A dynamic electro‐thermal coupled model for temperature prediction of a prismatic battery considering multiple variables

Abstract

Summary A dynamic coupled electro‐thermal model including the impact of the state of charge (SOC), inner temperature and current flux on resistance and heat generation rate is proposed for prismatic batteries, including the ohmic and polarization resistances and entropy coefficient. This model reflects the interaction between generation rate of heat and temperature distribution and is appropriate to be employed. Subsequently, the dynamic model is implemented to reveal the temperature increase of a Li‐ion prismatic battery with the capacity of 50‐Ah under the conditions of static and dynamic currents. Experiments are performed to demonstrate the model which is given in this article can precisely describe the thermal behavior under the conditions of static and dynamic currents and different ambient temperatures, with an average relative error of 5.87% for the static condition and of 11.81% for the dynamic condition. In addition, comparative studies are utilized to decide the application range of the dynamic model. It shows according to the results that it should be used for the temperature prediction under the condition of dynamic current. As for the static current condition, the static model ignoring the effect of current can replace the dynamic model, although the temperature prediction precision of the dynamic model is slightly higher than that of the static model. Finally, the proposed dynamic model is compared with a resistance‐based thermal model where the heat generation depends only on SOC and is demonstrated to be superior to its counterpart.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

“…In addition, polarization internal resistance is greatly affected by SOC, especially in the terminal area of SOC. 28,29 At this time, the model parameters will be disturbed and lead to abnormal jitter, and since the parameter identification method based on FFRLS does not track parameter changes, model identification accuracy is reduced. Thus, FF-AGLS is proposed in this paper in order to attempt to solve the above problems.…”

“…Moreover, ohmic internal resistance and polarization internal resistance are greatly affected by temperature and SOC, among which the ohmic resistance is very significantly affected by temperature interference, especially at low temperatures. In addition, polarization internal resistance is greatly affected by SOC, especially in the terminal area of SOC 28,29 . At this time, the model parameters will be disturbed and lead to abnormal jitter, and since the parameter identification method based on FFRLS does not track parameter changes, model identification accuracy is reduced.…”

“…[25][26][27] The model parameters of the battery significantly change at different temperatures, and the battery parameters sharply fluctuate at low temperatures and low SOC areas. 28,29 As a result, the algorithm's tracking effect on parameters is severely affected, and the accuracy of parameter identification is highly reduced, which also serves as the current bottleneck in the algorithm.…”

“…This is caused by the biased estimation used by FFRLS in reducing the number of calculations, making the algorithm very sensitive to the chattering of data and greatly weakening the robustness of the algorithm 25‐27 . The model parameters of the battery significantly change at different temperatures, and the battery parameters sharply fluctuate at low temperatures and low SOC areas 28,29 . As a result, the algorithm's tracking effect on parameters is severely affected, and the accuracy of parameter identification is highly reduced, which also serves as the current bottleneck in the algorithm.…”

Study on the Influence of Flat Heat Pipe Structural Parameters in Battery Thermal Management System

“…The detailed modeling process and experimental validation of the resistance-based battery model are described in our previous studies (Li et al, 2020).…”