Building Data-Centric Edge AI for BMS & Predictive Maintenance
Online Training Workshop: Build, Learn, Deploy
Overview
This workshop is designed to introduce developers to the hardware and software required to build data-centric Edge AI applications for battery management system (BMS) monitoring and predictive maintenance. Participants learn how battery data, such as current, voltage, and temperature, is acquired, processed, and managed deterministically at the device, and how this data is transformed into AI-ready features for on-device inference. The workshop covers the selection and integration of edge hardware (MCUs, MPUs, NPUs, sensors, and accelerators) alongside production-grade software for real-time control, time-series data management, signal processing, and Edge AI frameworks. Through hands-on exercises, developers gain a clear understanding of how data management, analytics, and AI work together to detect anomalies, predict failures, and enable reliable, explainable motor health intelligence directly at the edge.
Workshop Date: Thursday, June 25, 2026
Workshop Time: 11:00 AM–2:00 PM PT (2:00 PM–5:00PM ET)
Workshop Location: Online
Online Tutorial Workshop Syllabus
Objective: Estimate State of Charge (SoC) and State of Health (SoH) in real time on an Edge device. Build data-driven firmware to ingest battery signals, generate cleaned features, apply Edge AI for anomaly detection and lifecycle prediction, and visualize battery performance and health trends over time.
The workshop comprises three 60-minute modules.
Module 1: Data-Driven Firmware Design and Programming
- Sensors for battery monitoring
- Sample signals such as cell voltage, current, temperature, and pack-level telemetry
- Hardware setup
- Embedded device, storage media, and communication interfaces for BMS applications
- Data modeling for edge devices: BMS (SoC & SoH) and predictive maintenance
- Ingest and store sensor samples, features, inference results, events, and system metrics
- Define sampling rates, stream window sizes, and key data structures
- Data cleaning and conditioning with deterministic stream buffering
- Per-channel filtering, normalization, outlier detection, and handling of missing data
- Transactional commits and time-series storage for reliable and safe data management
- Feature engineering for Edge AI
- Extract features such as voltage/current trends, moving averages, deltas, internal resistance indicators, and statistical metrics (mean, variance, peaks)
Module 2: Building an Edge AI Pipeline
- Select AI model algorithm
- Anomaly detection vs. regression/classification for SoC and SoH estimation
- Datasets with variations and disturbances
- Charge/discharge cycles, temperature fluctuations, load variations, aging effects, and degradation patterns
- Train a model with an Edge AI development tool
- Train using normal battery behavior, validate with varying operating and degraded conditions
- Assess model size, latency, and deterministic performance
- Export optimized model to embedded runtime
- Real-time inference and battery analytics pipeline
- Integrate ML inference into feature streams to generate SoC, SoH, and anomaly scores per time window
- Define thresholds, persistence logic, and battery state models (e.g., normal, degraded, critical)
- Simulate operating conditions and observe model response and prediction transitions
Module 3: Edge Device Analytics, Visualization, and Connectivity
- Communicate with ITTIA Data Connect over Ethernet, WiFi, and/or serial interfaces
- Create a custom on-device dashboard with ITTIA Analitica
- Display metrics such as SoC, SoH, voltage/current trends, temperature distribution, anomaly counts, and battery status
- Visualize data via web dashboards, web services (HTTP/REST), Python, and C/C++
- Analyze battery behavior and lifecycle trends
- Frequency, duration, and severity of anomalies
- Long-term degradation patterns and performance changes
- Correlate multi-sensor data
- Relate voltage, current, and temperature interactions
- Identify patterns affecting battery efficiency and health
- Score battery health and performance
- Evaluate SoH trends, anomaly severity, and degradation indicators
- Generate battery condition and alert levels
- Use anomaly data for diagnostics and predictive maintenance
- Detect early signs of battery degradation or failure
- Enable proactive maintenance and optimization
- Validate system performance
- Ensure bounded latency per processing window
- Maintain efficient storage usage and long-term reliability
Workshop Sample Application
During this hands-on workshop, you will be trained to design, build, and deploy a real-world, data-driven AI application, gaining practical skills you can immediately apply to embedded and edge systems.
