Internet of Everything (IoE) - Input Based Research Framework: Machine Learning Model for Battery Module Longevity Optimization and Failure Prediction
Abstract
Battery storage plays a vital role in smoothing out the fluctuations in energy demand and generation from distributed renewable sources. Batteries are susceptible to various factors such as a change in temperature, charging cycles, etc. Power storage batteries are expensive, therefore various measures should be taken to make sure it is working in the optimal conditions, these batteries also fail during operations thus affecting the power demands. The research will investigate various machine learning approaches to optimize battery operations in domestic applications (Modules Longevity and failure predictions). Machine learning can help predict battery failure and optimize battery life by using reinforced learning, it is possible to make optimal battery charging and discharging decisions. It involves two methods: supervised learning, which trains a model with previously known input and output data so it can predict future outputs, while unsupervised learning, which finds hidden patterns and intrinsic structures within the input and output data (Temperature, Charging cycles, Voltage and Current). The research will involve extensive simulations and building machine learning models. The machine learning models are trained to capture a battery’s state of health and to predict its remaining lifetime. These two concepts help to know when to recharge its battery as well as when to schedule battery module replacements. The outcome of the research will be reflected in the application of a smart power storage system for ideal self battery monitoring and optimization.
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