Safer Systems, Better Data: How Edge Digitisation Over CAN Improves Battery, Hydrogen and Industrial Applications
In the rapidly evolving landscape of industrial automation and energy storage systems, the need for reliable, real-time data collection and processing has never been more critical, creating exciting opportunities for innovation . As we advance through 2025, edge digitisation combined with Controller Area Network (CAN) bus technology is revolutionising how industries monitor, control, and optimise their most critical systems—from battery management to the hydrogen industry safety and beyond.
Understanding Edge Digitisation in Industrial Applications
Edge digitisation represents a fundamental shift in how industrial systems process and analyse data, which is essential for improving operational efficiency . Rather than sending all sensor information to centralised cloud servers, edge computing brings data processing capabilities directly to where the data is generated—at the source. This approach dramatically reduces latency, improves system reliability, and enhances security by keeping sensitive operational data local.
The industrial edge has become increasingly important as industries seek to empower enterprises on their digitalisation journey while maintaining the robust performance required for critical applications. This localised processing capability is particularly valuable in applications where millisecond response times can mean the difference between safe operation and catastrophic failure.
The Power of CAN Bus Technology
Controller Area Network (CAN) bus protocol has long been the catalyst and backbone of reliable communication in automotive and industrial applications. Originally developed by Bosch for automotive systems, CAN has proven its worth in demanding environments where data integrity and real-time communication are paramount.
CAN bus offers several key advantages for industrial applications, particularly in the manufacturing sector :
Robust Communication: CAN’s differential signaling and error detection mechanisms ensure reliable data transmission even in electrically noisy industrial environments. The protocol can detect and correct transmission errors automatically, maintaining system integrity.
Real-Time Performance: With deterministic message prioritisation, CAN ensures that critical safety messages reach their destination within predictable timeframes, making it ideal for time-sensitive applications.
Multi-Master Architecture: Unlike traditional master-slave protocols, CAN allows any node on the network to initiate communication, creating a more flexible and resilient system architecture.
Cost-Effective Scaling: CAN networks can accommodate multiple sensors and control units on a single two-wire bus, significantly reducing installation costs and complexity compared to point-to-point wiring systems.
Battery Management Revolution
The energy storage industry faces unprecedented challenges as battery systems become larger, more complex, and more critical to operational success, highlighting the need for rapid development in monitoring technologies to build safer systems. Traditional battery monitoring approaches often rely on centralized systems that may miss critical events or fail to respond quickly enough to prevent damage in renewable energy applications .
Edge digitization over CAN is transforming battery management by bringing intelligent monitoring directly to the battery pack level, a focus of researchers in the field . Advanced projects using sensors like cell monitoring units can now process voltage, temperature, and impedance data locally, identifying potential issues before they escalate into safety hazards.
Modern battery management systems benefit from this approach in several ways:
Enhanced Safety: Local processing enables immediate response to dangerous conditions like thermal runaway or cell imbalance, which can lead to catastrophic failures. Instead of waiting for data to travel to a central controller and back, edge-based systems can trigger protective measures within milliseconds.
Improved Accuracy: By processing data at the source, edge systems eliminate the noise and delays associated with long cable runs and multiple signal conversions. This results in more accurate state-of-charge calculations and better overall battery performance.
Predictive Maintenance: Edge computing platforms can run sophisticated algorithms to analyse battery degradation patterns and predict maintenance needs, reducing unexpected failures and extending battery life.
Scalable Architecture: CAN-based systems can easily accommodate additional battery modules or monitoring points without major infrastructure changes, supporting system growth and evolution.
Hydrogen Safety Applications
As the hydrogen economy grows, H2 systems present unique safety challenges due to the gas’ high flammability and tendency to leak through the smallest openings, particularly when used as a fuel . Traditional safety monitoring approaches often rely on centralised gas detection systems that may not provide adequate coverage or response speed for large facilities.
Edge digitisation combined with CAN communication is revolutionising hydrogen safety monitoring by distributing intelligent sensors throughout the facility, including those that monitor hydrogen peroxide levels . These smart sensors can detect hydrogen leaks, monitor environmental conditions, and coordinate emergency responses without relying on a central control system.
The benefits for hydrogen industry applications include:
Distributed Intelligence: Rather than depending on a single point of failure, edge-based hydrogen safety systems distribute processing power across multiple intelligent nodes. Each sensor can make autonomous decisions while coordinating with the broader system.
Faster Response Times: Local processing eliminates communication delays that could prove critical in emergency situations. Edge sensors can trigger ventilation systems, shut off hydrogen fuel supplies, or activate alarms within milliseconds of detecting a leak.
