A Battery Voltage Level Monitoring System for Telecommunication Towers

This research article was published by Engineering, Technology & Applied Science Research, Vol. 11, No. 6, 2021Voltage fluctuations in batteries form a major challenge the telecommunication towers face. These fluctuations mostly occur due to poor management and the lack of a battery voltage level monitoring system. The current paper presents a battery voltage-level monitoring system to be used in telecommunication towers. The proposed solution is incorporated with a centralized mobile application dashboard for accessing the live data of the installed battery, integrated with voltage-level, current, temperature, fire, and gas sensors. An Arduino Uno microcontroller board is used to process and analyze the collected data from the sensors. The Global Service Message (GSM) module is used to monitor and store data to the cloud. Users are alerted in the case of low voltage, fire, and increase in harmful gases in the tower through Short Message Service (SMS). The experiment was conducted at Ngorongoro and Manyara telecommunication towers. The developed system can be used in accessing battery information remotely while allowing real-time continuous monitoring of battery usage. The proposed battery voltage-level monitoring system contributes to the elimination of battery hazards in towers. Therefore, the proposed battery voltage level monitoring system can be adopted by telecommunication tower engineers for the reduction of voltage fluctuation risks

Online diagnosis of state of health for lithium-ion batteries based on short-term charging profiles

In this study, a machine learning method is proposed for online diagnosis of battery state of health. A prediction model for future voltage profiles is established based on the extreme learning machine algorithm with the short-term charging data. A fixed size least squares-based support vector machine with a mixed kernel function is employed to learn the dependency of state of health on feature variables generated from the charging voltage profile without preprocessing data. The simulated annealing method is employed to search and optimize the key parameters of the fixed size least squares support vector machine and the mixed kernel function. By this manner, the proposed algorithm requires only partial random and discontinuous charging data, enabling practical online diagnosis of state of health. The model training and experimental validation are conducted with different kernel functions, and the influence of voltage range and noise are also investigated. The results indicate that the proposed method can not only maintain the state of health estimation error within 2%, but also improve robustness and reliability

A novel adaptive state of charge estimation method of full life cycling lithium-ion batteries based on the multiple parameter optimization.

The state of charge (SoC) estimation is the safety management basis of the packing lithium-ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state-space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is less than 0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs

Real time battery voltage level monitoring system for telecommunication towers a case study: Habari Node public limited company Arusha, Tanzania

A Project Report Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Embedded and Mobile System of the Nelson Mandela African Institution of Science and TechnologyVoltage fluctuations in batteries during consumption are amongst the challenges facing telecommunication towers. Due to these fluctuations, many injuries that cause deaths and environmental poisoning have been reported. These fluctuations and injuries mainly occur due to poor management and lack of battery voltage-level monitoring systems after installation. This paper proposes a battery voltage-level monitoring system to be used in telecommunication towers. The proposed solution is incorporated with a centralized mobile application dashboard that allows access to the live data of the installed battery due to integration with components for sensing the battery’s voltage, current, and temperature levels, as well as fire, and gas contents. An Arduino Uno microcontroller board was used in the processing and analysis of the data collected from the sensors. The global service message module (GSM) internet connectivity was used to store and monitor data in the cloud. The user was then alerted about low voltage, detected fire, and increased levels of harmful gases in the tower through a short message service (SMS). The experiment was conducted at Ngorongoro and Manyara telecommunication towers and it revealed that the developed battery voltage-level monitoring system could access battery information remotely while allowing users to continually monitor the battery usage in telecommunication towers in real time. The unique value of this study is the proposed battery voltage-level monitoring system that contributes to the elimination of battery hazards in telecommunication towers. The proposed battery voltage-level monitoring system can be adopted by telecommunication towers engineers to reduce voltage fluctuation risks like injuries, environmental degradation, and deaths