Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Abstract

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

“…However, when the lithium-ion battery is fully charged, lithium ions start to move towards the cathode, resulting in the release of stored energy. During continuous charging and discharging, battery degradation takes place [33]. One of the limiting factors in battery lifetime is attributed to battery degradation, which needs to be addressed efficiently [34].…”

Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network

“…Thanks to the advantages of high energy density, long storage life, high safety, and no pollution, lithium-ion batteries are widely applied in the field of electric vehicles (Yuan et al, 2015;Wang et al, 2021). However, with the use of electric vehicles starting, irreversible electrochemical reactions occur in the onboard lithium-ion batteries, which will increase their internal resistance and decrease their maximum available capacity, leading to the attenuation of their remaining useful life (RUL) and a serious reduction of the driving distances of electric vehicles Ansari et al, 2022). Besides, it is well known that the discharging capacity of lithium-ion batteries is poor in a OPEN ACCESS EDITED BY low-temperature environment.…”

“…It is suitable for the real vehicle operating environment. Common data-driven methods include the artificial neural network (ANN), (Ansari et al, 2021), support vector regression (SVR) (Xue et al, 2020), relevance vector regression (RVR) (Chen et al, 2021), and gaussian process regression (GPR) (Li et al, 2019). The transformer model, a deep learning neural network, has succeeded in the field of natural language processing and has steadily moved to the field of time-series prediction owing to its unique structure, long-distance modeling capability, and outstanding parallel computing capacity (Tian et al, 2022;Vallés-Pérez et al, 2022).…”

Remaining Useful Life Prediction of Aeroengines Based on Multi-Head Attention Mechanism

“…Based on the abovementioned theory, an attempt can be made to construct a multidimensional HI using multi-dimensional features to characterize the degradation process of equipment. Ansari et al [13] constructed a multi-channel artificial neural network (ANN) for extracting multiple features of batteries, and their proposed model showed strong versatility. Peng et al [14] proposed a prediction model based on the idea of classification and parallel processing.…”