Research on intelligent detection method of new energy vehicle power battery based on improved ViBe algorithm

Background: Traditional foreground detection methods for new energy vehicles using the ViBe algorithm often suffer from ghosting effects, which can obscure the accurate detection of moving targets. Aims: This study enhances foreground detection accuracy by addressing ghosting issues in the ViBe algorithm and improving the battery pack state detection system for new energy vehicles. Method: The method includes analyzing global light changes before foreground detection and updating the background model using the three-frame difference method. The system integrates hardware and software to process data with the ViBe algorithm, measuring voltage from twelve 18650-type lithium batteries. Results: The battery management system prototype exhibits an absolute measurement error within -1.2 mV compared to the high-precision multimeter. The system maintains measurement accuracy across varying temperatures, demonstrating effective environmental adaptability. Conclusion: The enhanced system successfully reduces ghosting in foreground detection and provides reliable battery state monitoring. It is robust under extreme conditions, contributing to improved diagnostic capabilities and enhanced traffic safety.