Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.
Fast charging of lithium–ion batteries is essential for accelerating a widespread use of electric vehicles; however, its adoption significantly increases battery thermal stress and the risk of thermal runaway, particularly in aged cells. This study proposes a simulation-trained d…
Range estimation for electric vehicles (EVs) is critical for intelligent transportation systems since it directly affects charging planning, route optimization, driver confidence, energy management, battery utilization, and driver decision-making processes. However, current studi…
Metallic particle contaminants have been shown to have a detrimental effect on the reliability, performance and capacity of lithium-ion battery cells. In addition, they pose a significant safety risk. Typical contaminants, such as iron (Fe), copper (Cu) and aluminium (Al), often…
Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high c…
This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation o…
The accurate prediction of lithium-ion battery state of health (SOH) can extend battery life, enhance device safety, and ensure sustained reliability in critical applications. Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel meth…