Instantaneous estimation of internal temperature in lithium‐ion battery by impedance measurement

Abstract

Summary Due to the various drawbacks of collecting temperature using embedded or patch thermocouple sensor, the internal temperature estimation is getting more and more attention in the field of lithium power battery. In this paper, the commercial 18650 LiFePO4 battery is selected to analyze the characteristic of Electrochemical Impedance Spectroscopy (EIS) from 0°C to 55°C of 0.1 to 10 000 Hz. The results reveal that there exists intrinsic relationship between the alternating current (AC) impedance phase shift and the internal temperature in the range of 10 to 100 Hz from 5 to 55°C. And the intrinsic relationship is not interfered with the State‐of‐Charge (SOC) and the State‐of‐Health (SOH). Subsequently, the relationship is described with a modified Arrhenius equation under the excitation frequency of 12, 44, and 79 Hz. Finally, a novel internal temperature estimation method is proposed by the AC impedance phase shift. The applicability and accuracy of the method are further verified via 10 temperature points. The results indicate that the estimation error is within 1°C in the common operating temperature range (15‐45°C), suggesting that the proposed method can be applied to estimate battery internal temperature. Finally, the implementation system of real‐time estimation for engineering application is constructed.

Determination of Internal Temperature Differences for Various Cylindrical Lithium-Ion Batteries Using a Pulse Resistance Approach

“…In our previous works [27,28], we used a general exponential equation, such as [21,22], to describe the relationship between resistance and temperature. However, in the literature, the relation between resistance for different cell internal processes and temperature is frequently described with a (modified) Arrhenius relation [8,[10][11][12]14,[18][19][20]23]. Since the Arrhenius relation is a more commonly used and physics-based approach, the goal of this chapter is to deduce and parameterize an Arrhenius-based function describing the relation between the R DC values, derived from the characterization pulses, and the cell temperature in thermal equilibrium.…”

“…As assumed, Equation (4) can reflect the temperature behavior of the R DC values, despite the fact that the resistance values represent the superposition of several processes. Similar to [19,22], who used the R-Square (R 2 ) as a criterion for assessing the fitting quality for their temperature estimation method, the adjusted R-Square (R 2 adj ) [45] was used for a quantitative assessment of the fitting results in this work. The R 2 and R 2 adj are statistical measures for the goodness of a curve fitting result, whereby values closer to one indicate a better fit quality.…”

“…These methods usually utilize a certain impedance feature of the electrochemical impedance spectroscopy (EIS) to determine the LIB's temperature, whereby the nature of the determined temperature varies from method to method. Table 1 summarizes the statements on the determined temperature from existing methods in the literature, which range from electrode-specific temperatures [8,9], internal/core temperature [2,5,[10][11][12][13][14][15][16][17][18][19] to average/integral [20][21][22], and internal temperature distribution [6,[23][24][25]. The cells examined in the studies of Table 1 cover a wide variety of chemistries and casing types, including various cylindrical, prismatic, and pouch cells with capacities reaching from 2.3 Ah to 90 Ah.…”

Data‐Driven–Based Internal Temperature Estimation for Lithium‐Ion Battery Under Variant State‐of‐Charge via Electrochemical Impedance Spectroscopy

“…The key for temperature estimation of LIBs is to correctly map the relations between the selected features and the LIBs’ internal temperature. Overall, model‐based methodologies like the Arrhenius model [ 10,13 ] and empirical formula model, [ 14,15 ] as well as data‐driven based techniques like neural networks (NNs) [ 16–18 ] and support vector regression (SVR) [ 19 ] are alternatives that can be selected according to data properties. In detail, Spinner et al [ 10 ] proposed an improved Arrhenius formula to describe the relationship between the internal temperature and the imaginary part of the impedance.…”

Impedance Based Temperature Estimation of Lithium Ion Cells Using Artificial Neural Networks

“…The impedance based methods have gained substantial interest because of their characteristic of measuring the average internal temperature without using internal or external hardware [9]. Therefore, the method is also known as sensorless temperature measurement.…”