Abstract
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.
Highlights
A battery management system this helps to improve the robustness and performance of the battery
The behavior of the battery can be understood by the State of Charge and State of Health or the Performance Characteristics are very useful is modern day technologies earlier many conventional methods were used for estimating these responses but later after the improvisation in the computational power of machines like computers old technologies like Adaptive learning methods - machine learning and ANN (Artificial Neural Nets) started evolving that offer far better performance than the conventional approaches of estimating SoC and SoH
From the vast research being conducted on these self- adjusting systems for the Battery health estimation some of basic attributes like voltage current and ambient temperature of the battery pack is being used for the estimation of SoC and for the estimation of health of the battery pack additional parameter like SoC, charge cycles, voltage, current, temperature and along with additional parameters are taken into consideration when dealing with these methodologies
Summary
A battery management system this helps to improve the robustness and performance of the battery. The BMS acquires information about the electrical and ambient parameters of the battery and protects it from electrical damages and helps to enhances its functioning and battery life. A representation of various ML techniques and their classification are mentioned below. From the vast research being conducted on these self- adjusting systems for the Battery health estimation some of basic attributes like voltage current and ambient temperature of the battery pack is being used for the estimation of SoC and for the estimation of health of the battery pack additional parameter like SoC, charge cycles, voltage, current, temperature and along with additional parameters are taken into consideration when dealing with these methodologies.
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