Periodic Reporting for period 1 - LoLiPoP IoT (Long Life Power Platforms for Internet of Things)
Período documentado: 2023-06-01 hasta 2024-05-31
The vision of LoLiPoP-IoT is to enable long battery life wireless sensors to be retrofitted in IoT applications, offering life spans >5 years, in some cases full power autonomy.
We develop Energy Harvesting/micro-power management solutions that enable long battery life sensors 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 carbons emission, 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 this can identify bottlenecks resulting in reductions of >10% in production, cycle time and inventory costs. In a smart mobility this can help 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. Total production costs and unplanned downtime costs is~ $50 billion annually. 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 the human needs and avoid unnecessary consumption of energy. Energy/fuel consumption can be reduced by up to 20% and major reductions per year in 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 significantly, 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 above. 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 Act KDT platforms with supporting methodology.
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.
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, optimizing area occupation and reducing the environmental impact.
The laboratory Use Cases at ADI CATALYST and IMEC provide a sandbox for initial test and debug experiments.
We develop Energy Harvesting/micro-power management solutions that enable long battery life sensors 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 carbons emission, 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 this can identify bottlenecks resulting in reductions of >10% in production, cycle time and inventory costs. In a smart mobility this can help 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. Total production costs and unplanned downtime costs is~ $50 billion annually. 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 the human needs and avoid unnecessary consumption of energy. Energy/fuel consumption can be reduced by up to 20% and major reductions per year in 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 significantly, 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 above. 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 Act KDT platforms with supporting methodology.
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.
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, optimizing area occupation and reducing the environmental impact.
The laboratory Use Cases at ADI CATALYST and IMEC provide a sandbox for initial test and debug experiments.
Early stage efforts were spent setting up the project management, info share and reporting infrastructures to manage the project which is outlined in the project handbook (D1.1) Other key documents are the Quality and Risk Management Plan (D1.3) Data Management Plan (D1.4). The website and social media platforms (LinkedIn, X) were also put in place and planning complete for the 1st workshop (which was subsequently presented in M13). A series of GA amendments was necessary to capture some consortium changes and minor deviations in Use Case plans. Most of the effort was in WP2 defining the specifications for the UCs in WP7. These categorized by functionality (D2.1 (asset tracking), D2,2 (condition monitoring) & D2.3 (building comfort/energy efficiency)). Significant effort was also spent on understanding the key tech platforms that will be delivered by the partners (power sources – WP4; WSN modules – WP6; algorithms – WP3) and understanding the metrology and simulation needed of power consumptions and ambient energies from WP5. This included preparation for an in-person workshop in M13, the creation of methodologies and frameworks for defining who does what, detailed walkthroughs of each UC, determining how we mix and match COTS (commercial off the shelf) and partner developed platforms. Other pivotal deliverables include D2.4 (definition of tech platforms) & D6.5 (WSN module specifications).
16 deliverables (mostly from WP1 (project management) & WP2 (UC requirements specifications) and 2 milestones were met (project management infrastructure, First requirements and specifications and first dissemination activities).
We had 4 face to face meetings visiting 2 of the UC sites (UC1 & 10) and on-line consortium calls every 2 weeks
In relation to social sciences, activities carried out led to a R&D path on the simulation of human behaviour within buildings used for offices and civil use. Analyzes were conducted on the basis of the methodologies already adopted for similar purposes, highlighting the methods of interaction between the physical input data and those linked to human behaviour.
16 deliverables (mostly from WP1 (project management) & WP2 (UC requirements specifications) and 2 milestones were met (project management infrastructure, First requirements and specifications and first dissemination activities).
We had 4 face to face meetings visiting 2 of the UC sites (UC1 & 10) and on-line consortium calls every 2 weeks
In relation to social sciences, activities carried out led to a R&D path on the simulation of human behaviour within buildings used for offices and civil use. Analyzes were conducted on the basis of the methodologies already adopted for similar purposes, highlighting the methods of interaction between the physical input data and those linked to human behaviour.
The project proposal has a very detailed table (1.5) with an overview of expected progress beyond state of the art. They can be categorized as (i) POWER SOURCE (PMIC & Discrete) (ii) WSN IoT EDGE DEVICE (iii) SIMULATION TOOL (Harvested energy, WSN module power consumption, over the air tests for WSNs reliability and coexistence studies), (iv) ALGORITHMS (Energy Efficient Asset tracking, Condition monitoring, Building comfort/energy efficiency, Energy efficient distributed learning framework
& (v) INTEGRATION (Self-powered WSN sensors system, Antennas, Demo prototypes).
At this early stage not much has been developed yet, it is mainly planning and integration. However here are 2 early examples:-
1. IUNET-UBO has worked in the optimization of UHF rectifiers for wireless power transfer at micropower levels. Although designed rectifiers are quite canonical, optimizations achieved higher efficiencies than similar configurations in literature (e.g. 36% vs 32%).
2. Signify has investigated, using IFAG sensors, the option of ceiling-mounted CO2 sensors for Indoor Quality control and confirmed effectiveness of such sensors, enabling if preferred/wished the integration such CO2 sensors in the ceiling grid or into ceiling luminaires. Extensive testing has revealed that they record the CO2 with lower latency than wall sensors & the variability between sensor values from ceiling mounted sensors is lower. The results are submitted for publication (Hindawi) and the final version has now been accepted.
Next steps will include studying additional sensing functionalities for Indoor Air Quality (e.g. PM, VOC, humidity) using a multi-sensor module in a comparable Design-of-Experiments.
& (v) INTEGRATION (Self-powered WSN sensors system, Antennas, Demo prototypes).
At this early stage not much has been developed yet, it is mainly planning and integration. However here are 2 early examples:-
1. IUNET-UBO has worked in the optimization of UHF rectifiers for wireless power transfer at micropower levels. Although designed rectifiers are quite canonical, optimizations achieved higher efficiencies than similar configurations in literature (e.g. 36% vs 32%).
2. Signify has investigated, using IFAG sensors, the option of ceiling-mounted CO2 sensors for Indoor Quality control and confirmed effectiveness of such sensors, enabling if preferred/wished the integration such CO2 sensors in the ceiling grid or into ceiling luminaires. Extensive testing has revealed that they record the CO2 with lower latency than wall sensors & the variability between sensor values from ceiling mounted sensors is lower. The results are submitted for publication (Hindawi) and the final version has now been accepted.
Next steps will include studying additional sensing functionalities for Indoor Air Quality (e.g. PM, VOC, humidity) using a multi-sensor module in a comparable Design-of-Experiments.