arXiv subjects

David Howey

Publications and source records attributed to David Howey.

14 recordsLinked to original sources

arXiv2026

Lifetime Profit-Maximising Co-optimisation of Multi-Service Stacking for Battery Storage

Grid-scale battery energy storage can generate revenue by stacking services across electricity and frequency response markets, yet identifying the lifetime profit-maximising stacking strategy remains challenging. Decisions across services are coupled through shared battery system capacity, constrained by system operator energy management rules, and further shaped by product-specific technical requirements that govern system operation, degradation, and lifetime profitability. This paper presents an ageing-aware receding-horizon framework for co-optimising multi-service stacking that explicitly captures product-specific characteristics and state-of-energy compliance rules. The framework is applied to the Great Britain market, where storage operators can stack electricity trading with multiple dynamic frequency response services procured through the newly introduced 'Enduring Auction Capability' platform under energy management requirements imposed by the National Energy System Operator. Using real market data, we demonstrate that degradation modelling, discount rate, and battery ageing jointly govern both lifetime value and optimal stacking strategy. Accounting for ageing increases lifetime revenue by up to 32% relative to a degradation-agnostic benchmark, while higher-fidelity ageing modelling can provide a further revenue improvement of up to 16% over simpler formulations. Lower discount rates favour strategies that balance calendar and cycling ageing, while higher discount rates favour aggressive operation. Across all strategies, services responding to positive frequency deviations are consistently preferred over those responding to negative deviations. Lifetime profit is maximised by adapting the optimal service mix as the battery ages.

math.OC↗

arXiv2026

Current and temperature imbalances in parallel-connected grid storage battery modules

A key challenge with large battery systems is heterogeneous currents and temperatures in modules with parallel-connected cells. Although extreme currents and temperatures are detrimental to the performance and lifetime of battery cells, there is not a consensus on the scale of typical imbalances within grid storage modules. Here, we quantify these imbalances through simulations and experiments on an industrially representative grid storage battery module consisting of prismatic lithium iron phosphate cells, elucidating the evolution of current and temperature imbalances and their dependence on individual cell and module parameter variations. Using a sensitivity analysis, we find that varying contact resistances and cell resistances contribute strongly to temperature differences between cells, from which we define safety thresholds on cell-to-cell variability. Finally, we investigate how these thresholds change for different applications, to outline a set of robustness metrics that show how cycling at lower C-rates and narrower SOC ranges can mitigate failures.

eess.SY↗

arXiv2025

Opportunities for real-time process control of electrode properties in lithium-ion battery manufacturing

Lithium-ion batteries (LIBs) have an important role in the shift required to achieve a global net-zero carbon target of 2050. Electrode manufacture is amongst the most expensive steps of the LIB manufacturing process and, despite its apparent maturity, optimised manufacturing conditions are arrived at by largely trial and error. Currently, LIB manufacturing plants are controlled to follow the fixed "recipe" obtained by trial and error, which may nonetheless be suboptimal. Moreover, regulating the process as a whole to conform to the set conditions is not widespread. Inspired by control approaches used in other film and sheet processes, we discuss opportunities for implementing real-time process control of electrode-related products, which has the potential to reduce the electrode manufacturing cost, CO2 emissions, usage of resources by increases in process yield, and throughput. We highlight the challenges and significant opportunities of implementing real-time process control in LIB electrode production lines.

eess.SY↗

arXiv2025

Understanding long-term energy use in off-grid solar home systems in sub-Saharan Africa

Solar home systems provide low-cost electricity access for rural off-grid communities. As access to them increases, more long-term data becomes available on how these systems are used throughout their lifetime. This work analyses a dataset of 1,000 systems across sub-Saharan Africa. Dynamic time warping clustering was applied to the load demand data from the systems, identifying five distinct archetypal daily load profiles and their occurrence across the dataset. Temporal analysis reveals a general decline in daily energy consumption over time, with 77% of households reducing their usage compared to the start of ownership. On average, there is a 33% decrease in daily consumption by the end of the second year compared to the peak demand, which occurs on the 96th day. Combining the load demand analysis with payment data shows that this decrease in energy consumption is observed even in households that are not experiencing economic hardship, indicating there are reasons beyond financial constraints for decreasing energy use once energy access is obtained.

