The accuracy of cell voltage detection, achieved with a margin of ±10 mV, is confirmed by the test results. In this paper, we aim to enhance the reliability and robustness of the BMIC by implementing fault detection mechanisms within its circuits and incorporating fault recovery functionalities through digital circuits.
The detection method of battery parameters in battery management system is simple and the accuracy is limited [, , ], but the accuracy of parameters is the direct factor affecting the fault diagnosis results. Wang et al. proposed a model-based insulation fault diagnosis method based on signal injection topology.
The current and voltage of a logistics vehicle during the practical operation. In this study, the battery terminal voltages collected by three salve systems are used to test the proposed fault detection method. The voltage data of each salve system is flagged for fault location based on the data transmission layout.
To detect battery system faults accurately, an effective evaluation strategy is proposed by a trial-and-error process.
As well known, setting an appropriate threshold is critical parameters for battery fault detection. Reviewing amounts of relevant literature, the means of battery fault diagnosis are primarily depend on external characteristic parameters including voltage, temperature and SOC.
Based on the entropy and modified Shannon entropy, a voltage fault diagnosis method is proposed to detect battery system faults [24, 25]. Although these methods can be used to diagnose battery system faults and analyze fault levels, they cannot capture the accident timely and thus some fault information may be missed.