Personalized Air-Conditioning in Electric Vehicles Using Sensor Fusion and Model Predictive Control

Metzmacher, Henning; Wölki, Daniel; Schmidt, Carolin; Treeck, Christoph van

doi:10.1007/978-3-030-00819-2_9

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Metzmacher et al described an innovative approach to realize personalized air conditioning in vehicles. They combined contactless thermal comfort measurements and simulated real-time data [21]. Thermal comfort was provided by placing infrared heating panels and individual fans in the immediate environment of a thermal manikin.…”

Section: Personalized Comfort Zones In Passenger Cabinsmentioning

confidence: 99%

Effects of personal control for thermal comfort in long-distance trains

Maier

Zierke

Hoermann

et al. 2021

Energy and Buildings

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Section: Personalized Comfort Zones In Passenger Cabinsmentioning

confidence: 99%

Effects of personal control for thermal comfort in long-distance trains

Maier

Zierke

Hoermann

et al. 2021

Energy and Buildings

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“…Preliminary studies using the system have been conducted using two local actuators. 57,58 It was shown that the system is able to compensate cold and warm ambient conditions and increase the subject’s thermal comfort within a defined temperature range. Since only two actuators were tested, the system ability to compensate for cold ambient temperatures was limited.…”

Section: Discussionmentioning

confidence: 99%

Modular personalized climatization testing infrastructure with smartphone-based user feedback

Metzmacher

Syndicus

Warthmann

et al. 2022

Building Services Engineering Research and Technology

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Heating and ventilation in buildings and vehicles are responsible for over a third of global final energy usage and the resulting emissions. Local climatization can help to save energy while at the same time enable more individualized and adapted micro-climates around people. For the domain of local comfort, integrative test environments for sensors, actuators, and control software are scarce, and oftentimes tailored to a specific set of components. Here, a server-based modular testing infrastructure which allows integration and evaluation of sensors, actuators, and control strategies is presented. Currently, the system is able to integrate, monitor, and log data of thermal imaging, motion sensing, environmental sensor such as temperature or air velocity and to forward signals to actuators such as fans, infrared- or contact-heaters. The generation of control signals is model-based and relies on user feedback provided via the system’s smartphone app. Lastly, learning algorithms can be trained and compared during user studies. Practical Application: Although developed in a laboratory based research context, the proposed system is based on open standards and protocols. It therefore can be applied by practitioners, developers, and manufacturers in order to test stand-alone components as well as ensembles of sensors and actuators for personalized climatization in an integrative and replicable manner.

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