arXiv subjects

Gregory Offer

Publications and source records attributed to Gregory Offer.

5 recordsLinked to original sources

arXiv2025

Phantom LAM and LLI: Resistance and Hysteresis Bias in Voltage-Curve Degradation Mode Analysis

Degradation mode analysis (DMA) is widely used to decompose capacity fade into loss of lithium inventory (LLI) and loss of active material (LAM) from low-rate voltage-capacity data. Yet the measured trace is a pseudo-OCV (pOCV) that includes two non-degradation contributions: an SOC-dependent ohmic drop and intrinsic charge-discharge hysteresis, especially in graphite--silicon oxide (C/SiOx) negative electrodes. We show these can dominate attribution and generate Phantom LAM/LLI --apparent material loss created by curve registration, branch choice and voltage-windowing rather than true degradation. Using two commercial 21700 cells (LG M50T: higher resistance; Molicel P45B: lower resistance), we extract an SOC-dependent instantaneous resistance $R_\Omega(\mathrm{SOC})$ from the first $\sim$50,ms pulse step and apply an IR correction to pOCV before fitting. In LG M50T, IR correction lifts the low-rate discharge pOCV by $+13$--$27$,mV with ageing; without it, PE-LAM is increasingly under-diagnosed (to $-8.80%$ relative error at late life) and LLI is suppressed (median $-3.07%$), with compensating inflation of apparent graphite loss. In P45B, on a branch-fair $3.0$--$4.2$,V window, end-of-life charge-branch DMA reports higher PE-LAM ($+3.42$,pp) and LLI ($+5.36$,pp), while the discharge branch recovers larger Si-LAM (discharge--charge difference to $+14.38$,pp). Raising the lower cutoff ($2.5$--$4.2 \rightarrow 3.0$--$4.2$,V) further under-reports Si-LAM by $13.61$,pp by removing the Si-sensitive low-voltage tail. We propose a practical protocol: correct only the instantaneous ohmic term, harmonize the voltage window, and base quantitative attribution on the discharge branch, treating anomalous/negative component LAMs on charge as allocation artefacts rather than recovery.

physics.chem-ph↗

arXiv2025

Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation

Reliable digital twins of lithium-ion batteries must achieve high physical fidelity with sub-millisecond speed. In this work, we benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO), which conditions each spectral layer on particle radius and solid-phase diffusivity. Models are trained on simulated trajectories spanning four current families (constant, triangular, pulse-train, and Gaussian-random-field) and a full range of State-of-Charge (SOC) (0 % to 100 %). DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. The basic FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. Introducing parameter embedding marginally increases error, but enables generalisation to varying radii and diffusivities. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver. Consequently, PE-FNO's capabilities in inverse tasks are explored in a parameter estimation task with Bayesian optimisation, recovering anode and cathode diffusivities with 1.14 % and 8.4 % mean absolute percentage error, respectively, and 0.5918 percentage points higher error in comparison with classical methods. These results pave the way for neural operators to meet the accuracy, speed and parametric flexibility demands of real-time battery management, design-of-experiments and large-scale inference. PE-FNO outperforms conventional neural surrogates, offering a practical path towards high-speed and high-fidelity electrochemical digital twins.

cs.LG↗

arXiv2023

A Computational Framework Integrating Physics-based Model and Equivalent Circuit Network Model to Simulate Li-ion Batteries

Battery models generally fall into two categories: physics-based models and ECM models. Physics-based Doyle-Fuller-Newman (DFN) models can accurately simulate the battery internal electrochemical processes, but to properly account for thermal effects requires a strong coupling between a DFN model and a 3D thermal model, which is computationally unaffordable. Distributed Equivalent Circuit Network (ECN) models can perform simulations with high speed and reasonable accuracy. However, these models rely heavily on the characterisation experiments for ECN parameter identification, which is resource-intensive and can lead to inaccurate parametrisation outcomes due to internal thermal inhomogeneity. To harness the strengths of both models, we propose a computational framework to integrate electrochemical DFN model and 3D distributed ECN model together. Using this framework, we simulate constant current discharge experiments of Kokam 7.5 Ah pouch cell (Model SLPB75106100) and compare the simulations with the commonly-used lumped DFN-thermal model. The computational model outperforms the lumped DFN model at low-temperature and/or high C-rate scenarios significantly. The largest predicting error of the framework at 3 C-rate &Tam = 25oC and at 1 C-rate &Tam = 0 oC is approximately 1/3 of that for DFN model. At 3 C-rate &Tam = 5oC, the difference between these two can rise to 377 mV. By integrating DFN and 3D-distributed ECN together, the computational framework can simulate the complicated interplay between electrochemistry, thermal process, and electricity within a cell fast and accurately. We anticipate this computational framework to be a valuable toolset to assist researchers and engineers in the design and control of Li-ion batteries.

physics.app-ph↗

arXiv2022

Diffusion-aware voltage source: An equivalent circuit network to resolve lithium concentration gradients in active particles

Traditional equivalent circuit models (ECMs) have difficulties in estimating battery internal states due to the lack of relevant physics, such as the lithium diffusion in active particles. Here we configure a circuit network to describe the lithium diffusion and define it as a new high-level circuit element called diffusion-aware voltage source. The circuit representation is proven equivalent to the discretized diffusion equation. The new voltage source gives the electrode potential as a function of the surface concentration and thus automatically incorporates the diffusion overpotential. We show that an ECM with the proposed diffusion-aware voltage sources (called "shell ECM") can reproduce the single particle model simulation results, making it a trustworthy easy-to-implement substitute. Furthermore, the simplest shell ECM consisting of a single diffusion-aware voltage source and a resistor is validated against experimental constant-current discharges at various rates. The diffusion-aware voltage source can be used to measure diffusivity by fitting the diffusion resistance against experimental data. The viability of the shell ECM for onboard usage is confirmed by implementation into a battery management system of WAE Technologies. By tracking the internal concentration states, the shell ECM demonstrates robustness to dynamic applied-current profiles.

cond-mat.mtrl-sci↗

arXiv2022

Degradation model of high-nickel positive electrodes: Effects of loss of active material and cyclable lithium on capacity fade

Nickel-rich layered oxides have been widely used as positive electrode materials for high-energy-density lithium-ion batteries, but their degradation has severely affected cell performance, in particular at a high voltage and temperature. However, the underlying degradation mechanisms have not been well understood due to the complexity and lack of predictive models.Here we present a model at the particle level to describe the structural degradation caused by phase transition in terms of loss of active material (LAM), loss of lithium inventory (LLI), and resistance increase. The particle degradation model is then incorporated into a cell-level P2D model to explore the effects of LAM and LLI on capacity fade in cyclic ageing tests. It is predicted that the loss of cyclable lithium (trapped in the degraded shell) leads to a shift in the stoichiometry range of the negative electrode but does not directly contribute to the capacity loss, and that the loss of positive electrode active materials dominates the fade of usable cell capacity in discharge. The available capacity at a given current rate is further decreased by the additional resistance of the degraded shell layer. The change pattern of the state-of-charge curve provides information of more dimensions than the conventional capacity-fade curve, beneficial to the diagnosis of degradation modes. The model has been implemented into PyBaMM and made available as open source codes.

cond-mat.mtrl-sci↗