Periodic Reporting for period 2 - LoLiPoP IoT (Long Life Power Platforms for Internet of Things)
Período documentado: 2024-06-01 hasta 2025-05-31
The vision of LoLiPoP-IoT is to enable long battery life wireless sensors to be retrofitted in IoT applications, offering life spans of >5 years, in some cases full power autonomy.
We develop Energy Harvesting/micro-power management solutions that enable long battery life sensors to be retrofitted on, in or near equipment and infrastructure. Wireless sensors enable us to collect data for anomaly detection, efficiency and performance monitoring. We have unprecedented opportunities to exploit such data, bringing billions of € in savings and disruptive benefits for industry and society (reduced carbon emissions, increased renewable integration), making the world a safer and better-connected place.
LoLiPoP IoT targets challenges in 3 FUNCTIONALITIES for multiple application domains:
A) ASSET TRACKING: Optimise flow, management and throughput of assets. In a factory identify bottlenecks resulting in reductions of >10% in production, cycle time and inventory costs. In smart mobility this can monitor assets to avoid misplacement, minimize downtime as well as make savings in transportation cycle times and energy/carbon footprint.
B) CONDITION MONITORING (predictive maintenance): monitoring a parameter of condition in machinery (vibration, temperature etc.), to identify a significant change indicative of a developing fault. For industry 4.0 maintenance overheads can be reduced from 40% to <15% with additional improvements in cycle time & downtime. Predictive maintenance saves ~ 8% to 12% over preventative maintenance.
C) ENERGY EFFICIENCY & COMFORT OPTIMISATION: Key sensory data can predict, understand and adjust the energy load (e.g. equipment, buildings) and energy needs. Optimize the work environment for human needs and avoid unnecessary consumption of energy. Energy/fuel consumption can be reduced by up to 20% and major reductions per battery replacement costs achieved. This will deliver carbon footprint savings due to less energy usage as well as less batteries going into landfill. Meanwhile, workplace satisfaction and wellbeing can be increased with a more productive workforce, lower absence and care-related costs.
Development is driven by 10 use case (UC) requirements from IoT applications in the functionality domains listed. The use cases orthogonally target the applications industry 4.0 smart mobility and energy efficient buildings.
LoLiPoP-IoT creates a series of disruptive long battery life enabling Chips JU platforms with supporting methodology.
Multi-source Energy Harvesting power management solutions to convert light, vibrations, magnetic and heat to electricity and minimise power drain.
Emerging technology-based energy harvester and storage components system-level optimised.
Ultra-low power components & algorithms that dramatically reduce the power consumption of WSN modules.
Innovative Architectures for wireless data collection that minimise battery power drain.
Simulation Models to optimise selection, sizing, and system integration of components.
WSN edge device algorithms to enable condition monitoring.
WSN edge device algorithms to enable asset tracking.
Power-efficient Algorithms to optimise building comfort levels and energy consumption.
Eco-friendly multiband antennas to support multi-purpose transponders, optimising area occupation and reducing the environmental impact.
We develop Energy Harvesting/micro-power management solutions that enable long battery life sensors to be retrofitted on, in or near equipment and infrastructure. Wireless sensors enable us to collect data for anomaly detection, efficiency and performance monitoring. We have unprecedented opportunities to exploit such data, bringing billions of € in savings and disruptive benefits for industry and society (reduced carbon emissions, increased renewable integration), making the world a safer and better-connected place.
LoLiPoP IoT targets challenges in 3 FUNCTIONALITIES for multiple application domains:
A) ASSET TRACKING: Optimise flow, management and throughput of assets. In a factory identify bottlenecks resulting in reductions of >10% in production, cycle time and inventory costs. In smart mobility this can monitor assets to avoid misplacement, minimize downtime as well as make savings in transportation cycle times and energy/carbon footprint.
B) CONDITION MONITORING (predictive maintenance): monitoring a parameter of condition in machinery (vibration, temperature etc.), to identify a significant change indicative of a developing fault. For industry 4.0 maintenance overheads can be reduced from 40% to <15% with additional improvements in cycle time & downtime. Predictive maintenance saves ~ 8% to 12% over preventative maintenance.
C) ENERGY EFFICIENCY & COMFORT OPTIMISATION: Key sensory data can predict, understand and adjust the energy load (e.g. equipment, buildings) and energy needs. Optimize the work environment for human needs and avoid unnecessary consumption of energy. Energy/fuel consumption can be reduced by up to 20% and major reductions per battery replacement costs achieved. This will deliver carbon footprint savings due to less energy usage as well as less batteries going into landfill. Meanwhile, workplace satisfaction and wellbeing can be increased with a more productive workforce, lower absence and care-related costs.
Development is driven by 10 use case (UC) requirements from IoT applications in the functionality domains listed. The use cases orthogonally target the applications industry 4.0 smart mobility and energy efficient buildings.