eess.SY↗

arXiv2024

Physics-based battery model parametrisation from impedance data

Non-invasive parametrisation of physics-based battery models can be performed by fitting the model to electrochemical impedance spectroscopy (EIS) data containing features related to the different physical processes. However, this requires an impedance model to be derived, which may be complex to obtain analytically. We have developed the open-source software PyBaMM-EIS that provides a fast method to compute the impedance of any PyBaMM model at any operating point using automatic differentiation. Using PyBaMM-EIS, we investigate the impedance of the single particle model, single particle model with electrolyte (SPMe), and Doyle-Fuller-Newman model, and identify the SPMe as a parsimonious option that shows the typical features of measured lithium-ion cell impedance data. We provide a grouped parameter SPMe and analyse the features in the impedance related to each parameter. Using the open-source software PyBOP, we estimate 18 grouped parameters both from simulated impedance data and from measured impedance data from a LG M50LT lithium-ion battery. The parameters that directly affect the response of the SPMe can be accurately determined and assigned to the correct electrode. Crucially, parameter fitting must be done simultaneously to data across a wide range of states-of-charge. Overall, this work presents a practical way to find the parameters of physics-based models.

eess.SY↗

arXiv2024

Extra Throughput versus Days Lost in load-shifting V2G services: Influence of dominant degradation mechanism

Electric vehicle (EV) batteries are often underutilized. Vehicle-to-grid (V2G) services can tap into this unused potential, but increased battery usage may lead to more degradation and shorter battery life. This paper substantiates the advantages of providing load-shifting V2G services when the battery is aging, primarily due to calendar aging mechanisms (active degradation mechanisms while the battery is not used). After parameterizing a physics-based digital-twin for three different dominant degradation patterns within the same chemistry (NMC), we introduce a novel metric for evaluating the benefit and associated harm of V2G services: \textit{throughput gained versus days lost (TvD)} and show its strong relationship to the ratio of loss of lithium inventory (LLI) due to calendar aging to the total LLI ($\text{LLI}_\text{Cal}/\text{LLI}$). Our results that focus systematically on degradation mechanisms via lifetime simulation of digital-twins significantly expand prior work that was primarily concentrating on quantifying and reducing the degradation of specific cells by probing their usage and charging patterns. Examining various cell chemistries and conditions enables us to take a broader view and determine whether a particular battery pack is appropriate for load-shifting (V2G) services. Our research demonstrates that the decision "to V2G or not to V2G" can be made by merely estimating the portion of capacity deterioration caused by calendar aging. Specifically, TvD is primarily influenced by the chemistry of cells and the environmental temperature where the car is parked, while the usage intensity and charging patterns of EVs play a lesser role.

eess.SY↗

arXiv2024

Learning Li-ion battery health and degradation modes from data with aging-aware circuit models

Non-invasive estimation of Li-ion battery state-of-health from operational data is valuable for battery applications, but remains challenging. Pure model-based methods may suffer from inaccuracy and long-term instability of parameter estimates, whereas pure data-driven methods rely heavily on training data quality and quantity, causing lack of generality when extrapolating to unseen cases. We apply an aging-aware equivalent circuit model for health estimation, combining the flexibility of data-driven techniques within a model-based approach. A simplified electrical model with voltage source and resistor incorporates Gaussian process regression to learn capacity fade over time and also the dependence of resistance on operating conditions and time. The approach was validated against two datasets and shown to give accurate performance with less than 1% relative root mean square error (RMSE) in capacity and less than 2% mean absolute percentage error (MAPE). Critically, we show that the open circuit voltage versus state-of-charge function must be accurately known, and any inaccuracies or changes in this over time strongly influence the inferred resistance. However, this feature (or bug) may also be used to estimate in operando differential voltage curves from operational data.

eess.SY↗

arXiv2023

Lead-acid battery lifetime extension in solar home systems under different operating conditions

Solar home systems (SHS) provide low-cost electricity access for rural off-grid communities. Batteries are a crucial part of the system, however they are often the first point of failure due to shorter lifetimes. Using field data, this work models the degradation of lead-acid batteries for different SHS use-cases, finding the dominant ageing mechanisms in each case. Corrosion is the dominant ageing mechanisms in all cases apart from the highest use case. This is caused by extended time at high state of charge (SOC) and hence high voltage. A new voltage control scheme is proposed for one of the use cases dominated by corrosion, whereby the number of days between full recharges varies depending on the degradation mechanisms the battery experiences. Simulating the new voltage control scheme yields a 25% increase in battery lifetime whilst ensuring no loss of load to the user.

eess.SY↗

arXiv2023

Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data

Accurate battery lifetime prediction is important for preventative maintenance, warranties, and improved cell design and manufacturing. However, manufacturing variability and usage-dependent degradation make life prediction challenging. Here, we investigate new features derived from capacity-voltage data in early life to predict the lifetime of cells cycled under widely varying charge rates, discharge rates, and depths of discharge. Features were extracted from regularly scheduled reference performance tests (i.e., low rate full cycles) during cycling. The early-life features capture a cell's state of health and the rate of change of component-level degradation modes, some of which correlate strongly with cell lifetime. Using a newly generated dataset from 225 nickel-manganese-cobalt/graphite Li-ion cells aged under a wide range of conditions, we demonstrate a lifetime prediction of in-distribution cells with 15.1% mean absolute percentage error using no more than the first 15% of data, for most cells. Further testing using a hierarchical Bayesian regression model shows improved performance on extrapolation, achieving 21.8% mean absolute percentage error for out-of-distribution cells. Our approach highlights the importance of using domain knowledge of lithium-ion battery degradation modes to inform feature engineering. Further, we provide the community with a new publicly available battery aging dataset with cells cycled beyond 80% of their rated capacity.

