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PaceControls: A Model Based Design Approach for HVACR Equipment Control
Technology Category
- Analytics & Modeling - Digital Twin / Simulation
- Networks & Connectivity - ZigBee
Applicable Industries
- Equipment & Machinery
- Telecommunications
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Digital Twin
- Virtual Reality
Services
- System Integration
- Testing & Certification
The Challenge
PaceControls, a leading technology developer and manufacturer of IoT solutions for the HVACR industry, faced a significant challenge in controlling HVACR equipment. The HVACR systems come in various configurations and sizes, ranging from single compressor/single fan to multi compressor/multi fan units. The technology developed by PaceControls had to be flexible enough to accommodate 10 major HVACR configurations, each with over 200 individual requirements. The challenges included managing requirements and algorithmic complexity, ease of installation and use, accurate estimation of savings, and more. The technology also needed to support Wi-Fi, ZigBee, and 4G LTE communications, and over the air (OTA) firmware update capability.
About The Customer
PaceControls is a leading technology developer and manufacturer of cloud-based Internet of Things (IoT) solutions for the Heating, Ventilation, Air Conditioning & Refrigeration (HVACR) industry. They have developed a third generation product, an Android-based, 4G LTE/Wi-Fi/ZigBee wireless energy and smart grid product suite, named PACE+PACE Cloud. The PaceControls technology, residing between the thermostat and the HVACR equipment, accepts the thermostat control signal and other signals to produce optimized control signals that minimize energy usage of the HVACR equipment. In large commercial installations, networks of Packaged Roof top Units (RTU) provide the heating and cooling. PACE Nodes are installed on each RTU and communicate with one another over a ZigBee network.
The Solution
PaceControls relied on solidThinking Embed (sTE) to implement the Model Based Design (MBD) process to develop its energy saving algorithms. The MBD process consists of simulation and Hardware in the Loop (HIL) testing, and Embed fully supports all steps in the process. During the simulation step, the system requirements are represented by design models. Dynamic models of the HVACR systems are created followed by the design, analysis, and modeling of the energy and demand optimization control algorithms. The combined “controls + plant” model goes through an iterative “analysis + redesign + simulation” process until its performance lies within the acceptable range for each of the design models. During the HIL testing step, Embed automatically generates C code for the controller model. The code is then compiled with Embed support libraries as an Android application where it executes on the PACE Node utilizing a cloud-connected Qualcomm Snapdragon microcontroller.
Operational Impact
Quantitative Benefit
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