Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

He, Wei; Williard, Nicholas; Osterman, Michael; Pecht, Michael

doi:10.1016/j.jpowsour.2011.08.040

Cited by 798 publications

(352 citation statements)

References 25 publications

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“…Simulations indicate that the regression models using discharged voltage and internal resistance as aging parameters can more accurately build a state of health profile than those using cycle numbers. He et al [36] proposed a double-index lithium battery degradation model and used the Dempster-Shafer theory (DST) to initialize the model parameters and the Bayesian Monte Carlo (BMC) method to update the model parameters, which are used to predict the battery RUL. Hu et al [37] put forward a nonlinear kernel regression model of lithium battery degradation, obtained degradation parameters through the K-nearest neighbor, and used PSO to optimize the weight of the K-nearest neighbor regression model.…”

Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

158

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Lithium-ion batteries are the primary power source in electric vehicles, and the prognosis of their remaining useful life is vital for ensuring the safety, stability, and long lifetime of electric vehicles. Accurately establishing a mechanism model of a vehicle lithium-ion battery involves a complex electrochemical process. Remaining useful life (RUL) prognostics based on data-driven methods has become a focus of research. Current research on data-driven methodologies is summarized in this paper. By analyzing the problems of vehicle lithium-ion batteries in practical applications, the problems that need to be solved in the future are identified.

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Section: Rul Prognostics Methodologies Based On Artificial Intelligencementioning

confidence: 99%

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Guan

2016

Applied Sciences

158

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“…However, this approach still relies on the impedance measurements, which are hard to implement due to cost and space constraints. He [37] developed a degradation model for lithium-ion batteries according to a new exponential model. This finding suggested a new model of the form:…”

Section: State Of Life (Sol)mentioning

confidence: 99%

Battery Management Systems in Electric and Hybrid Vehicles

et al. 2011

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Abstract:The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery's performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

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“…Compared with traditional ones, lithium-ion battery has many advantages including high output voltage, high energy density, low self-discharge, long cycle life, high reliability and etc [1][2] . These advantages have brought more widely industrial applications on lithium-ion battery.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning

Zhao¹

2018

Proceedings of the 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018)

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Abstract. In this paper, a conceptual framework for Remaining Useful Life (RUL) prediction of lithium-ion battery integrating deep learning is presented. The main processing stages, i.e., feature extraction, redundant information removal, data preprocessing, DNN model training, RUL prediction and evaluation, are discussed. Finally, a feature extraction method is presented by analyzing a lithium-ion battery data set from NASA AMES Center.

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Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method

Cited by 798 publications

References 25 publications

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

Battery Management Systems in Electric and Hybrid Vehicles

A Conceptual Framework for Lithium-ion Battery RUL Prediction Using Deep Learning

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