State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Abstract

Summary 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 paper, 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.

Connecting battery technologies for electric vehicles from battery materials to management

“…However, Shu et al. ( Shu et al., 2021 ) were optimistic about the machine-learning-based SoH prediction methods due to their simplicity and accuracy and considered them as game-changers for future transportation electrification. Ren et al.…”

Battery health evaluation using a short random segment of constant current charging

“…Recently, with the development of machine learning techniques and the availability of a large amount of high-quality battery data, various data-driven methods ( Li et al., 2019 ; Ng et al., 2020 ; Shu et al., 2021 ) have been proposed for battery health prognostics. According to the input types, these methods can be roughly divided into two categories: feature-based methods and sequence-based methods.…”

Trajectory Similarity Matching and Remaining Useful Life Prediction Based on Dynamic Time Warping

“…erefore, the model-based prediction methods are generally only applicable to the prediction of the RUL of component-level equipment. In contrast, data-driven methods only need historical data to learn and predict equipment status, which do not require much domain knowledge [11], and draw a growing number of attentions from researchers. At present, frequently used data-driven methods include "Support Vector Machine" [12,13], "Hidden Markov Model" [14], "Relevant Vector Machine" [15], "Gaussian process regression" [16], ensemble prediction technique [17,18], "Data Mining" [19], and different types of "Deep Neural Network" [20,21].…”