An Extensive Study on Machine Learning based Battery Health Estimation

This manuscript is a comparative study on various machine learning Regression methods like Decision Tree and Random Forest and SVM and other improvised methods along with unsupervised methods like Reinforcement learning, ANN methods like DNN are also discussed along with advanced methods like GRU, CNN, LSTM for estimating the battery health in order to estimate its life which is used in the modern-day technology of Battery Management System. The evolution of the present day BMS bought a great opportunity to study more about adaptive learning systems as it provides greater efficiency and tunes itself basing on environmental changes for battery health estimation studying on various methods on the subsets of artificial intelligence can be helpful to build more accurate correlation between the input and output. Adaptive learning even having a self-adjusting feature the computational limitations and the data being used is also important in producing correct result with a promising accuracy, so multiple algorithms, architectures and models are studied for better understanding in order to come to conclusions for selecting the apt model for satisfying results. Compared to other conventional methods Artificial Intelligence and their subsets learn from the error and adopt which outperforms other models in accuracy