LoLiPoP-IoT creates a series of disruptive long battery life enabling Chips JU platforms with supporting methodology.
Multi-source Energy Harvesting power management solutions to convert light, vibrations, magnetic and heat to electricity and minimise power drain.
Emerging technology-based energy harvester and storage components system-level optimised.
Ultra-low power components & algorithms that dramatically reduce the power consumption of WSN modules.
Innovative Architectures for wireless data collection that minimise battery power drain.
Simulation Models to optimise selection, sizing, and system integration of components.
WSN edge device algorithms to enable condition monitoring.
WSN edge device algorithms to enable asset tracking.
Power-efficient Algorithms to optimise building comfort levels and energy consumption.
Eco-friendly multiband antennas to support multi-purpose transponders, optimising area occupation and reducing the environmental impact.
Significant progress was made in developing technology platforms (power sources – WP4 and algorithms – WP3) & integrating these into WSN modules (WP6) for the Use Cases (WP7) based on the specifications derived in year 1 (WP2), supported by the simulation models from WP5. 2 in-person industry workshops were held in Istanbul and Finland. In all, 16 deliverables and 2 milestones were met (Energy requirements defined; Components & Modules implemented and Energy Harvesting prototype and first Test reports available).
P1 had focused on defining the specifications for the UCs. So far, it seems that significant battery lifetime extension is possible through careful and collaborative co-design of WSN modules. In most UCs (all except UC3 & 10 which has less of a focus on battery life) significant real life ambient energies seem available. The value-add potential of the WSN sensors is evident, what remains to be seen is the extent of the savings/improvements possible. For P2 we are building evidence via prototypes, installation in UC applications and simulation models that battery life extension/full power autonomy is possible.
P1 had focused on defining the specifications for the UCs. So far, it seems that significant battery lifetime extension is possible through careful and collaborative co-design of WSN modules. In most UCs (all except UC3 & 10 which has less of a focus on battery life) significant real life ambient energies seem available. The value-add potential of the WSN sensors is evident, what remains to be seen is the extent of the savings/improvements possible. For P2 we are building evidence via prototypes, installation in UC applications and simulation models that battery life extension/full power autonomy is possible.
LoLiPoP-IoT develops a series of disruptive long battery life enabling Chips JU platforms with supporting methodology that exploits the opportunity and addresses the challenges.
• Multi-source Energy Harvesting power management solutions to convert light, vibrations, magnetic and heat to electricity and minimize power drain.
• Emerging technology-based energy harvester and storage components system level optimized.
• Ultra-low power components & algorithms that dramatically reduce the power consumption of WSN modules.
• Innovative Architectures for wireless data collection that minimize battery power drain.
• Simulation Models to optimise selection, sizing, and system integration of components.
• WSN edge device algorithms to enable condition monitoring for predictive/preventative maintenance.
• WSN edge device algorithms to enable asset tracking.
• Power-efficient Algorithms to optimise building comfort levels and energy consumption.
• Eco-friendly multiband antennas to support multi-purpose transponders, while optimizing area occupation and reducing the environmental impact of distributed electronics.
Significant progress in UC1s has been visible in P2, all the development has been also interlinked with the refinement of the reference architecture specified in WP2. The technological platforms (either COTS or partner developed) were further specified, developed/selected and integrated into prototypes (corresponding initial evaluation and reporting started in M21-M24). Significant attention has been paid to mapping of UC visions, specific requirements, and integration of the expected results of algorithms (WP3), power sources (WP4), simulation models (WP5) and WSN IoT edge devices (WP6). This included refinement of the P1 walkthroughs for each UC to understand the functionality and power consumption need and energy harvesting potential.
• Multi-source Energy Harvesting power management solutions to convert light, vibrations, magnetic and heat to electricity and minimize power drain.
• Emerging technology-based energy harvester and storage components system level optimized.
• Ultra-low power components & algorithms that dramatically reduce the power consumption of WSN modules.
• Innovative Architectures for wireless data collection that minimize battery power drain.
• Simulation Models to optimise selection, sizing, and system integration of components.
• WSN edge device algorithms to enable condition monitoring for predictive/preventative maintenance.
• WSN edge device algorithms to enable asset tracking.
• Power-efficient Algorithms to optimise building comfort levels and energy consumption.
• Eco-friendly multiband antennas to support multi-purpose transponders, while optimizing area occupation and reducing the environmental impact of distributed electronics.
Significant progress in UC1s has been visible in P2, all the development has been also interlinked with the refinement of the reference architecture specified in WP2. The technological platforms (either COTS or partner developed) were further specified, developed/selected and integrated into prototypes (corresponding initial evaluation and reporting started in M21-M24). Significant attention has been paid to mapping of UC visions, specific requirements, and integration of the expected results of algorithms (WP3), power sources (WP4), simulation models (WP5) and WSN IoT edge devices (WP6). This included refinement of the P1 walkthroughs for each UC to understand the functionality and power consumption need and energy harvesting potential.