Kapacitásprognózisok

Kapacitásprognózisok refer to the quantitative prediction of the remaining capacity of electrochemical storage devices, primarily lithium‑ion batteries, over time or cycling. The term combines the Hungarian words for “capacity” (kapacitás) and “forecast” (prognózis). These predictions are crucial for grid‑storage management, electric‑vehicle range estimation, and maintenance scheduling in commercial and industrial applications.

A kapacitásprognózis is typically expressed as a function of calendar time, depth‑of‑discharge, temperature, and charge/discharge rates. Classical approaches employ semi‑empirical Arrhenius‑based models that incorporate diffusion‑controlled, solid‐state resistance, and electrolyte‑related loss mechanisms. More advanced methods use data‑driven techniques, such as regression trees, Gaussian processes, and neural networks, trained on laboratory cycling data to capture non‑linear ageing pathways.

For practical deployment, a prediction model must be coupled with real‑time state‑of‑charge estimation. The capacity forecast is updated in periodic cycles, and if the predicted capacity falls below a configurable threshold, the system triggers a maintenance action or a battery replacement cycle. The reliability of a kapacitásprognózis depends on the fidelity of the underlying ageing model and the accuracy of the input variables, such as cell temperature logging and accurate current measurement.

In the energy sector, capacity prognoses support long‑term reserve sizing, reduce the risk of unexpected outages, and improve economic models for battery procurement. Regulations in the European Union and other electricity markets increasingly mandate capacity validation for grid‑connected storage units, raising the profile of kapacitásprognózis as a standard operational tool.