Real-Time Prediction of Capacity Fade and Remaining Useful Life of Lithium-Ion Batteries Based on Charge/Discharge Characteristics

Abstract

We propose a robust and reliable method based on deep neural networks to estimate the remaining useful life of lithium-ion batteries in electric vehicles. In general, the degradation of a battery can be predicted by monitoring its internal resistance. However, prediction under battery operation cannot be achieved using conventional methods such as electrochemical impedance spectroscopy. The battery state can be predicted based on the change in the capacity according to the state of health. For the proposed method, a statistical analysis of capacity fade considering the impedance increase according to the degree of deterioration is conducted by applying a deep neural network to diverse data from charge/discharge characteristics. Then, probabilistic predictions based on the capacity fade trends are obtained to improve the prediction accuracy of the remaining useful life using another deep neural network.

Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

“…Moreover, it is the most used and most developed approach, with research based on the use of neural networks and their variants, support vector machine [1][2][3][4][5][6][7][8][9][20][21][22][23][24], probabilistic methods (Bayesian networks, Markov models and their derivatives) [1,4,[31][32][33][34][35][36], stochastic models [21,33,35,[37][38][39][40][41][42][43], state and filtering models (Kalman filter and their variants, particle filter, etc.) [4,15,[43][44][45][46][47][48][49][50][51][52][53][54], regression tools (support vector regression and their variants) [45][46]…”

“…[4,15,[43][44][45][46][47][48][49][50][51][52][53][54], regression tools (support vector regression and their variants) [45][46][47][48][49]54], or combinations of different methods [4]. In addition, the Gaussian process (GP) regression [4,17,[49][50][51][52][53][54] is a commonly used method among regression-based data-driven approaches, etc. A comprehensive review of various data-driven algorithms has been carried out by Nam-Ho et al ( 2017) in [4].…”

A Multiphysics System-to-Cell Framework to Assess the Impact of Operating Conditions of Standalone PV Systems on Lithium-Ion Battery Lifetime

“…Equation (15) correlates the state of charge to the lithium concentration in the anode [23,43]. Where…”

“…However, the development and the implementation of such advanced frameworks are still challenging, particularly in long-term performance analyses at the system level. Numerous studies have been carried out on the aging mechanisms and lifetime prediction of Li-ion batteries to study the feasible solutions for increasing battery lifetime [13][14][15]. Dufo-Lopez et al [16] analyzed different lifetime estimation methods applicable to designing and optimizing autonomous renewable energy systems.…”

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

“…In addition, due to the limitation of the cell voltage and storage capacity of a single LIB cell, high power applications of LIBs such as EVs and grid-tied energy storage systems require hundreds or even thousands of single battery cells [8]. Cell inconsistencies in a LIB pack are a common issue; thus an appropriate BMS is also indispensable for the safe and reliable operation of the LIB pack as well as every single cell of the battery pack [9,10]. A BMS can be designed to serve many functions including but not limited to data acquisition, estimation of the state of charge (SOC) [11,12] and state of health (SOH) [13,14], temperature measurement/estimation [15], cell balancing [16,17], fault detection/diagnosis [18,19] and thermal management [16].…”