STM32H5 + ITTIA DB Lite
Building Intelligent Battery Management at the Edge
Battery-management systems continuously generate valuable operational data. Cell voltage, pack voltage, current, temperature, State-of-Charge, balancing activity, charge behavior, and operating cycles collectively describe how a battery is performing and how its condition is changing.
The challenge is that battery health cannot always be understood from the latest measurement alone. By combining STM32H5 with the ITTIA DB Lite product family, developers can create a local data foundation that continuously manages battery information, preserves historical context, prepares AI features, and supports intelligent decision-making directly on the MCU.
Battery Intelligence Requires History
A battery cell may have a normal voltage at the present moment while its historical behavior indicates an emerging problem. Temperature differences between cells may gradually increase. Voltage variation may become more pronounced during charging. A cell may respond differently under load compared with previous operating cycles. Each individual measurement may still remain within an acceptable range.
The important information comes from understanding how those measurements are changing over time. This makes battery management an ideal example of the relationship between embedded data management and Edge AI.
ITTIA DB Lite for Battery Data Management
The ITTIA DB Lite product family is designed for resource-constrained microcontroller environments and can support MCUs from a variety of semiconductor vendors, provided the target device offers adequate memory, storage, and processing resources for the intended application.
This flexibility allows developers to apply the same embedded data-management and Edge AI architecture across multiple MCU platforms, including the broad STM32 family. For the purpose of this blog, however, we will focus specifically on STM32H5 as a representative platform for demonstrating how ITTIA DB Lite and ITTIA DB Lite AI can enable structured data management, historical processing, feature engineering, and intelligent Edge AI applications directly on the microcontroller.
An STM32H5-based BMS can continuously collect measurements from battery-monitoring electronics and sensors. ITTIA DB Lite provides structured transactional and time-series data management directly on the MCU, allowing the system to organize information such as cell voltage, current, temperature, State-of-Charge, balancing activity, operating states, and battery events. Instead of keeping only the latest values, the application can preserve useful historical windows. The basic processing path becomes:
Battery Sensors → STM32H5 → ITTIA DB Lite → Historical Battery Data
This history enables the application to analyze not only the current battery state but also the behavior that led to it.
ITTIA DB Lite AI for Battery Feature Engineering
AI models often require derived information rather than isolated raw measurements. A battery-health application may need to examine voltage variation between cells, temperature gradients, current trends, charge-rate behavior, changes in State-of-Charge, differences between cycles, or statistical characteristics calculated over time. ITTIA DB Lite AI can use the historical data managed locally to support this feature-engineering process and prepare data for inference.
The resulting Edge AI pipeline becomes:
Battery Measurements → ITTIA DB Lite → Historical Windows → ITTIA DB Lite AI → Battery Features → AI Model → Battery Health Insight
The database therefore becomes an active part of the AI pipeline rather than simply a location where data is stored.
Detecting Abnormal Battery Behavior Earlier
Consider a battery pack in which one group of cells begins behaving slightly differently from the others. Temperature variation begins increasing during charging. Voltage recovery after high-load events starts changing, and the difference between cells gradually becomes more pronounced.
A traditional monitoring system may not react because none of the individual measurements has crossed a predefined safety threshold. An Edge AI model can examine the combination of historical features and identify that the battery's behavior is moving away from its normal pattern. This may allow the system to generate an early health indicator or anomaly before the condition becomes severe.
The application can preserve the decision path:
Cell Measurements → Historical Data → Features → AI Model → Inference → Battery Health Result
This provides both intelligence and traceability.
Making Battery AI More Explainable
Explainability becomes particularly important when AI influences battery-health, maintenance, or operational decisions. Knowing that an AI model generated an anomaly score is useful, but engineers also need to understand the data behind that result.
The combination of ITTIA DB Lite and ITTIA DB Lite AI can maintain the relationship between historical measurements, calculated features, the AI model, inference results, confidence values, and resulting actions. An engineer can therefore investigate the event and understand what changes in voltage, temperature, current, or other measurements contributed to the AI decision.
This turns AI inference from an isolated output into part of a traceable data process.
Local Battery Intelligence
Battery monitoring may need to operate regardless of network or cloud availability. With STM32H5 and the ITTIA DB Lite product family, data acquisition, historical management, feature engineering, inference, and local actions can remain on the embedded device.
This reduces dependence on external connectivity and allows immediate decisions where the battery is being monitored. Instead of continuously transmitting every raw measurement, the system can retain detailed data locally and communicate selected information such as health indicators, anomalies, summaries, or historical windows associated with important events.
A Practical STM32H5 Battery Management Demonstration
A battery-management demonstration can continuously ingest cell voltage, pack current, temperature, State-of-Charge, and other operational measurements into ITTIA DB Lite.
ITTIA DB Lite maintains the time-series history, while ITTIA DB Lite AI calculates features that describe voltage consistency, thermal behavior, current trends, and changes over time.
The demonstration can then introduce a simulated abnormal condition such as increasing temperature variation, voltage imbalance, an unusual load pattern, or another change in battery behavior. The AI model detects the deviation and generates an anomaly or battery-health result. The audience can follow the complete workflow:
Battery Data → Historical Context → Feature Engineering → AI Inference → Battery Health Decision
This makes the value of embedded data infrastructure tangible.
From Battery Measurements to Battery Intelligence
STM32H5 provides the processing environment for intelligent battery-monitoring applications. ITTIA DB Lite provides the structured historical data foundation, while ITTIA DB Lite AI connects that history with feature engineering and AI inference. Together, they allow the BMS to move beyond simply monitoring the latest voltage, current, and temperature readings.
The system gains the ability to understand historical behavior, identify patterns, recognize abnormalities, preserve AI context, and make intelligent decisions directly at the edge.
STM32H5 + ITTIA DB Lite turn battery history into actionable Edge AI intelligence.
