Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles

Pola, Daniel; Navarrete, Hugo F.; Orchard, Marcos E.; Rabie, Ricardo S.; Cerda, Matías A.; Olivares, Benjamín E.; Silva, Jorge F.; Espinoza, Pablo; Pérez, A.

doi:10.1109/tr.2014.2385069

Cited by 94 publications

(64 citation statements)

References 38 publications

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“…Walker et al's [51] research has found that PF is more accurate than the method (nonlinear least squares and an unscented Kalman filter (UKF)) for predicting RUL. Daniel et al [52] presented the implementation of a PF prognostic framework that uses statistical characterization to estimate the state of charge of a battery. The results show that the proposed framework can prognosticate the discharge time in terms of conditional expectations.…”

Section: Rul Prognostics Methodologies Based On Filtering Techniquesmentioning

confidence: 99%

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

Guan

2016

Applied Sciences

155

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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 Filtering Techniquesmentioning

confidence: 99%

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

Guan

2016

Applied Sciences

155

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“…To ensure operation within these limits, the SoC value is estimated based on an unscented Kalman filter [35], with outer feedback correction loops as presented in [36]. This is because Bayesian estimation algorithms have been demonstrated to be a well-suited estimation tool for nonlinear problems such as SoC estimation, and they have several advantages including real-time implementation and use of empirical models that better deal with limited and noisy data compared to methods such as ampere-hour counting, internal impedance measurement, and open circuit voltage measurement [37,38].…”

Section: Community Power Controller At the Microgrid Levelmentioning

confidence: 99%

“…. , i n } = {1, 2,8,25,26,32,38,42,43,44,46, 48} and the optimal structure of the model has four rules. Note that exogenous variables were not included in the model.…”

Section: Fuzzy Prediction Interval For Net Power Of the Microgridmentioning

confidence: 99%

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

et al. 2019

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This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.

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“…The defect of this method is that the uncertainty of future load profiles is not considered. In order to predict RDT under uncertain future loads, Pola et al employed a two‐state Markov chain to quantify current transition probability and obtain the interval of future current prediction. It is noted that the existing studies including all mentioned above often overlook some dynamic uncertainties in the prediction process, such as the state estimation uncertainty and future load uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A method for remaining discharge time prediction of lithium-ion batteries under dynamic uncertainty

Zhang

Dong

et al. 2019

Int J Energy Res

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Summary Remaining discharge time of the battery system in electric vehicles relates strongly to the decision‐making of driving. Subjected to the various uncertainties, such as modeling uncertainty, state estimation uncertainty, and future load uncertainty, the accuracy and reliability of the remaining discharge time prediction reduce, which will lead to range anxiety. A stochastic framework based on the state‐of‐charge estimation and prediction strategy is proposed to predict remaining discharge time against the uncertainty. Firstly, the equivalent circuit model is established to model the dynamic behavior of the battery. Through the analysis of different fitting functions of open circuit voltage and state‐of‐charge, the Akaike information criterion is introduced to select the best function. Secondly, the coestimator is employed to bound the influence of the model parameter uncertainty and noise uncertainty. Finally, the uncertainties are quantified by a probabilistic method, and a Monte Carlo–based stochastic prediction strategy is proposed to predict the probability distribution of remaining discharge time under dynamic uncertainty. Experimental results demonstrate that the proposed prediction framework is of great effectiveness as it can provide an accurate remaining discharge time interval under dynamic uncertainty, which helps to promote the energy‐saving driving and overcome the range anxiety.

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Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles

Cited by 94 publications

References 38 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

Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

A method for remaining discharge time prediction of lithium-ion batteries under dynamic uncertainty

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