energies Article Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries Shaheer Ansari 1 , Afida Ayob 1,2, * , Molla Shahadat Hossain Lipu 1,2, * , Aini Hussain 1 and Mohamad Hanif Md Saad 3,4   Citation: Ansari, S.; Ayob, A.; Hossain Lipu, M.S.; Hussain, A.; Saad, M.H.M. Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries. Energies 2021, 14, 7521. https://doi.org/10.3390/en14227521 Academic Editor: Ricardo J. Bessa Received: 3 October 2021 Accepted: 2 November 2021 Published: 11 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; p100855@siswa.ukm.edu.my (S.A.); draini@ukm.edu.my (A.H.) 2 Centre for Automotive Research (CAR), Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia 3 Department of Mechanical and Manufacturing Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; hanifsaad@ukm.edu.my 4 Institute of IR 4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia * Correspondence: afida.ayob@ukm.edu.my (A.A.); lipu@ukm.edu.my (M.S.H.L.) 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) applica- tions. 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. Keywords: lithium-ion battery; remaining useful life; electric vehicles; backpropagation neural network; multi-channel input (MCI) profile 1. Introduction The increased number of fossil-based vehicles has significantly triggered global tem- perature rise, environmental pollution, and health hazards [1]. To address these issues, electric vehicles (EVs) have been extensively exploited among researchers and automobile engineers due to their reliability, simplicity, comfort, and improved efficiency [2]. In addi- tion, EVs offer several potential benefits, such as improved energy storage management, increased usage of renewable power, and lower dependence on fossil-fuel-based energy generations. Moreover, the successful implementation of EV technology is important towards achieving United Nations Sustainable Development Goals (UN SDGs) by 2030 [3]. In this regard, there has been substantial progress in hybrid EVs due to their advanced battery and propulsion systems [4]. The application of advanced battery technology in EVs and hybrid EVs with regard to low or zero emissions permits the automobile industries to develop an advanced battery technology compared to conventional engines based on Energies 2021, 14, 7521. https://doi.org/10.3390/en14227521 https://www.mdpi.com/journal/energies