期刊
JOURNAL OF POWER SOURCES
卷 490, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jpowsour.2021.229523
关键词
Temperature estimation; Cell resistance; Battery management system; State estimation; Lithium-ion battery
资金
- European Union [EVERLASTING-713771]
It is crucial to understand the temperature distribution within a battery pack for its impact on capacity loss, power degradation, and safety. This study introduces a novel sensorless method for determining the temperature of a cell by exploiting the relationship between the cell's overpotential and temperature. Through analysis of reference pulses, it is shown that a Δt in the 10 ms to 100 ms range has the greatest sensitivity to temperature and the least dependence on other parameters.
Knowing the temperature distribution within a battery pack is vital, because of the impact on capacity loss, power degradation and safety. Temperature measurements are usually realized with temperature sensors attached to a limited number of cells throughout the battery pack, leaving the majority of cells in larger battery systems unattended. This work presents a novel sensorless method for determining the temperature of a cell by exploiting the relation of the cell's overpotential and temperature exemplary using a 18650 nickel-rich/silicon-graphite cell, although the method is basically applicable to any cell. Current changes in the battery load are utilized as pulse excitation for the calculation of a direct-current resistance R-DC,R-Delta t determined after a certain time Delta t. Reference pulses at 10/20/30/40 degrees C are recorded to investigate the influence of state-of-charge and pulse rise/fall-time, as well as the pulse-current amplitude and direction on R-DC,R-Delta t. The analysis of the reference pulses shows that a Delta t in the 10 ms to 100 ms regime has the greatest sensitivity to temperature and the least dependence on other parameters. The method is finally validated using a 6s1p-module with an externally constant temperature gradient applied to the serial connection, showing an average estimation error smaller than 1K for each cell.
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