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Found 13 papers, 2 papers with code
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.
Looping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity.
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.
Learning battery model parameter dynamics from data with recursive Gaussian process regression
This opens up new opportunities to understand battery ageing in real applications.
Bayesian hierarchical modelling for battery lifetime early prediction
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).
Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature
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.
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.
Predicting battery end of life from solar off-grid system field data using machine learning
Hundreds of millions of people lack access to electricity.
Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis
Here, a piecewise-linear approach to battery health forecasting was compared to a Gaussian process regression tool and found to perform equally well.
Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life
Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols.
Unlocking Extra Value from Grid Batteries Using Advanced Models
Lithium-ion batteries are increasingly being deployed in liberalised electricity systems, where their use is driven by economic optimisation in a specific market context.
Battery health prediction under generalized conditions using a Gaussian process transition model
Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments.
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs.
