Data analysis of battery storage systems

Merlinda Andoni, Wenshuo Tang, Valentin Robu, David Flynn

Research output: Contribution to conferencePaperpeer-review


Battery energy storage systems can assist Distribution Network Operators (DNOs) to face the challenges raised by the substantial increase in distributed renewable generation, such as rooftop PV installations and wind generators. A key problem is that these resources are intermittent and often “invisible” to the DNO. If not monitored and addressed, the aggregate size of small embedded generation resources can cause thermal wearing of distribution assets and voltage excursions, especially in sunny/windy periods with insufficient local demand. Several developers of energy storage solutions, with technologies such as Lithium-ion batteries, offer their products to address peak shaving, frequency and voltage control needs within the network. Once deployed within the energy network, as batteries experience capacity degradation with usage, these companies will need to incorporate methods from Prognostics and Health Management (PHM) in order to better manage their products. The main deliverable of this project is validation of data analysis, based on relevance vector machine (RVM), to predict the remaining useful life of Lithium-ion batteries. The accuracy of the predictions for different batteries are all within 10 cycles (within 8.5% relative error). These results reaffirm our view, that as technology as a service trends become increasingly relevant within a Distribution System Operator Model, energy networks will increasingly adopt asset management solutions that utilise PHM methods.
Original languageEnglish
Publication statusPublished - 12 Jun 2017
EventCIRED 2017 - SECC, Glasgow, United Kingdom
Duration: 12 Jun 201715 Jun 2017


ConferenceCIRED 2017
Country/TerritoryUnited Kingdom
Internet address


  • Data analysis
  • energy system
  • Prognostics

ASJC Scopus subject areas

  • General Energy
  • General Engineering


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