cs.LG↗

arXiv2023

Electrochemical impedance spectroscopy beyond linearity and stationarity - a critical review

Electrochemical impedance spectroscopy (EIS) is a widely used experimental technique for characterising materials and electrode reactions by observing their frequency-dependent impedance. Classical EIS measurements require the electrochemical process to behave as a linear time-invariant system. However, electrochemical processes do not naturally satisfy this assumption: the relation between voltage and current is inherently nonlinear and evolves over time. Examples include the corrosion of metal substrates and the cycling of Li-ion batteries. As such, classical EIS only offers models linearised at specific operating points. During the last decade, solutions were developed for estimating nonlinear and time-varying impedances, contributing to more general models. In this paper, we review the concept of impedance beyond linearity and stationarity, and detail different methods to estimate this from measured current and voltage data, with emphasis on frequency domain approaches using multisine excitation. In addition to a mathematical discussion, we measure and provide examples demonstrating impedance estimation for a Li-ion battery, beyond linearity and stationarity, both while resting and while charging.

eess.SY↗

arXiv2022

"Knees" in lithium-ion battery aging trajectories

Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in lithium-ion battery aging trajectories. We first review definitions for knees and three classes of "internal state trajectories" (termed snowball, hidden, and threshold trajectories) that can cause a knee. We then discuss six knee "pathways", including lithium plating, electrode saturation, resistance growth, electrolyte and additive depletion, percolation-limited connectivity, and mechanical deformation -- some of which have internal state trajectories with signals that are electrochemically undetectable. We also identify key design and usage sensitivities for knees. Finally, we discuss challenges and opportunities for knee modeling and prediction. Our findings illustrate the complexity and subtlety of lithium-ion battery degradation and can aid both academic and industrial efforts to improve battery lifetime.

physics.app-ph↗

arXiv2020

Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics

This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman model using asymptotic analysis including electrode-average terms. The Bayesian approach allows complex target distributions to be estimated, which enables a global analysis of the parameter space. The analysis focuses on the identification problem (i) locally, under a set of discrete quasi-steady states of charge, and in comparison (ii) globally with a continuous excursion of state of charge. The performance of the methodology was evaluated using synthetic data from multiple numerical simulations under diverse types of current excitation. We show that various diffusivities as well as the transference number may be estimated with small variances in the global case, but with much larger uncertainty in the local estimation case. This also has significant implications for estimation where parameters might vary as a function of state of charge or other latent variables.

eess.SY↗

arXiv2019

A State Observer Design for Simultaneous Estimation of Charge State and Crossover in Self-Discharging Disproportionation Redox Flow Batteries

This paper presents an augmented state observer design for the simultaneous estimation of charge state and crossover flux in disproportionation redox flow batteries, which exhibits exponential estimation error convergence to a bounded residual set. The crossover flux of vanadium through the porous separator is considered as an unknown function of the battery states, model-approximated as the output of a persistently excited linear system. This parametric model and the simple isothermal lumped parameter model of the battery are combined to form an augmented space state representation suitable for the observer design, which is carried out via Lyapunov stability theory including the error-uncertainty involved in the approximation of the crossover flux. The observer gain is calculated by solving a polytopic linear matrix inequality problem via convex optimization. The performance of this design is evaluated with a laboratory flow battery prototype undergoing self-discharge.

eess.SY↗

arXiv2019

An Adaptive Observer Design for Charge-State and Crossover Estimation in Disproportionation Redox Flow Batteries undergoing Self-Discharge

This article considers a model formulation and an adaptive observer design for the simultaneous estimation of the state of charge and crossover flux in disproportionation redox flow batteries. This novel nonaqueous battery chemistry allows a simple isothermal lumped parameter model to be formulated. The transport of vanadium through the porous separator is a key unknown function of battery variables and it is approximated in the space of continuous functions. The state and parameter observer adaptation laws are derived using Lyapunov analysis applied to the estimation error, the stability and convergence of which are proved. Numerical values of observer gains are calculated by solving a polytopic linear matrix inequality and equality problem via convex optimization. The performance of this design is evaluated on a laboratory flow battery prototype, and it is shown that the crossover flux can be considered a linear function of state of charge for this battery configuration during self-discharge.

math.OC↗