastal waters, and at station B1 in the coastal waters of the northwest, that is, from the northeast along the northern coastal waters to the northwest, the water temperature reached [26][27][28][29][30][31][32][33] 12.82-13.70 °C, and a high-temperature zone was formed with the northern coastal waters as the center. According to the definition of Yang Dongfang isothermal water mass, there was a hightemperature water mass in the waters of Jiaozhou Bay (Figure 2), located in the coastal waters from the northeast to the northwest.…”