Alexandre Barbosa de Lima

Alexandre Barbosa de Lima, Roberta Vieira Raggi

Accurate terminal-voltage prediction underpins model-based battery management, yet low-order equivalent-circuit models (\ecm{}) lack expressiveness under transient conditions, whereas purely data-driven predictors sacrifice interpretability and may degrade under operating-condition shift. This paper introduces a residual-corrected hybrid formulation in which a first-order Thevenin \ecm{} (\ecmrc{}) provides the dominant voltage structure, and a compact neural network embedded as a universal differential equation (\ude{}) corrects only the latent polarization mismatch. The \ecmrc{} parameters identified by nonlinear least squares warm-start the hybrid model so that the learned component operates in a low-residual regime. Experiments on a public Panasonic 18650PF dataset compare the proposed \ecmude{} with standalone \ecmrc{} and Long Short-Term Memory (\lstm{}) baselines across four axes: matched-condition prediction on UDDS at \SI{25}{\celsius}, inference-time perturbation of the supplied state-of-charge (\SOC{}, denoted $z$) input, zero-shot temperature transfer (\SI{25}{\celsius} to \SI{-20}{\celsius}), and zero-shot drive-cycle transfer to US06, LA92, and HWFET. The proposed \ecmude{} achieves the lowest voltage error in every setting, reducing mean absolute error (\mae{}) by 48\% relative to the \lstm{} under matched conditions and showing an order-of-magnitude lower inter-seed variability (coefficient of variation: 0.44\% vs.\ 6.20\%). Substantial gains persist under challenging distribution shifts, indicating that the physical model anchors prediction where a purely learned model is most vulnerable. These results position residual-corrected \ecmude{} as a lightweight and interpretable enhancement of low-order circuit models for voltage prediction in battery management systems (\bms{}).

Alexandre Barbosa de Lima

Channel estimation in wideband multiple-input multiple-output (MIMO) systems faces fundamental pilot overhead limitations in high-dimensional beyond-5G and sixth-generation (6G) scenarios. This paper presents a hybrid tensor-neural architecture that formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations -- a fundamentally different setting from prior tensor methods that assume fully observed received signal tensors. A canonical polyadic (CP) baseline implemented via a projection-based scheme (Tucker completion under partial observations) and Tucker decompositions are compared under varying signal-to-noise ratio (SNR) and scattering conditions: CP performs well for specular channels matching the multipath model, while Tucker provides greater robustness under model mismatch. A lightweight three-dimensional (3D) U-Net learns residual components beyond the low-rank structure, bridging algebraic models and realistic propagation effects. Empirical recovery threshold analysis shows that sample complexity scales approximately with intrinsic model dimensionality $L(N_r + N_t + N_f)$ rather than ambient tensor size $N_r N_t N_f$, where $L$ denotes the number of dominant propagation paths. Experiments on synthetic channels demonstrate 10-20\,dB normalized mean-square error (NMSE) improvement over least-squares (LS) and orthogonal matching pursuit (OMP) baselines at 5-10\% pilot density, while evaluations on DeepMIMO ray-tracing channels show 24-44\% additional NMSE reduction over pure tensor-based methods.

Alexandre Barbosa de Lima, Maurício B. C. Salles, José Roberto Cardoso

This article presents two Deep Forward Networks with two and four hidden layers, respectively, that model the drive cycle of a Panasonic 18650PF lithium-ion (Li-ion) battery at a given temperature using the K-fold cross-validation method, in order to estimate the State of Charge (SOC) of the cell. The drive cycle power profile is calculated for an electric truck with a 35kWh battery pack scaled for a single 18650PF cell. We propose a machine learning workflow which is able to fight overfitting when developing deep learning models for SOC estimation. The contribution of this work is to present a methodology of building a Deep Forward Network for a lithium-ion battery and its performance assessment, which follows the best practices in machine learning.