Battery health prediction under generalized conditions using a Gaussian process transition model

@article{Richardson2018BatteryHP,
  title={Battery health prediction under generalized conditions using a Gaussian process transition model},
  author={R. Richardson and Michael A. Osborne and D. Howey},
  journal={arXiv: Applications},
  year={2018}
}
  • R. Richardson, Michael A. Osborne, D. Howey
  • Published 2018
  • Mathematics, Computer Science
  • arXiv: Applications
  • Accurately predicting the future health of batteries is necessary to ensure reliable operation, minimise maintenance costs, and calculate the value of energy storage investments. The complex nature of degradation renders data-driven approaches a promising alternative to mechanistic modelling. This study predicts the changes in battery capacity over time using a Bayesian non-parametric approach based on Gaussian process regression. These changes can be integrated against an arbitrary input… CONTINUE READING

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