Environmental Adaptation: Edge processors can adapt their sensitivity and response algorithms based on local environmental conditions, reducing false alarms while maintaining safety effectiveness.
Integration Capabilities: CAN-based hydrogen safety systems can easily integrate with existing industrial control systems, fire suppression equipment, and emergency response protocols.
Industrial Process Optimisation
Beyond specific applications like batteries and hydrogen, edge digitisation over CAN is transforming general industrial processes by bringing intelligence closer to the point of operation. This approach enables more responsive control systems, safer systems, better quality monitoring, and improved overall equipment effectiveness, serving as an example for future advancement .
Manufacturing environments benefit from edge computing in numerous ways:
Real-Time Quality Control: Edge processors can analyse product quality data as items move through production lines, enabling immediate corrections that prevent defective products, including fertilizers, from reaching customers.
Predictive Maintenance: By analysing vibration, temperature, and operational data locally, edge systems can identify equipment problems before they cause unplanned downtime.
Energy Optimisation: Intelligent edge nodes can optimise energy consumption based on real-time demand and production schedules, reducing operational costs while maintaining productivity.
Supply Chain Integration: Edge systems can coordinate with supply chain management systems to optimise material flow and reduce waste throughout the production process.
Key Technologies Enabling the Revolution
Several technological advances and ongoing development have made edge digitisation over CAN more practical and powerful than ever before, as the majority of industries adopt this technology.
Advanced Microcontrollers: Modern microcontrollers combine processing power, memory, and communication capabilities in compact, energy-efficient packages that can operate in harsh industrial environments.
Sensor Fusion: Edge processors can combine data from multiple sensor types—temperature, pressure, vibration, chemical—to create comprehensive situational awareness that would be impossible with individual sensors.
Machine Learning at the Edge: Compact machine learning algorithms can now run on edge processors, enabling predictive analytics and pattern recognition without cloud connectivity.
Cybersecurity Integration: Edge systems can implement security measures locally, protecting sensitive operational data while enabling necessary communication with higher-level systems.
Implementation Considerations
Successfully implementing edge digitisation over CAN requires careful consideration of several factors:
Network Architecture: Proper CAN network design ensures reliable communication at various stages while minimising the impact of individual node failures. This includes considerations for bus topology, termination, and message prioritisation.
Data Management: Edge systems must balance local processing capabilities with the need to share information with higher-level systems. This requires careful consideration of data filtering, compression, and transmission strategies.
Security Protocols: Industrial edge systems must implement robust security measures to protect against cyber threats while maintaining the real-time performance required for critical applications.
Scalability Planning: Systems should be designed to accommodate future expansion and technology updates without requiring complete infrastructure replacement.
Environmental Considerations: Industrial edge devices must withstand temperature extremes, vibration, electromagnetic interference, and other challenging environmental conditions.
Future Trends and Opportunities
As we look toward the future, several trends are shaping the development and evolution of edge digitisation in industrial applications:
Artificial Intelligence Integration: The integration of artificial intelligence (AI) with edge computing technology is transforming how edge devices process complex computational tasks, enabling more sophisticated analysis and decision-making at the point of operation.
5G and Advanced Connectivity: Next-generation wireless technologies will enhance the capabilities of edge systems by providing high-speed, low-latency connectivity when needed while maintaining local processing autonomy.
Digital Twin Integration: Edge systems will increasingly serve as the foundation for digital twin implementations, providing real-time data that keeps virtual models synchronised with physical systems.
Sustainability Focus: Edge computing can contribute to sustainability goals by optimising energy usage, reducing waste, and enabling more efficient resource utilisation across industrial operations.
Conclusion
Edge digitisation over CAN bus technology represents a long history of paradigm shifts in how industrial systems collect, process, and act upon critical data. By bringing intelligence closer to the point of operation, these systems deliver improved safety, better performance, and greater reliability across a wide range of applications.
From battery management systems that prevent thermal runaway to hydrogen safety networks that respond to leaks in milliseconds, edge digitisation is enabling safer, more efficient industrial operations. As the technology continues to evolve, organisations that embrace this approach will find themselves better positioned to meet the challenges of an increasingly complex and demanding industrial landscape.
The combination of proven CAN bus reliability with modern edge computing capabilities offers a powerful foundation for the industrial systems of today and tomorrow. Whether you’re designing new systems or upgrading existing infrastructure, edge digitisation over CAN provides a pathway to safer operations, better data quality, and improved overall system performance.
For organisations ready to embrace this technology, the benefits are clear: reduced downtime, improved safety, lower operational costs, and the foundation for continued innovation in an ever-evolving industrial environment. The future of industrial automation is not just connected—it’s intelligently distributed, processing critical decisions to produce a mix of outcomes where they matter most.
