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Internal Short Circuit Detection in Lithium-Ion Batteries Under Shipboard Vibration: A Unified Model-Based and Data-Driven Benchmark with NPU-Accelerated Inference
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DOI：10.3390/batteries12080280.png)
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Abstract
En 中文
Shipboard lithium-ion battery systems experience continuous mechanical vibration, yet model-based and data-driven internal short-circuit (ISC) detectors have not been compared under such conditions. We present, to our knowledge, the first unified vibration-aware ISC benchmark: a model-based Extended Kalman Filter (EKF) Δ SOC rule and three convolutional detectors—ModernTCN, LITE, and NPU-Conv2D—are evaluated on a simulated NCR18650PF module under quiescent, MIL-STD-810H-derived, and head-sea vibration, with vibration coupled to cell resistance through a phenomenological assumption Δ R = k R | a | . The EKF observes the terminal voltage alone, whereas the data-driven detectors additionally observe cell temperature, so the comparison couples detector class with input observability. Under this assumed envelope and the swept coupling range, the dual-channel data-driven configurations pass 135 / 135 deadline-scored outcomes against 129 / 135 for the EKF, with zero pre-onset false alarms versus 15, and their advantage lies in detection-delay dispersion rather than in mean latency. Deployed on an STM32N6 microcontroller, the INT8 NPU-Conv2D completes one inference in 0.752 ms, 179 × lower latency than the EKF firmware. Calibration-free robustness emerges as the practical advantage of the evaluated dual-channel detectors.
Keywords:
lithium-ion battery
internal short-circuit detection
shipboard vibration
extended Kalman filter
time-series classification
embedded AI
neural processing unit
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B
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4.8
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1.8K
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