An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

Liu, Datong; Yin, Xuehao; Song, Yuchen; Liu, Wang; Yu, Peng

doi:10.1109/access.2018.2854224

Cited by 106 publications

(66 citation statements)

References 35 publications

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“…The numbers of genes transferred, n t , is calculated by (11). In this way, the lower-weight particle is overwritten in part by the higher-weight one with favorable genes, like gene editing in biochemistry.…”

Section: Implementation Of the Lpf Algorithmmentioning

confidence: 99%

“…For instance, neural networks [9] and relevance vector machines (RVMs) [10] have been utilized to learn the battery SOH, using test data to estimate the parameters of a battery degradation model. However, these methods are dependent on the training datasets and their models lose accuracy under complex conditions [11]. A more adaptive method is a parameter estimation approach, using a filter such as the extended Kalman filter (EKF) [12], unscented Kalman filter [13], or their improved versions [14][15][16], which can adjust the model parameters to track battery degradation.…”

Section: Introductionmentioning

confidence: 99%

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Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Saldivar

Bai

et al. 2019

Energies

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Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

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Section: Implementation Of the Lpf Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Saldivar

Bai

et al. 2019

Energies

View full text Add to dashboard Cite

show abstract

“…Model-based approaches develop an electrochemical or electrical model of battery to identify the state of batteries. These techniques characterize battery parameters by applying the adaptive filters such as particle filters [4] and Kalman filters [5]. Model-based methods are solid however, they require comprehensive domain knowledge and their development is time-consuming and complex [6].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

et al. 2020

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The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

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“…44,45 The SoC is an important parameter reflecting its remaining available power. 49 A comparative study was conducted on the comprehensive model identification. 46 The load-responsive model switching SoC estimation was investigated on the LIBs, and an event trigger procedure was developed to detect the estimation performance by leveraging the high-pass filter and coulomb counting algorithms.…”

mentioning

confidence: 99%

“…48 An equivalent model was constructed by the physical property analysis that is easy to parameterize. 49 A comparative study was conducted on the comprehensive model identification. 50 A model-based SoC observer was constructed, and the error analysis was performed as well.…”

mentioning

confidence: 99%

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

Cao

Wang

Fernandez

et al. 2019

Energy Science & Engineering

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The state of charge (SoC) estimation is the safety management basis of the packing lithium‐ion batteries (LIB), and there is no effective solution yet. An improved splice equivalent modeling method is proposed to describe its working characteristics by using the state‐space description, in which the optimization strategy of the circuit structure is studied by using the aspects of equivalent mode, analog calculation, and component distribution adjustment, revealing the mathematical expression mechanism of different structural characteristics. A novel particle adaptive unscented Kalman filtering algorithm is introduced for the iterative calculation to explore the working state characterization mechanism of the packing LIB, in which the incorporate multiple information is considered and applied. The adaptive regulation is obtained by exploring the feature extraction and optimal representation, according to which the accurate SoC estimation model is constructed. The state of balance evaluation theory is explored, and the multiparameter correction strategy is carried out along with the experimental working characteristic analysis under complex conditions, according to which the optimization method is obtained for the SoC estimation model structure. When the remaining energy varies from 10% to 100%, the tracking voltage error is <0.035 V and the SoC estimation accuracy is 98.56%. The adaptive working state estimation is realized accurately, which lays a key breakthrough foundation for the safety management of the LIB packs.

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An On-Line State of Health Estimation of Lithium-Ion Battery Using Unscented Particle Filter

Cited by 106 publications

References 35 publications

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Battery Remaining Useful Life Prediction with Inheritance Particle Filtering

Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features

A novel adaptive state of charge estimation method of full life cycling lithium‐ion batteries based on the multiple parameter optimization

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