arXiv subjects

David A. Howey

Publications and source records attributed to David A. Howey.

At least 19 recordsLinked to original sources

arXiv2026

Multisine electrochemical impedance spectroscopy for Li-ion battery characterisation

Electrochemical impedance spectroscopy (EIS) is a valuable tool for non-invasive battery characterisation as it provides a compact data representation of physical processes over a wide range of time scales. Commonly, sinusoids at different frequencies are injected sequentially (single-sines). Alternatively, a multisine excitation (a sum of sines) is advantageous for reducing experiment time and allowing impedance to be measured during operational conditions (e.g.\ charging, discharging, relaxation, and during temperature changes). In this work, we demonstrate high-fidelity multisine EIS measurements on Li-ion cells over a wide frequency range (20 mHz to 1 kHz) taken with a commercial potentiostat, and compare these to single-sine EIS, discussing the advantages of both techniques and how to verify the conditions of linearity and stationarity. We also measure broadband multisine impedance at different operating conditions (during charging/discharging, relaxation, and temperature changes), showing how this tool gives new insights into battery dynamics, material properties, charge transfer processes, and thermal performance.

eess.SY↗

arXiv2025

A Primer on Bayesian Parameter Estimation and Model Selection for Battery Simulators

Physics-based battery modelling has emerged to accelerate battery materials discovery and performance assessment. Its success, however, is still hindered by difficulties in aligning models to experimental data. Bayesian approaches are a valuable tool to overcome these challenges, since they enable prior assumptions and observations to be combined in a principled manner that improves numerical conditioning. Here we introduce two new algorithms to the battery community, SOBER and BASQ, that greatly speed up Bayesian inference for parameterisation and model comparison. We showcase how Bayesian model selection allows us to tackle data observability, model identifiability, and data-informed model development together. We propose this approach for the search for battery models of novel materials.

stat.ME↗

arXiv2025

Pseudo-random sequences for low-cost operando impedance measurements of Li-ion batteries

Operando impedance measurements are promising for monitoring batteries in the field. In this work, we present pseudo-random sequences for low-cost operando battery impedance measurements. The quadratic-residue ternary sequence and direct-synthesis ternary sequence exhibit specific properties related to eigenvectors of the discrete Fourier transform matrix that allow computationally efficient compensation for drifts and transients in operando impedance measurements. We describe the application of pseudo-random sequences and provide the data processing required to suppress drift and transients, validated on simulations. Finally, we perform experimental operando impedance measurements on a Li-ion battery cell during fast-charging, demonstrating the applicability of the proposed method. It's low-cost hardware requirements, fast measurements, and simple data-processing make the method practical for embedding in battery management systems.

eess.SY↗

arXiv2024

PyBOP: A Python package for battery model optimisation and parameterisation

The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, including the electrochemical and equivalent circuit models provided by the popular open-source PyBaMM package. Using the same approaches, PyBOP can also be used for design optimisation under user-defined operating conditions across a variety of model structures and design goals. PyBOP facilitates optimisation with a range of methods, with diagnostics for examining optimiser performance and convergence of the cost and corresponding parameters. Identified parameters can be used for prediction, on-line estimation and control, and design optimisation, accelerating battery research and development.

eess.SY↗

arXiv2024

Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science

Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to keep up. This paper discusses strategies based on structured, semantic, and linked data to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use a battery-related ontology, BattINFO to standardise terms and enable automated data extraction and analysis. Our methodology integrates full-text search and machine-readable data, enhancing data retrieval and battery testing. We aim to unify commercial cell information and develop tools for the battery community such as manufacturer-independent cycling procedure descriptions and external memory for Large Language Models. Although only a first step, this approach significantly accelerates battery research and digitalizes battery testing, inviting community participation for continuous improvement. We provide the structured data and the tools to access them as open source.

cs.IR↗

arXiv2024

The Impact of Grid Storage on Balancing Costs and Carbon Emissions in Great Britain

Grid energy storage can help to balance supply and demand, but its financial viability and operational carbon emissions impact is poorly understood because of the complexity of grid constraints and market outcomes. We analyse the impact of several technologies (Li-ion and flow batteries, pumped hydro, hydrogen) on Great Britain balancing mechanism, the main market for supply-demand balancing and congestion management. We find that, for many locations and technologies, financially optimal operation of storage for balancing can result in higher carbon emissions. For example, the extra emissions associated with a 1 MW 2-hour duration Li-ion battery in winter vary between +230 to -71 kgCO2/h. Although storage enable higher usage of renewables, it can also unlock additional demand leading to greater use of gas. In addition, balancing services alone are presently insufficient for financial viability of storage projects. This work highlights the need for market reform aligning financial incentives with environmental impacts.

eess.SY↗

arXiv2024

Depreciation Cost is a Poor Proxy for Revenue Lost to Aging in Grid Storage Optimization

