A Deep Learning-Enhanced EIS Online Monitoring Framework Based on Sparse DC-DC Measurements for Vehicular Fuel Cell Systems

Le résumé fourni par la source

Fast online access to fuel cell impedance is important for health diagnosis and fine-grained management, yet existing onboard methods still struggle to balance update rate and measurement fidelity. This paper presents a hybrid monitoring framework that combines sparse physical calibration from a production DC-DC converter with continuous virtual estimation. A six-phase interleaved DC-DC converter injects dual-frequency excitation at 10 Hz and 1 kHz and, with synchronous Fast Fourier Transform (FFT) extraction under integer-period sampling, provides physically grounded impedance references while keeping current ripple below 1%. Between calibration instants, a compact recurrent estimator, FR-LSTM-lite, infers impedance continuously from electrochemistry-guided inputs. Structured pruning and 8-bit quantization reduce the model to a form suitable for automotive microcontrollers. Hardware-in-the-Loop (HIL) and 140-kW bench tests show inference latency below 1 ms and impedance error no greater than 1.5%, delivering milliohm-level monitoring without extra hardware or sustained excitation. Together, these capabilities make milliohm-resolution impedance available in real time on production hardware, opening a practical path toward impedance-feedback fuel cell control.

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