Machine Learning Applied to Estimate the Battery SoH by using Various Algorithms in Python
Ashutosh Deshpande
, Jitendra Patil , Sohan Patil , Ganesh Lohar
Python, Machine Learning, Artificial Intelligence, Algorithm, Battery, SOH.
Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this the conception of SOH is defined, and the state-of the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction. The growing interest and recent breakthroughs in artificial intelligence and machine learning (ML) have actively contributed to an increase in research and development of new methods to estimate the state of health of battery by using various algorithms. This paper provides a survey of battery state estimation methods based on ML algorithms accuracy such as, Logistic Regression (LR), Linear Discriminant Analysis (LDA), KNeighbors Classifier (KNN), Decision Tree Classifier (CART), GaussianNB (NB) with help of Python. Programming in Python with the help of SKlearn & Pandas.
"Machine Learning Applied to Estimate the Battery SoH by using Various Algorithms in Python ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 12, page no.1169 - 1175, December-2022, Available :https://ijsdr.org/papers/IJSDR2212190.pdf
Volume 7
Issue 12,
December-2022
Pages : 1169 - 1175
Paper Reg. ID: IJSDR_203240
Published Paper Id: IJSDR2212190
Downloads: 000347280
Research Area: Engineering
Country: Pune, Maharashtra , India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