Dispatch of a grid energy storage system for arbitrage is typically formulated into a rolling-horizon optimization problem that includes a battery aging model within the cost function. Quantifying degradation as a depreciation cost in the objective can increase overall profits by extending lifetime. However, depreciation is just a proxy metric for battery aging; it is used because simulating the entire system life is challenging due to computational complexity and the absence of decades of future data. In cases where the depreciation cost does not match the loss of possible future revenue, different optimal usage profiles result and this reduces overall profit significantly compared to the best case (e.g., by 30-50%). Representing battery degradation perfectly within the rolling-horizon optimization does not resolve this - in addition, the economic cost of degradation throughout life should be carefully considered. For energy arbitrage, optimal economic dispatch requires a trade-off between overuse, leading to high return rate but short lifetime, vs. underuse, leading to a long but not profitable life. We reveal the intuition behind selecting representative costs for the objective function, and propose a simple moving average filter method to estimate degradation cost. Results show that this better captures peak revenue, assuming reliable price forecasts are available.

eess.SY↗

arXiv2023

Constrained optimal control of monotone systems with applications to battery fast-charging

Enabling fast charging for lithium ion batteries is critical to accelerating the green energy transition. As such, there has been significant interest in tailored fast-charging protocols computed from the solutions of constrained optimal control problems. Here, we derive necessity conditions for a fast charging protocol based upon monotone control systems theory.

math.OC↗

arXiv2023

Looping in the Human Collaborative and Explainable Bayesian Optimization

Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity. While attempts have been made to develop human-centric optimizers, they typically assume user knowledge is well-specified and error-free, employing users mainly as supervisors of the optimization process. We relax these assumptions and propose a more balanced human-AI partnership with our Collaborative and Explainable Bayesian Optimization (CoExBO) framework. Instead of explicitly requiring a user to provide a knowledge model, CoExBO employs preference learning to seamlessly integrate human insights into the optimization, resulting in algorithmic suggestions that resonate with user preference. CoExBO explains its candidate selection every iteration to foster trust, empowering users with a clearer grasp of the optimization. Furthermore, CoExBO offers a no-harm guarantee, allowing users to make mistakes; even with extreme adversarial interventions, the algorithm converges asymptotically to a vanilla Bayesian optimization. We validate CoExBO's efficacy through human-AI teaming experiments in lithium-ion battery design, highlighting substantial improvements over conventional methods. Code is available https://github.com/ma921/CoExBO.

cs.LG↗

arXiv2023

Fast dynamic time warping and clustering in C++

We present an approach for computationally efficient dynamic time warping (DTW) and clustering of time-series data. The method frames the dynamic warping of time series datasets as an optimisation problem solved using dynamic programming, and then clusters time series data by solving a second optimisation problem using mixed-integer programming (MIP). There is also an option to use k-medoids clustering for increased speed, when a certificate for global optimality is not essential. The improved efficiency of our approach is due to task-level parallelisation of the clustering alongside DTW. Our approach was tested using the UCR Time Series Archive, and was found to be, on average, 33% faster than the next fastest option when using the same clustering method. This increases to 64% faster when considering only larger datasets (with more than 1000 time series). The MIP clustering is most effective on small numbers of longer time series, because the DTW computation is faster than other approaches, but the clustering problem becomes increasingly computationally expensive as the number of time series to be clustered increases.

eess.SP↗

arXiv2023

Learning battery model parameter dynamics from data with recursive Gaussian process regression

Estimating state of health is a critical function of a battery management system but remains challenging due to the variability of operating conditions and usage requirements of real applications. As a result, techniques based on fitting equivalent circuit models may exhibit inaccuracy at extremes of performance and over long-term ageing, or instability of parameter estimates. Pure data-driven techniques, on the other hand, suffer from lack of generality beyond their training dataset. In this paper, we propose a hybrid approach combining data- and model-driven techniques for battery health estimation. Specifically, we demonstrate a Bayesian data-driven method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime. Computational efficiency is ensured through a recursive approach yielding a unified joint state-parameter estimator that learns parameter dynamics from data and is robust to gaps and varying operating conditions. Results show the efficacy of the method, on both simulated and measured data, including accurate estimates and forecasts of battery capacity and internal resistance. This opens up new opportunities to understand battery ageing in real applications.

eess.SY↗

arXiv2022

Bayesian hierarchical modelling for battery lifetime early prediction

Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this -- along with the limited experimental resources usually available for each cycling condition -- makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.

eess.SY↗

arXiv2022

Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.

