Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles

Abstract

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey

“…This observation suggests that the integration of spatial features, though beneficial for certain applications, may not universally enhance predictive performance for temperature forecasting in dry areas [46]. Such insights are crucial for the development of tailored AI solutions in renewable energy sectors, particularly in optimizing the deployment and operation of solar and wind energy systems [47][48][49].…”

Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations

“…They tried to find the optimum solution that maximized profit measured in present-day value. Regarding the negative effects of EV charging stations on the grid, Geske et al [11] introduced the charging simulation of EVs in the power system distribution network of Magdeburg. They created different scenarios for charging operations and based the departure and arrival of vehicles on real-time data.…”

“…Switches create AC at a higher frequency from the input to transfer power. The Sd switch pair connected in parallel to the resonant circuit is used to alleviate voltage fluctuations [11,12,13]. The current controller uses the input voltage Vx(t) via the voltage sensor, the resonant current ip(t) via the current sensor, and the secondary circuit output voltage Vo(t) to determine the correct switching frequency.…”

Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study

“…In addition to all these methods, our research team discussed enhancements to the LSTM (long short-term memory) and Bi-LSTM (Bidirectional Long Short-Term Memory) methods. Evaluation metrics such as the Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared were applied in our previous publication [13].…”