Abstract
Background: Lithium-ion batteries are widely used in new energy vehicles and energy storage
systems due to their superior performance. However, lithium batteries are prone to safety problems
in the use process, so the fault diagnosis technology of lithium batteries has attracted more attention.
Objective: This study aimed to ensure the safety of lithium batteries and accurately and timely diagnose
the soft short circuit (soft SC) fault of lithium battery
Methods: Aiming at the energy storage lithium battery pack, this study proposed a soft short-circuit
fault diagnosis method for the lithium-ion battery pack based on the improved Extended Kalman Filter
(EKF) algorithm. First, the 1st-order RC equivalent circuit model of normal battery and soft SC fault
battery was established, and model parameters were identified using Recursive Least Squares with Forgetting
Factor (FFRLS). Then, using the improved EKF, the state of charge (SOC) of a single cell was
estimated, and the difference between the calculated SOC and the estimated SOC by the coulomb counting
method was used to detect soft SC faults and compared them with the reference data. Finally, the SC
resistance value indicated the severity of the fault.
Results: The proposed method could accurately diagnose the soft short circuit fault, and the error was
found to be lower than the traditional EKF algorithm. The estimation error was about 0.4% for the battery
with slight failure and about 1.5% for the battery with serious failure.
Conclusion: The experimental results showed that the improved EKF algorithm could estimate the SOC
difference more accurately, and the effect of soft SC fault diagnosis was better. At the same time, it
could quantitatively identify the size of the short circuit resistance, which is very helpful for the subsequent
management of the battery system.
Keywords:
Lithium-ion battery, soft short circuit (soft SC), state of charge, fault diagnosis, EKF algorithm, resistance.
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