Reliably estimating the capacity of household systems to store the excess electricity generated by photovoltaics

The use of photovoltaics (PVs) to generate household electricity is on the rise, as a growing number of individuals worldwide are now choosing to power their home using solar panels. To store the excess energy generated by solar panels on sunny days and re-use it at night and on cloudy days, one requires additional energy technologies known as home storage systems.

Home storage systems are essentially high-capacity batteries that can store additional energy produced by solar panels, discharging it when the panels are unable to produce energy due to a lack of sunlight. Ultimately, these systems allow households to gain greater independence from the electricity grid, generating and sourcing more of their own energy.

While home storage systems have become increasingly widespread, there are still very few methods to estimate the extent to which their capacity fades over time. Reliably estimating the capacity of household energy storage systems could be highly advantageous, as it could inform the future deployment of effective residential PV systems.

Researchers at RWTH Aachen University, JARA-Energy and ACCURE Battery Intelligence GmbH recently introduced a scalable capacity estimation technique that could be used to reliably assess the capacity of residential storage systems.

Their proposed method, introduced in a paper published in Nature Energy, has so far been used to assess the capacity of 21 privately operated home storage systems in Germany that are based on lithium-ion batteries.

“About 10 years ago, home storage systems were a brand-new product,” Jan Figgener, lead author from RWTH Aachen University and Senior Battery Expert at ACCURE Battery Intelligence, told Tech Xplore.

“To investigate their operation and lifetime, we have measured 21 systems in private households over eight years. Our capacity estimation method tailored to home storage operation could not have been presented timelier, as the recent batteries regulation of the European Union (EU) requires manufacturers to provide such information to customers.”

The method proposed by Figgener and his colleagues estimates the capacity of home storage systems in three key steps. Firstly, it entails determining when a storage system is at full capacity (i.e., can store no more energy) and when it is empty (i.e., not storing any energy). Subsequently, it requires calculating how much capacity is between these two states.

“To detect the full and empty states, we identify relaxation processes within specific voltage ranges,” explained Figgener. “The capacity can then be estimated while applying coulomb counting with offset current correction. From a diagnostics perspective, home storage systems are interesting, as full cycles occur regularly from spring to fall—this is not the case for many other applications such as electric vehicles.”

As part of their recent study, Figgener and his colleagues used their method to estimate the capacity of 21 home storage systems in Germany. Their analyses revealed that on average, these systems lost approximately 2–3% of their capacity per year.

“Most systems reach their given warranty due to the implementation of capacity reserves, which is a good sign for industry given these systems were from the first product generation,” said Figgener.

“We could validate our method through regularly conducted field capacity tests and hope it can contribute to the development of diagnostic methods required by the European Union to establish customer transparency.”

The most notable contribution of the recent work by this research group is that it introduces a promising method that manufacturers and solar energy companies could use to estimate the capacity of their energy storage systems.

In addition, Figgener and his colleagues compiled an extensive dataset containing information about home storage systems spanning across 106 years, which could be used to conduct additional studies focusing on home storage systems or to train computational models.

“After having identified the capacity loss, we will soon show what underlying degradation modes are the reason behind it,” added Figgener.

“Furthermore, we are developing new methods to derive state of health estimates, for example, based on voltage relaxation phases or internal resistance estimates. We hope that other researchers will use the published dataset comprising 14 billion datapoints from 106 system years to improve the research area of field data analytics.”

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