Coupled Quantum Reservoir Features for Multi-Chemistry Battery State-of-Health Estimation

Résumé fourni par la source

Accurate battery state-of-health (SOH) estimation is essential for electric vehicles and energy-storage systems. We propose Coupled Quantum Reservoir Features (CQRF), a fixed ten-qubit two-register quantum reservoir. It processes battery cycles independently, producing 57 observables without recurrent feedback. With a Compact Bottleneck Readout (CBR) containing only 125 trainable parameters, CQRF achieves a macro mean absolute percentage error (MAPE) of 0.0172 across four datasets spanning nickel cobalt manganese (NCM), lithium iron phosphate (LFP), and nickel cobalt aluminum (NCA)/NCM-blend batteries, across 398 evaluated cells and ten seeds. Under matched internal settings, CQRF+CBR outperforms the recurrent Quantum Recurrent Reservoir (QRR)+CBR baseline at equal parameter count on all four dataset-level means and 9 of 11 evaluation contexts, while providing at least a 184-fold reduction in trainable parameters relative to the quantum physics-informed neural network (QPINN). Cross-dataset evaluation yields approximate MAPE-derived scores of 86% under leave-one-dataset-out and 85% under single-source transfer. Because normalization uses unlabeled target-dataset statistics, this represents cross-dataset generalization with mild unsupervised target-domain adaptation rather than strict zero-shot transfer. Architecture ablations, capacity-input removal, and cycle-summary controls support the coupled non-recurrent design as an alternative to recurrent quantum memory. Finally, 150 samples (50 each from HUST, MIT, and TJU) are executed on IBM’s 156-qubit ibm_marrakesh processor in an unmitigated quantum processing unit (QPU) audit. Simulator–QPU feature correlations of 0.18–0.41 and a TJU score reduction from 99.02% to 63.53% characterize current hardware execution as a feasibility and noise-sensitivity audit rather than performance validation.

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