Methodology to determine the heat capacity of lithium-ion cells

Bryden, Thomas; Dimitrov, Boris; Hilton, George F.; León, Carlos Ponce de; Bugryniec, Peter J.; Brown, Solomon; Cumming, Denis J.; Cruden, Andrew

doi:10.1016/j.jpowsour.2018.05.084

Cited by 68 publications

(27 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In Equation 2, the adiabatic temperature (∆T ) is the difference between the onset and maximum cell temperature, C p is the average specific heat of the cell and m is the average mass of the cell. The mass of the cell was measured to be 40.48 g. The C p value, following the method outlined by Bryden et al (2018), was calculated by Equation 3 from the average temperate rate (dT /dt) of a cell in an adiabatic environment subject to a constant heating power (P ) at the cells surface. The specific heat of the cell was determined to be 1107 J kg −1 K −1 .…”

Section: Comparison Of Heat Release Under Different Abuse Scenariosmentioning

confidence: 99%

Pursuing safer batteries: Thermal abuse of LiFePO4 cells

Bugryniec

Davidson

Cumming

et al. 2019

Journal of Power Sources

View full text Add to dashboard Cite

Section: Comparison Of Heat Release Under Different Abuse Scenariosmentioning

confidence: 99%

Pursuing safer batteries: Thermal abuse of LiFePO4 cells

Bugryniec

Davidson

Cumming

et al. 2019

Journal of Power Sources

View full text Add to dashboard Cite

“…Further, the short transient response is caused by the lithium-ion flow in the solid electrolyte interphase layer, and the anode electrode is represented by polarization resistance (R 1 ) and capacitance (C 1 ), respectively. These R 1 and C 1 appear only during the transient period [53]. A 1-RC battery model was considered in this study due to its optimum performance, ease of modelling, low computational cost and adequate accuracy when compared to other higher-order RC models [54,55].…”

Section: Heat Generation Modelmentioning

confidence: 99%

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

et al. 2022

View full text Add to dashboard Cite

Estimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. It is impractical to measure the core temperature of each cell using physical sensors, while at the same time implementing a complex core temperature estimation strategy in onboard low-cost BMS is also challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models with different levels of accuracy and complexity. Broadly, there are first-order and second-order heat resistor–capacitor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study. The aim was to determine whether it is worth investing towards developing a second-order thermal model instead of a first-order model with respect to prediction accuracy considering the modeling complexity and experiments required. Both the thermal models along with the parameter estimation scheme were modeled and simulated in a MATLAB/Simulink environment. Models were validated using laboratory test data of a cylindrical 18,650 LIB cell. Further, a Kalman filter with appropriate process and measurement noise levels was used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models were analyzed for comparison purposes.

show abstract

“…It is fundamental to have a well‐proportioned training dataset to make the prediction model have high learning ability and generalization ability. The capacity can be reflected by the dSOC/dV , 64 dQ/dV , 65 and other working performance, including SOC, voltage (V), current (I), and temperature (Tem). Thus, the variable sets for training can be defined by these factors as ( V , I , SOC , Tem , dSOC/dV , dQ/dV ), while the depending result is capacity.…”

Section: Description Of the Soc And Soh Estimation Approachmentioning

confidence: 99%

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

et al. 2021

View full text Add to dashboard Cite

Summary An accurate estimate of the battery state of charge and state of health is critical to ensure the lithium‐ion battery's efficiency and safety. The equivalent circuit model‐based methods and data‐driven models show the potential for robust estimation. However, the state of charge and state of health estimation system's performance with a parallel comparison has been rarely investigated. In this study, the performances of state of charge and state of health with equivalent circuit model‐based methods and data‐driven estimations are analyzed by different aged and capacity batteries through methods including extended Kalman filters, fully connected deep network with drop methods, and the combination (extended Kalman filters—fully connected deep network with drop methods). Besides the battery state of the voltage and current, the relationship between inner resistance, temperature, and capacity are also considered. Finally, a suggested method is promising for online state estimation of battery working at different temperatures and initial working state. The results indicate that the maximum state of charge estimation errors of the fully connected deep network with drop methods is 0.56% for the fully charged battery. Simultaneously, with an uncertain initial state of charge, the extended Kalman filter shows the lowest maximum state of charge estimation errors (1.4%). For the state of health estimation, the optimized method uses extended Kalman filters to do the monitor first; after 5 testing points, if the state of health drops to lower than 0.95, extended Kalman filters—fully connected deep network with drop methods is suggested. And finally, estimation errors for this method decreased from 30% to 2%.

show abstract

Methodology to determine the heat capacity of lithium-ion cells

Cited by 68 publications

References 25 publications

Pursuing safer batteries: Thermal abuse of LiFePO4 cells

Pursuing safer batteries: Thermal abuse of LiFePO4 cells

Smart Core and Surface Temperature Estimation Techniques for Health-Conscious Lithium-Ion Battery Management Systems: A Model-to-Model Comparison

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

Contact Info

Product

Resources

About