arXiv subjects

Remus Teodorescu

Publications and source records attributed to Remus Teodorescu.

9 recordsLinked to original sources

arXiv2026

Parameterisation of NMC and LFP 4680 cells for electrochemical and thermal modelling

An electrochemical-thermal model of the 4680 cylindrical cell is presented: a Doyle-Fuller-Newman model of the electrode stack coupled to a two-node thermal balance of the roll and the can (a thermal DFN, TDFN), solved by an implicit finite-volume scheme. Two cells are parameterised from teardown geometry and open literature, with every value tagged by provenance: the Tesla NMC811/graphite tabless cell and the BYD FC4680 LiFePO4 multi-tab cell. The reaction heat is evaluated at the open-circuit potential of the particle-mean concentration, which restores the heat of mixing. Both sets are calibrated in the same way, with the test fixture identified from the measurement alone. Against the 1C and 2C charge test of Ank et al., the calibrated NMC set follows the mean can temperature within 0.52 and 1.46 K, with a contact heat of 0.94 mOhm matching the authors' estimate for their holder. For the LFP cell, 1C and 2C discharges to 2.0 V were measured on the authors' bench; the fixture conductance (0.337 W/K) follows from the energy balance and the heat capacity (277 J/K) from the cooling. With the end-of-lithiation drop of LiFePO4 restored in the cathode open-circuit potential, the calibrated set follows the can temperature within 0.44 K (1C) and 0.76 K (2C), reproduces the peaks and the relaxation after cut-off. What remains is what a single can node and a single representative particle do not describe: the axial gradient of the NMC can under contact heating, and a gradual decline of the LFP voltage before the knee at 2C, which points to the distribution of particle states in the electrode. The parameter sets (BPX, CSV) and the bench data are openly available.

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arXiv2026

Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference

Grid foundation models (GridFMs) are graph transformers pretrained on a masked power-flow objective, and evaluate power flow orders of magnitude faster than an iterative solver. Their size, however, targets data-centre inference, whereas low-inertia operation increasingly asks for inference at the edge, next to the measurement, at batch size one and within a single synchrophasor (PMU) reporting frame. We distil a 20.1M-parameter, 12-layer GridFM teacher into a 0.62M-parameter, 6-layer student, a $32.5\times$ parameter reduction at half the message-passing depth, using an objective that combines the masked power-flow loss with output, physics-residual, latent-state and attention transfer. The physics-residual term, which matches the teacher's power-residual profile, has not previously been applied to typed power-grid graphs. We evaluate on a renewable variant of the IEEE 118-bus benchmark grid in which 26 of the conventional (synchronous) generators are replaced by inverter-based renewables with battery flexibility and standardised reactive-power limits (IEEE 1547), giving $97\,529$ physically valid (AC-OPF-feasible) operating scenarios, and we ablate all eight combinations of the transfer terms, reporting accuracy against ground truth and fidelity to the teacher separately. Distillation lowers student error on all four predicted quantities, by up to $48\%$ on active-power dispatch, and every distilled student is more accurate than its teacher on reactive-power dispatch. The gain is largely carried by the output matching term. The physics-residual term, in the layer-aggregated form used here, is consistently harmful, which we attribute to the spatial averaging it applies to a bus-level residual field. The resulting student is $32.5\times$ smaller in parameter count and has half the message-passing depth, making it a candidate for subsequent edge-device benchmarking.

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arXiv2026

Acceleration of Modelling with Physics Informed Learning: Frameworks and Perspectives for Real-Time Control of Electrochemical Devices

Electrochemical devices (batteries, fuel cells, and electrolyzers) are in full development, driven by the green energy transition. Their real-time control requires ms predictions in order to take critical decisions during fast transients or faults. The physics behind include coupled multi-physics phenomena that conventional finite element methods cannot solve so fast with the current CPU technology. This paper evaluates the potential of physics-informed machine learning represented by three frameworks: \ac{pinn}, \ac{pideeponet}, and \ac{pino} by evaluating their training effort, inference speed, and extrapolation capacity. Our analysis reveals valuable performance trade-offs. \acp{pinn} offer simplicity for fixed problem instances but require retraining for parameter changes. \ac{pideeponet} enables operator learning across varying conditions with mesh-free geometric flexibility. \ac{pino} delivers superior performance on regular grids, with the strongest extrapolation capabilities due to spectral derivative computation and resolution invariance. \ac{pideeponet} is particularly suited for irregular, unstructured geometries (e.g., porous electrodes or complex flow fields), while \ac{pino} works best for layered, structured-grid problems (e.g., transport across stacked electrochemical layers) requiring fast inference. Possible future applications include real-time lithium concentration prediction for safe fast-charging and micro short circuit detection, water management in fuel cells, and optimal power management in electrolyzers under intermittent renewable inputs. These findings establish physics-informed operator learning as a transformative approach for next-generation electrochemical device controller technology.