Anomaly Detection and Battery Intelligence in BMS
During the workshop, participants take on the role of an embedded engineer responsible for ensuring safe, reliable, and efficient operation of a battery system in real-world conditions. Using MCU-based platforms, they capture live battery signals, such as cell voltage, current, temperature, and pack-level telemetry, directly at the edge, without reliance on cloud connectivity. The team trains an on-device AI model that learns what “normal” battery behavior looks like across charge, discharge, and varying operating conditions.
As the battery operates, subtle abnormalities begin to emerge, unexpected voltage fluctuations, temperature variations, current irregularities, and early signs of degradation that are difficult to detect using traditional threshold-based methods. The AI detects these anomalies in real time, while ITTIA DB Lite AI persistently stores and organizes both sensor data and inference results directly on the device.
By combining AI with deterministic data management, raw battery signals are transformed into actionable insights. Anomaly frequency, duration, and severity are analyzed alongside key battery metrics to estimate State of Charge (SoC) and State of Health (SoH). On-device analytics provide continuous tracking of voltage trends, temperature behavior, load conditions, and anomaly counts.
By the end of the session, participants build a complete Edge AI solution that demonstrates how data-centric design and on-device intelligence enable accurate SoC/SoH estimation, early fault detection, and predictive maintenance for battery systems, delivering reliable, explainable, and real-time battery intelligence directly at the edge.
Course Details
Workshop Cost: USD $250 per attendee
The workshop fee includes:
- Comprehensive lecture materials
Workstation: This is a hands-on workshop. You must have an STM32H573I-DK device, a USB-C cable, and a Windows PC:
*Unlock 25% OFF your STM32H573I order when you register!
Edge Data AI Certification for Embedded Devices
Workshop attendees will receive ITTIA DB Platform certification. This certification validates hands-on expertise in using the ITTIA DB Platform for deterministic data management, on-device data processing, and Edge AI–ready data handling. It demonstrates and validates the ability to design reliable, power-fail-safe, and AI-enabled embedded systems using proven data architectures, reducing development risk, accelerating time to production, and enabling high-quality, scalable solutions.
Past Workshop Participants
ITTIA has delivered highly sought-after, hands-on workshops that attract engineers and innovation leaders from across multiple sectors. Our sessions are trusted by companies building real-world, data- and AI-driven embedded systems, and have been attended by teams from leading organizations committed to advancing intelligent edge technologies, including:
Abbott – Acuity Brands – Advances Energy – Applied materials – Avery Biomedical Devices, Inc. – Badger Meter – Cardios – Cat Wranglers
Cognosos Inc. – Continental AG – CPI – Declarative Futures – EDS – Emtech – Everspin – EXFO – Honeywell – Kellogg Northwestern
L3Harris – Magna International – Mayo clinic – Megavolt Labs – Milwaukee Tool – Mold Masters – Nidec Global Appliances – OTTO Engineering
Philips – Resideo – Schneider Electric – Siemens – Southland Sensing Ltd. – TITOMA – TTI Floor Care – Wind River – Zetron/Codan
Audience
These immersive, hands-on workshops are built for embedded architects and engineers who are shaping the next generation of data-driven, AI-enabled edge systems and want more than theory, they want results. Through practical, expert-led sessions, participants learn how to select the right device platform, design deterministic real-time firmware, and implement robust on-device data management that elevates data to a first-class system asset. The program goes further by showing how to prepare and integrate optimized AI pipelines directly into embedded workflows, while addressing cybersecurity, functional safety, diagnostics, and long-term lifecycle management from day one. By the end of the workshop, engineers leave with the confidence, skills, and real-world experience needed to build intelligent, trustworthy, and future-proof embedded systems ready for production.
These workshops are highly technical, intended for professional engineers and are not suitable for the general public without relevant qualifications.
