Speaker
Description
Electric batteries are often connected in parallel to ensure a wider power supply range to external electrical loads. Their condition is routinely monitored through the current measured when the batteries supply power. When the condition is adequate, the current is balanced throughout the system, with each battery contributing equally to the electrical load.
To ensure that monitoring focuses on the relative contributions of each battery rather than the total electrical load, we propose a statistical process monitoring (SPM) approach based on compositional data. We demonstrate that the proposed approach can maintain a controlled false alarm rate across varying total electrical loads. Its practical applicability is illustrated through a case study in the SPM of parallel-connected nickel-cadmium batteries installed on a modern high-speed train fleet to power auxiliary onboard systems.
Funding Details
The research activity of C. Capezza, A. Lepore, and E. Rossi was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004). The research activity of B. Palumbo was carried out within the MOST - Sustainable Mobility National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1033.17-06-2022, CN00000023). This work reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
| Classification | Mainly application |
|---|---|
| Keywords | Predictive Maintenance, Multivariate Quality Control, Performance Degradation, Current Imbalance Detection |