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arXiv2026

Physics-informed neural network surrogate modeling of single particle model for lithium-ion batteries

Physics-based models play a key role in battery management, yet face challenges in real-time applications due to the high computational cost of solving coupled algebraic-partial differential equations. To accelerate model simulation, this study benchmarks three physics-informed neural network (PINN) architectures for modeling the battery single particle model, including two conventional PINN architectures and a DeepONet-based architecture. Both the accuracy and the generalization of these PINNs have been evaluated and compared under various current conditions. Our results highlight the potential of PINNs in modeling battery physics but also reveal limitations of conventional PINN architectures under highly dynamic current conditions. Among them, the Fourier-enhanced DeepONet achieves superior generalization performance and offers nearly a 10 times speedup compared with numerical solvers. This work provides an example of integrating physics-based models

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arXiv2025

Data-Selective Online Battery Identification Using Extended Time Regular Expressions

In this paper, we propose a data-efficient online battery identification method which targets highly informative battery cell data segments based on the driving pattern of the vehicle. We consider the case of a vehicle driving on/off a motorway and construct an Extended Time Regular Expression (ETRE) to detect data segments fitting these driving patterns. Simulation results indicate that by only using up to 10.71% of the data on average, the proposed method provides a low-bias and low-variance estimator under non-negligible current and voltage noise compared to other conventional estimation algorithms.

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arXiv2025

Merging Physics-Based Synthetic Data and Machine Learning for Thermal Monitoring of Lithium-ion Batteries: The Role of Data Fidelity

Since the internal temperature is less accessible than surface temperature, there is an urgent need to develop accurate and real-time estimation algorithms for better thermal management and safety. This work presents a novel framework for resource-efficient and scalable development of accurate, robust, and adaptive internal temperature estimation algorithms by blending physics-based modeling with machine learning, in order to address the key challenges in data collection, model parameterization, and estimator design that traditionally hinder both approaches. In this framework, a physics-based model is leveraged to generate simulation data that includes different operating scenarios by sweeping the model parameters and input profiles. Such a cheap simulation dataset can be used to pre-train the machine learning algorithm to capture the underlying mapping relationship. To bridge the simulation-to-reality gap resulting from imperfect modeling, transfer learning with unsupervised domain adaptation is applied to fine-tune the pre-trained machine learning model, by using limited operational data (without internal temperature values) from target batteries. The proposed framework is validated under different operating conditions and across multiple cylindrical batteries with convective air cooling, achieving a root mean square error of 0.5 °C when relying solely on prior knowledge of battery thermal properties, and less than 0.1 °C when using thermal parameters close to the ground truth. Furthermore, the role of the simulation data quality in the proposed framework has been comprehensively investigated to identify promising ways of synthetic data generation to guarantee the performance of the machine learning model.

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arXiv2024

Optimal DC-link Voltage from Weight and Loss Perspective for eVTOLs

Electric vertical takeoff and landing (eVTOL) aircraft are emerging as a modern transportation solution aimed at reducing urban traffic congestion and improving the carbon footprint. The power architecture in eVTOLs is defined by the dc bus formed by the battery packs and the power converter used to drive eVTOL motors. A high dc bus voltage is preferred for the power architecture since it can reduce the weight of power cables for a given power rating. However, the impact of high dc bus voltage on the efficiency of the drivetrain power converter must be considered, since reduced efficiency leads to poor battery pack utilization. In this paper, a systematic optimization study is performed considering SiC-based inverter for the drivetrain power converter. Optimal value of dc bus voltage is determined considering the flight profile of eVTOLs. A power converter topology is proposed that can provide optimal performance and enhance the lifetime of the batteries along with providing better monitoring, diagnostics and protection. The optimization strategy is validated experimentally, demonstrating the proposed power architecture's ability to maximize efficiency while enhancing the safety of the battery energy storage system in eVTOLs.

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arXiv2024

Computationally Efficient Machine-Learning-Based Online Battery State of Health Estimation

A key function of battery management systems (BMS) in e-mobility applications is estimating the battery state of health (SoH) with high accuracy. This is typically achieved in commercial BMS using model-based methods. There has been considerable research in developing data-driven methods for improving the accuracy of SoH estimation. The data-driven methods are diverse and use different machine-learning (ML) or artificial intelligence (AI) based techniques. Complex AI/ML techniques are difficult to implement in low-cost microcontrollers used in BMS due to the extensive use of non-linear functions and large matrix operations. This paper proposes a computationally efficient and data-lightweight SoH estimation technique. Online impedance at four discrete frequencies is evaluated to derive the features of a linear regression problem. The proposed solution avoids complex mathematical operations and it is well-suited for online implementation in a commercial BMS. The accuracy of this method is validated on two experimental datasets and is shown to have a mean absolute error (MAE) of less than 2% across diverse training and testing data.

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arXiv2024

Novel Low-Complexity Model Development for Li-ion Cells Using Online Impedance Measurement

Modeling of Li-ion cells is used in battery management systems (BMS) to determine key states such as state-of-charge (SoC), state-of-health (SoH), etc. Accurate models are also useful in developing a cell-level digital-twin that can be used for protection and diagnostics in the BMS. In this paper, a low-complexity model development is proposed based on the equivalent circuit model (ECM) of the Li-ion cells. The proposed approach uses online impedance measurement at discrete frequencies to derive the ECM that matches closely with the results from the electro-impedance spectroscopy (EIS). The proposed method is suitable to be implemented in a microcontroller with low-computational power, typically used in BMS. Practical design guidelines are proposed to ensure fast and accurate model development. Using the proposed method to enhance the functions of a typical automotive BMS is described. Experimental validation is performed using large prismatic cells and small-capacity cylindrical cells. Root-mean-square error (RMSE) of less than 3\% is observed for a wide variation of operating conditions.

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