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

Abstract

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations

“…Battery RUL affects the accuracy and performance of EVs. Continuous charging and discharging of the battery cause capacity degradation, which can have unfavorable effects like significant failure, financial loss, and safety concerns (Ansari et al, 2021a(Ansari et al, , 2021bLipu et al, 2021). Therefore, it is essential to calculate the battery RUL in order to ensure accurate, reliable, durable, and safe operation of EV technology.…”

“…Data-driven approaches, on the other hand, rely on previous battery data that includes a variety of parameters, including voltage, current, impedance, capacity, and temperature. Data-driven techniques do not depend on complex mathematical models and they can determine the battery RUL based on previous and present data of the battery (Ansari et al, 2021a(Ansari et al, , 2021b). • State of function (SOF).…”

Transfer Learning-Based Remaining Useful Life Prediction Method for Lithium-Ion Batteries Considering Individual Differences

“…Ren [15] proposed a deep learning approach that combines autoencoders with deep neural networks (DNNs) to extract multidimensional RUL information, enhancing prediction accuracy. Ansari [16] presented an RUL prediction method based on Multi-Channel Input (MCI) configuration and Recurrent Neural Network (RNN) algorithms, including both Single-Channel Input (SCI) and MCI configurations. Compared to the single channel-based method, this method significantly reduces prediction errors.…”

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

“…Artificial intelligence (AI) approaches have the potential to significantly enhance the functionality and performance of BMS in EVs [12]. AI-driven BMS in EVs offers a range of benefits, including improved performance, safety, energy efficiency, and user experience, while also helping to extend the lifespan of the battery.…”