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Remaining useful life prediction for lithium-ion batteries using time series forecasting models
(2023)
Automobiles are an important part of modern civilization, offering a variety of functions such as passenger and freight transportation, travel and delivery services, and emergency response. The vast majority of vehicles on the road today are fuelled by hydrocarbon fuels, specifically gasoline, and diesel.
With regard to air pollution, global warming, and the depletion of fossil fuel reserves, the use of hydrocarbon fuels to power electric vehicles as a result, greenhouse are released. So, there has been a discernible increase in the production of environmentally friendly, alternative automobiles that are powered by green energy. Electric vehicles typically utilize lithium-ion batteries (LIBs) as a substitute fuel source to produce electricity. Although LIBs are a desirable alternative to conventional hydrocarbon fuels, their longevity is constrained, and they should not be used past the point at which they are on the verge of losing their usefulness. An accurate evaluation of these batteries' health is necessary to assure their safe use and prevent potentially disastrous battery explosions.
Prediction models could help solve the previously mentioned problems. This study's objective is to assess how well various machine learning algorithms can foretell battery health. In order to get this outcome, time series forecasting techniques are used to application metrics, and it was discovered that Long Short-Term Memory (LSTM) models were effective at creating forecasts that could be relied upon. The LSTM model outperformed all other models on various metrics.
Accurate prediction using machine learning models can help boost the sales of EVs and ensure the safe usage of these batteries. This can significantly contribute to reducing air pollution, combating global warming, and preserving fossil fuels for future generations.