Publications

A full set of publications may be found at Google Scholar. As far as possible papers are made available for download as pre-prints (primarily through ArXiv and/or as open access through ORA or directly from the publisher, as required by our funders.

PhD theses may be accessed through the following ORA links (some are embargoed due to commercial sensitivity):

  1. Taeho Jung - Application of concentrated solution theory and core potential to ion exchange media, 2023.
  2. Gosia Wojtala - Degradation and thermal performance of Li-ion batteries: implications for electric vehicles, 2023.
  3. Antti Aitio - Bayesian methods for battery state of health estimation, 2022.
  4. Sam Greenbank - Data-driven battery state of health diagnostics and prognostics, 2022.
  5. Trishna Raj - The impact of path dependent degradation on the lifetime of Lithium-Ion batteries, 2021.
  6. Jorn Reniers - Degradation-aware optimal control of grid-connected lithium-ion batteries, 2020.
  7. Pietro Romanazzi - Fast and accurate hot-spot estimation in electrical machines, 2017.
  8. Damien Frost - Battery management systems with active loading and decentralised control, 2017.
  9. Christoph Birkl - Diagnosis and prognosis of degradation in lithium-ion batteries, 2017.
  10. Adrien Bizeray - State and parameter estimation of physics-based lithium-ion battery models, 2016.
  11. Robert Richardson - Impedance-based battery temperature monitoring, 2016.
  12. David Howey - Thermal design of air-cooled axial flux permanent magnet machines, 2010.
Recent article highlights

  1. Sulzer, V., Mohtat, P., et al., “The challenge and opportunity of battery lifetime prediction from field data”, Joule, 2021. Publisher copy - please contact us if you want the pdf sent to you. Author accepted manuscript.

  2. S. Greenbank and D.A. Howey, “Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life”. IEEE Transactions on Industrial Informatics, in press, 2021. Publisher copy or Pre-print

  3. Reniers, J.M., Mulder, G. and Howey, D. A., “Unlocking extra value from grid batteries using advanced models”, Journal of Power Sources, 487:229355, 2021. Publisher copy or Pre-print

  4. Aitio, A. and Howey, D.A. “Combining non-parametric and parametric models for stable and computationally efficient battery health estimation”, Proceedings of the ASME 2020 Dynamic Systems and Control Conference. Publisher copy or Pre-print

  5. Raj, T., Wang, A.A., Monroe, C.W., and Howey, D.A., “Investigation of Path‐Dependent Degradation in Lithium‐Ion Batteries”, Batteries and Supercaps, 3(12), 1377-1385, 2020. Open access publisher copy

  6. Aitio, A., Marquis, S.G., Ascencio, P., and Howey, D.A., “Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics”, IFAC-PapersOnLine, 53(2), 12497-12504, 2020. Publisher copy or Pre-print

  7. Richardson, R.R., Osborne, M.A., and Howey D.A. “Battery health prediction under generalized conditions using a Gaussian process transition model”, Journal of Energy Storage vol. 23, 2019. Open access publisher copy

  8. Reniers, J.M., Mulder, G., and Howey, D.A. “Review and performance comparison of mechanical-chemical degradation models for lithium-ion batteries”. Journal of The Electrochemical Society, vol. 166, num. 14, pp. A3189-A3200, 2019. Open access publisher copy

  9. Bizeray, A.M., Kim, J-H.,Duncan, S.R., and Howey D.A., “Identifiability and Parameter Estimation of the Single Particle Lithium-Ion Battery Model”, IEEE Transactions on Control Systems Technology, vol. 27, num. 5, 2019. Publisher copy or Pre-print