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dc.contributor.author Muhammad Hassnain, Naimat Alvi
dc.contributor.author Rehmat, Muhammad Junaid
dc.contributor.author Rahim, Adeena
dc.contributor.author Azam, Aitzaz
dc.contributor.author Malik, Usman Mehmood
dc.date.accessioned 2025-02-13T07:26:26Z
dc.date.available 2025-02-13T07:26:26Z
dc.date.issued 2023-06
dc.identifier.other PTC-457
dc.identifier.uri http://10.250.8.41:8080/xmlui/handle/123456789/49845
dc.description.abstract A Battery Health Monitoring System (BHMS) is a technology used to track the health and performance of batteries, particularly in applications where reliable and consistent power is critical. The system is designed to collect and analyze data from the battery to determine its condition, state of charge, and remaining useful life (RUL). This project aims to develop BHMS including Arduino, ESP8266, and sensors like voltage, current, and temperature. The system collects real-time data from the battery, analyzes it using machine learning algorithms, and predicts the remaining useful life (RUL) of the battery. The system displays the battery's health and RUL on a dashboard using different colors to indicate the battery's condition. The BHMS provides early warning of potential battery failures, allowing for proactive maintenance or replacement before a failure occurs. This project is useful in applications where reliable and consistent power is critical, such as in remote areas, renewable energy systems, and EVs (electric vehicles). By using a BHMS with a dashboard, one can monitor the battery's health and RUL in real-time, optimize battery usage, prevent failures, and reduce the total cost of ownership. A Battery Health Monitoring System (BHMS) is a sophisticated and intelligent system that monitors the health and efficiency of batteries. This facilitates the companies and users, and they can know in advance what state their battery is in and take decisions on these basis. BHMS will be determining the battery's health based on 7 parameters that are Battery capacity, Voltage, Current, Temperature, Charging voltage, Charging current, Instant of time. The value of these parameters shall be measured through sensors and transferred to a program that uses a deep learning model through which it shall be predicting what is the battery’s SOH (State of Health) and RUL (Remaining Useful Life in Cycles). These values will then be displayed on the Blynk IOT cloud dashboard for proper visualization using a Wi-Fi module. en_US
dc.language.iso en en_US
dc.publisher MCS en_US
dc.title SUNERGY and BHMS en_US
dc.type Project Report en_US


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