Abstract
Abstract Electric vehicles transform the automotive industry by replacing
traditional vehicles powered by fossil fuels with less polluting and efficient vehicles.
They are powered by rechargeable Li-ion batteries. While there are drawbacks
associated with Li-ion battery technology, it’s important to note that these challenges
can be addressed or mitigated effectively. A battery management system ensures the
tracking of all functions performed by the battery. An advanced battery management
system should accurately estimate the battery’s state of health and state of charge.
This paper aims to develop machine learning models for the estimation of the state of
health and state of charge and to implement cell balancing in a battery management
system. A dataset of battery charging and discharging profiles was used to train
various machine learning models for estimation. These methods are computationally
less complex than conventional methods. In addition, a cell balancing algorithm is
implemented to control the charging and discharging of individual cells in a battery
pack and balance the state of charge of each cell. The machine learning solution is
created using several machine learning models, including Gradient Boosting, Random
Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models

Gradient Boosting and Random Forest provide good MSE and R2 score. The use of
machine learning algorithms for the assessment of state of health and charge estimation
combined with the design of an efficient control algorithm for active cell balancing
offers significant advancements in the battery management system to optimise the
performance, reliability, and useful life of Li-ion batteries. The paper also presents
a case study utilising a combination of deep learning-based SoC, SoH estimation
algorithms in a simulated data set. A well-designed control algorithm for active
cell balancing presents a holistic and effective approach to optimise Li-ion batteries' performance,
reliability, and lifespan.
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