Utilization of IoT in Industrial Truck Battery Monitoring
Marek, Pavel (2019)
2019
Electrical Engineering
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2019-05-22
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201904301450
https://urn.fi/URN:NBN:fi:tty-201904301450
Tiivistelmä
An industrial battery is one of the most problematic components of electric industrial trucks. Aging of the battery, improper usage, and wrong maintenance lead to decreased ability to store energy in the battery. As a result, the truck's operation becomes less productive and reliable. Also, the capital loss is significant, if the battery must be replaced prematurely. Battery monitoring systems are used to monitor battery performance and to estimate State of Charge (SoC) and State of Health (SoH). However, precise estimation of SoC and SoH, as well as real-time remote monitoring, remains challenging.
This master's thesis proposes a concept of the IoT battery monitoring system that enables real-time battery condition tracking and also promises improvements in SoC and SoH estimations. The improvements can be achieved without a need for more expensive battery monitoring device due to the utilization of the computational power of the cloud. The thesis focuses on the design and development of battery monitoring device that is built based on the requirements of IoT battery monitoring system concept.
The outcome of this thesis is a fully functional and compact battery monitoring device/design for industrial batteries that is suitable to operate in the proposed IoT concept. The usability of the device has been tested in a real-life environment. The continuous automated operation, data collection for possible further data analysis, and essential detection of harmful conditions are presented among the results.
This master's thesis proposes a concept of the IoT battery monitoring system that enables real-time battery condition tracking and also promises improvements in SoC and SoH estimations. The improvements can be achieved without a need for more expensive battery monitoring device due to the utilization of the computational power of the cloud. The thesis focuses on the design and development of battery monitoring device that is built based on the requirements of IoT battery monitoring system concept.
The outcome of this thesis is a fully functional and compact battery monitoring device/design for industrial batteries that is suitable to operate in the proposed IoT concept. The usability of the device has been tested in a real-life environment. The continuous automated operation, data collection for possible further data analysis, and essential detection of harmful conditions are presented among the results.