stat.ME↗

arXiv2022

Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis

Large-scale grid-connected lithium-ion batteries are increasingly being deployed to support renewable energy roll-out on the power grid. These battery systems consist of thousands of individual cells and various ancillary systems for monitoring and control. Although many studies have focused on the behaviour of single lithium-ion cells, the impact of system design choices and ancillary system controls on long-term degradation and efficiency of system, containing thousands of cells, has rarely been considered in detail. Here, we simulate a 1 MWh grid battery system consisting of 18900 individual cells, each represented by a separate electrochemical model, as well as the thermal management system and power electronic converters. Simulations of the impact of cell-to-cell variability, thermal effects, and degradation effects were run for up to 10000 cycles and 10 years. It is shown that electrical contact resistances and cell-to-cell variations in initial capacity and resistance have a smaller effect on performance than previously thought. Instead, the variation in degradation rate of individual cells dominates the system behaviour over the lifetime. The importance of careful thermal management system control is demonstrated, with proportional control improving overall efficiency by 5 %-pts over on-off methods, also increasing the total usable energy of the battery by 5 %-pts after 10 years.

eess.SY↗

arXiv2021

Anisotropic thermal characterisation of large-format lithium-ion pouch cells

Temperature strongly impacts battery performance, safety and durability, but modelling heat transfer requires accurately measured thermal properties. Herein we propose new approaches to characterise the heat capacity and anisotropic thermal-conductivity components for lithium-ion pouch cells. Heat capacity was estimated by applying Newton's law of cooling to an insulated container within which the cell was submerged in warmed dielectric fluid. Thermal conductivity was quantified by heating one side of the cell and measuring the opposing temperature distribution with infra-red thermography, then inverse modelling with the anisotropic heat equation. Experiments were performed on commercial 20 Ah lithium iron phosphate (LFP) pouch cells. At 100% state-of-charge (SOC), the heat capacity of a 489 g, 224 mL pouch cell was 541 J/K. The through-plane and in-plane thermal conductivities were respectively 0.52 and 26.6 W/(mK). Capturing anisotropies in conductivity is important for accurate thermal simulations. State-of-charge dependence was also probed by testing at 50% SOC: the heat capacity dropped by 6% and thermal conductivity did not significantly change.

physics.chem-ph↗

arXiv2021

Principles of the Battery Data Genome

Electrochemical energy storage is central to modern society -- from consumer electronics to electrified transportation and the power grid. It is no longer just a convenience but a critical enabler of the transition to a resilient, low-carbon economy. The large pluralistic battery research and development community serving these needs has evolved into diverse specialties spanning materials discovery, battery chemistry, design innovation, scale-up, manufacturing and deployment. Despite the maturity and the impact of battery science and technology, the data and software practices among these disparate groups are far behind the state-of-the-art in other fields (e.g. drug discovery), which have enjoyed significant increases in the rate of innovation. Incremental performance gains and lost research productivity, which are the consequences, retard innovation and societal progress. Examples span every field of battery research , from the slow and iterative nature of materials discovery, to the repeated and time-consuming performance testing of cells and the mitigation of degradation and failures. The fundamental issue is that modern data science methods require large amounts of data and the battery community lacks the requisite scalable, standardized data hubs required for immediate use of these approaches. Lack of uniform data practices is a central barrier to the scale problem. In this perspective we identify the data- and software-sharing gaps and propose the unifying principles and tools needed to build a robust community of data hubs, which provide flexible sharing formats to address diverse needs. The Battery Data Genome is offered as a data-centric initiative that will enable the transformative acceleration of battery science and technology, and will ultimately serve as a catalyst to revolutionize our approach to innovation.

physics.soc-ph↗

arXiv2021

Multiscale coupling of surface temperature with solid diffusion in large lithium-ion pouch cells

Untangling the relationship between reactions, mass transfer, and temperature within lithium-ion batteries enables control approaches that mitigate thermal hot spots and slow degradation. Here, we develop an efficient physics-based pouch-cell model to simulate lock-in thermography experiments, which synchronously record the applied current, cell voltage, and surface-temperature distribution. Prior modelling efforts have been confounded by experimental temperature profiles whose characteristics suggest anisotropic heat conduction. Accounting for a multiscale coupling between heat flow and solid-state diffusion rationalizes this surface-temperature nonuniformity. We extend an earlier streamlined model based on the popular Doyle--Fuller--Newman theory, augmented by a local heat balance. The reduced-order model is exploited to parametrize and simulate commercial 20 Ah lithium iron phosphate (LFP) cells at currents up to 80 A. This work highlights how microscopic intercalation processes produce distinctive macroscopic heat signatures in large-format cells, as well as how heat signatures can be exploited to fingerprint material properties.

physics.chem-ph↗

arXiv2021

Estimation of Li-ion degradation test sample sizes required to understand cell-to-cell variability

Ageing of lithium-ion batteries results in irreversible reduction in performance. Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life and increases with age. Researchers need to know the minimum number of cells they should test to give an accurate representation of population variability, since testing many cells is expensive. In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets for commercially available cells assuming the model parameters could be drawn from a larger population distribution. Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested. Depending on the complexity, ageing models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit. This implies researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability.

stat.AP↗