Software / AI

Battery Performance & Predictive Maintenance Analytics Enables bank monitoring to cell level root cause detection

Improves degradation characterization from field data

Compared to the traditional BMS, battery analytic solutions can store historical data as well as compare data between racks and banks (racks connected to the same power conversion system). This enables the detection of banks that are underperforming in the asset. In the following example an underperforming bank was identified (figure 2) and the underlying root cause identified as cell imbalance due to a limiting cell with lower capacity (figure 1. The recovery of 10.4% available energy could be enabled if the single imbalanced rack were repaired, possibly by replacing the weakest module.

Underperforming bank identified, limited by one single cell

Cell imbalance due to capacity dispersion found as root cause

The open circuit voltage OCV) curve of a battery contains valuable information about the health of the cell and its two electrodes. While the ideal condition to obtain this curve at is from a low current full charge or discharge, this might not be possible for batteries in operation. A method to reconstruct an open circuit voltage curve from partial low-dynamic operational periods has now been demonstrated. This further enables deeper analysis of the asset through for example incremental capacity, evaluating different degradation modes.

Open circuit voltage curves reconstructed from field data

Tracking of degradation modes possible

Figure 1. Cell voltage dispersion during operation

Figure 2. Comparison of discharged energy between banks

Reconstructed qOCV from field data

Source: The true cost of BESS anomalies - PowerUp;Study: Reconstructing Battery OCV Curves from Field Data - ACCURE

Incremental capacity analysis to study degradation modes

Extracted from the PDF; charts and images may read imperfectly.