Search for Author, Title, Keyword
Prediction of remaining useful life for lithium-ion battery with multiple health indicators
Chun Su 1,2
More details
Hide details
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Hunan Provincial Key Lab of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Publication date: 2021-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(1):176-183
  • Four types of health indicators(HIs) are built with the battery operating data.
  • GRNN is applied to estimate the battery’s remaining capacity with the HIs.
  • Based on the predicted capacity value, the battery’s RUL is estimated with NAR.
Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.
Chang Y, Fang H, Zhang Y. A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery. Applied Energy 2017; 206: 1564–1578,
Chen L, Xu L, Zhou Y. Novel approach for lithium-ion battery on-Line remaining useful life prediction based on permutation entropy. Energies 2018; 11(4): 820.
Donoho D L, Johnstone I M. Threshold selection for wavelet shrinkage of noisy data. Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1994; A24–A25,
Donoho D L, Johnstone I M. Adapting to unknown smoothness via wavelet shrinkage. Fundamental Papers in Wavelet Theory. Princeton University Press 2014: 833–857,
Feng J, Kvam P, Tang Y. Remaining useful lifetime prediction based on the damage-marker bivariate degradation model: A case study on lithium-ion batteries used in electric vehicles, Engineering Failure Analysis 2016; 70: 323–342,
Hu X S, Li S, Peng H. A comparative study of equivalent circuit models for Li-ion batteries. Journal of Power Sources 2012; 198:359-367,
Hu X, Li S E, Jia Z, et al. Enhanced sample entropy-based health management of Li-ion battery for electrified vehicles. Energy 2014; 64: 953–960.
Ibrahim M, Jemei S, Wimmer G, et al. Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles. Electric Power System Research 2016; 136: 262–269,
Kim I S. A technique for estimating the state of health of lithium batteries though a dual-sliding-mode observer. IEEE Transactions on Power Electronics 2009; 25(4): 1013–1022,
Kim J, Lee S, Cho B H. Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction. IEEE Transactions on Power Electronics 2011; 27(1):436–451,
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22 (1): 63–72,
Liu D, Luo Y, Liu J, et al. Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm. Neural Computing and Applications 2014; 25: 557–572,
Long B, Xian W, Jiang L, Liu Z. An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectronics Reliability 2013; 53(6): 821–831,
Lyu C, Lai Q, Ge T, et al. A lead-acid battery’s remaining useful life prediction by using electrochemical model in the particle filtering framework. Energy 2017; 120: 975–984,
Patil M A, Tagade P, Hariharan K S, et al. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Applied Energy 2015; 159: 285–297,
Prasad G K, Rahn C D. Model based identification of aging parameters in lithium ion batteries. Journal of Power Sources 2013; 232: 79–85,
Richman J S, Moorman J R. Physiological time-series analysis, using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology 2000; 278(6): H2039–H2049,
Saha B, Goebel K, Christophersen J. Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control 2009; 31: 293–308,
Saha B, Poll S, Goebel K, et al. An integrated approach to battery health monitoring using Bayesian regression and state estimation. Proceedings of IEEE Autotestcon, 2007; 646–653,
Sahaand B, Goebel K. Battery Data Set, NASA ames prognostics data repository. NASA Ames Research Center, 2007.
Sbarufatti C, Corbetta M, Giglio M, et al. Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks. Journal of Power Sources 2017; 344: 128–140,
Song Y, Liu D, Yang C, et al, Data-driven hybrid remaining useful life estimation approach for spacecraft lithium-ion battery. Microelectronics Reliability 2017; 75: 142–153,
Su C, Chen H J. A review on prognostics approaches for remaining useful life of lithium-ion battery. IOP Conference Series: Earth and Environmental Science 2017; 93(1): 012040,
Valencia D, Orejuela D, Salazar J, et al. Comparison analysis between rigrsure, sqtwolog, heursure and minimaxi techniques using hard and soft thresholding methods. Proceedings of XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA) 2016; 1–5,
Waag W, Kbitz S, Sauer D U. Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Applied Energy 2013; 102: 885–897.
