Predictive maintenance for battery storage systems: How sensor data enables smart maintenance decisions
Battery energy storage systems (BESS) are key components of connected energy systems. For operators, it is no longer just the installed capacity of a system that matters but also how available, reliable, and economically viable it is in operation. To derive reliable analyses, detect anomalies, or make specific maintenance decisions based on recorded data, operators need dependable sensor-to-cloud data models. That serves as a basis for developing predictive maintenance measures and achieving higher Overall Equipment Effectiveness (OEE).
How companies reach a maintenance decision from an individual measurement
Large-scale battery storage systems—also referred to as Battery Energy Storage Systems (BESS)—have evolved in recent years into active infrastructure components of energy grids and can respond to volatile feed-in, peak loads, or market-based energy schedules. However, the numerous load changes affect the battery cells, modules, and power components of the storage systems. That makes it essential for operators to respond early to signs such as degradation, in other words, the gradual decline in storage capacity, thermal anomalies, or electrical deviations.
The Fraunhofer IVI describes the state variables of battery systems as the basis for evaluating the available performance and energy level of a battery system, and predicting the remaining life span, safety-relevant states, and potential premature system failures. The goal is to plan BESS maintenance not strictly according to fixed time intervals, but to base it on the actual state of the system. That entails structuring and analyzing the sensor data in such a way that reliable information can be derived from it for maintenance purposes.
Why sensor data alone is not enough
Operators continuously record different variables in their systems, including:
- Battery cell voltage
- Input and output current
- Temperatures
- States of charge
- Charge and discharge cycles
- Internal resistances
Taken on their own, however, each of these provides only limited insight. An increased cell temperature may be critical, but it may also be an expected consequence of a specific load profile. The measurements are therefore only relevant for maintenance purposes if they are linked and evaluated in the context of the system structure, operating state, and environmental conditions.
Why sensor-to-cloud data models serve as the basis
Measurement data is linked to operating and system states using sensor-to-cloud data models. They determine how the measurements from the battery management system (BMS) are transmitted via edge gateways to cloud or data platforms, where they are normalized and analyzed.
Each measurement needs to be clearly assigned:
- Which cell or module was measured?
- When and in what operating state did the measurement take place?
- In what unit, measurement quality, and at what sampling rate were the measurements taken?
The key for users is how the recorded data is processed. Without consistent asset hierarchies, timestamps, and units, time series cannot be reliably compared. Anomalies could be incorrectly located, aging trends could go undetected, and machine learning (ML) models could learn patterns that are unstable in operation. Predictive maintenance therefore does not begin with the algorithm, but already with the data model. The Asset Administration Shell (AAS) of Fraunhofer IPA, for example, can be consulted to that end. It creates a standardized, semantically described data structure for industrial assets.
SoH analysis: How companies can correctly assess the operating state
A key diagnostic metric for BESS systems is the state of health (SoH). This metric can be used to describe a battery’s condition, for example, its remaining capacity, state of function, and rate of aging. In contrast, the state of charge (SoC) indicates the current charge level of a battery. The SoH, on the other hand, assesses how sustainably a storage system can be used.
In practice, the difference is significant. A storage system may currently be sufficiently charged, yet still experience a decline in performance due to aging, thermal stress, or increasing internal resistance. SoH models therefore always assess the technical state of the battery. Maintenance priorities can be derived as a result:
- Which modules age faster than expected?
- Which racks should be monitored more closely?
- When does it make sense to adjust the operating limits?
From diagnostics to decision-making through machine learning
Machine learning adds pattern recognition and predictive capability to battery diagnostics. ML models can learn the normal operation of a system and identify anomalies that are difficult to detect using conventional threshold-based logic. Typical examples are:
- Cell voltage drift
- Abnormal temperature gradients
- Increasing internal resistance
- Altered charging behavior under comparable conditions
This entails, among other things, the use of regression models, time-series analyses, or clustering methods. For example, the Fraunhofer EMFT describes ML as a way to identify complex patterns in system operating data. In this context, the institute classifies anomaly detection as part of condition monitoring.
How can companies effectively combine edge, cloud, and OEE?
However, not every analysis should be performed in the cloud. Safety-critical functions, such as monitoring threshold values, safe shutdown in critical states, or plausibility checks, must be performed locally in the BMS or at the edge level. The cloud, on the other hand, is suitable for fleet comparisons, long-term trends, model training, and cross-location optimizations. A robust system architecture strategically combines both levels.
The benefit is apparent in the Overall Equipment Effectiveness (OEE). When applied to BESS, OEE comprises three dimensions: availability, performance, and quality. In other words, the storage system must be ready for use, deliver the expected performance, and provide energy in the required operating state. Predictive maintenance improves these dimensions by reducing unplanned outages, detecting degradation early on, and scheduling maintenance windows strategically.
The importance of battery storage systems for the electronica community
Predictive maintenance in BESS is not just a matter of software. It starts with precise sensor technology, high-performance BMS, secure communication, edge computing components, and scalable data platforms. This is exactly where electronica’s key topics converge: power electronics, embedded systems, sensor technology, connectivity, AI, and energy infrastructure.
Anyone who wants to dive deeper into topics related to battery diagnostics, predictive maintenance, sensor-to-cloud architectures, and data-based OEE optimization, can get extensive information at electronica in technical presentations, panel discussions, and expert forums, such as the Power & Battery Forum or the IIoT Forum, and talk with leading experts in the industry.