Wei J, Dong G, Chen Z. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression. IEEE Transactions on Industrial Electronics 2017; 65(7): 5634–5643,
Weng C, Cui Y, Sun J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression. Journal of Power Sources 2013; 235(4): 36–44,
Weng C, Sun J, Peng H. Model parametrization and adaptation based on the invariance of support vectors with applications to battery state of health monitoring. IEEE Transactions on Vehicular Technology 2015; 64(9): 3908–3917,
Widodoa A, Shimb M C, Caesarendra W, et al. Intelligent prognostics for battery health monitoring based on sample entropy. Expert Systems with Applications 2011; 38(9): 11763–11769,
Wu J, Zhang C, Chen Z. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Applied Energy 2016; 173: 134–140,
Xia Q, Wang Z, Ren Y, Sun B, et al. A reliability design method for a lithium-ion battery pack considering the thermal disequilibrium in electric vehicles. Journal of Power Sources 2018; 386:10–20,
Xia Q, Yang D, Wang Z, Ren Y, et al. Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles. Renewable and Sustainable Energy Reviews 2020; 131: 109993,
Xing Y, Ma E W M, Tsui K L, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability 2013; 53(6): 811–820,
Yan S, Ma B, Zheng C. Health index extracting methodology for degradation modelling and prognosis of mechanical transmissions. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (1): 137–144,
Yu J, Yang J, Tang D, Dai J. Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2019; 21 (1): 81–89,
Zhang C L, He Y G, Yuan L F, et al. Capacity prognostics of lithium-ion batteries using EMD denoising and multiple kernel RVM. IEEE Access 2017; 5: 12061–12070,
Zheng X, Fang H. An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction, Reliability Engineering & System Safety 2015; 144: 74–82,
Zhou J, He Z, Gao M, et al. Battery state of health estimation using the generalized regression neural network. Proceedings of 8th International Congress on Image and Signal Processing (CISP), 2015; 1396–1400.
Zhou Y, Huang M. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model. Microelectronics Reliability 2016; 65: 265–273,
Review on the Selection of Health Indicator for Lithium Ion Batteries
Wenlu Zhou, Qiang Lu, Yanping Zheng
Lithium Ion Battery Health Prediction via Variable Mode Decomposition and Deep Learning Network With Self-Attention Mechanism
Yang Ge, Fusheng Zhang, Yong Ren
Frontiers in Energy Research
A novel cuckoo search particle filtering strategy for the remaining useful life prediction of the lithium‐ion batteries in hybrid electric vehicle
Ren Pu, Shunli Wang, Xianpei Chen, Junhan Huang, Mingfang He, Wen Cao
International Journal of Energy Research
Useful energy prediction model of a Lithium-ion cell operating on various duty cycles
Damian Burzyński
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Remaining useful life prediction with insufficient degradation data based on deep learning approach
Yi Lyu, Yijie Jiang, Qichen Zhang, Ci Chen
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Lifetime estimation of tantalum capacitor for mobile applications using empirical and experimental techniques: a DOE approach
Cherry Bhargava, Pardeep Sharma, Ketan Kotecha
International Journal of Quality & Reliability Management
Characterization of the state of health of a complex system at the end of use
Christian Wandji, Helmi Rejeb, Peggy Zwolinski
Procedia CIRP
A tool wear condition monitoring approach for end milling based on numerical simulation
Qinsong Zhu, Weifang Sun, Yuqing Zhou, Chen Gao
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Green Sustainable Process for Chemical and Environmental Engineering and Science
Abu Numan-Al-Mobin, Karen Ly, MD Anjum, Hyeong Na
Construction of Health Indicators for Performance Evaluation of Lithium-Ion Battery: A Review
Ran Xia, Chun Su
2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
Perspective study on charge time measurement of long-term stored lithium-ion batteries used in electric-powered aircraft assessed and modelled by specific growth model with diffusion process backup
David Vališ, Jiří Hlinka, Marie Forbelská, Petr Procházka, Radoslav Cipín, Rostislav Koštial, Zdeněk Vintr
Journal of Energy Storage
DAE-Transformer-based Remaining Useful Life Prediction for Lithium-Ion Batteries in Energy Storages
Bowen Huang, Zihao Zeng, Yamin Zhou, Jiang Liu, Qian Zheng, Luting Wang, Zihao Feng, Shukai Sun, Zheng Pan
2023 3rd International Conference on New Energy and Power Engineering (ICNEPE)
Similarity-based residual life prediction method based on dynamic time scale and local similarity search
Meng Yao Gu, Zhi Xi Dai, Hai Teng Wu, Xin Sheng Xu
Journal of the Brazilian Society of Mechanical Sciences and Engineering
A Lithium-ion Battery Remaining Useful Life Estimation Method Based on Nonlinear Autoregressive Fusion Model with Exogenous Variables
Hailin Feng, Ningjuan Li
2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
Journals System - logo
Scroll to top