Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Seo, Bo-Hwan; Nguyễn, Thanh Hải; Lee, Dong-Choon; Lee, Kyo‐Beum; Kim, Jang-Mok

doi:10.6113/jpe.2012.12.5.778

Cited by 18 publications

(7 citation statements)

References 23 publications

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“…Some example works in the model-based approaches include the papers in [2][3][4][5]. Bhangu et al [2] propose to use a noncomplex, generic model of a battery cell and then use a Kalman filter in order to correct for the large state errors that develop over time.…”

Section: Introductionmentioning

confidence: 99%

“…Bhangu , et al [2] propose to use a non‐complex, generic model of a battery cell and then use a Kalman filter in order to correct for the large state errors that develop over time. Seo , et al [3] use a sigma‐point Kalman filter to derive a runtime‐based battery model, to estimate the SOC. This approach gives reasonably good estimation of SOC in terms of accuracy, while alleviating the effects of sensory noise and disturbance.…”

Section: Introductionmentioning

confidence: 99%

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Supervised learning for early and accurate battery terminal voltage collapse detection

Obeid

Tariq

Mukhopadhyay

2020

IET Circuits, Devices & Systems

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Rechargeable batteries are critical components in many electrical systems nowadays. One has to ensure reliable diagnosis and assessment of the installed batteries for smooth and safe operations. Assessment of the remaining capacity of a battery is crucial diagnostic information. A battery management system (BMS) needs to reliably report the ability of the battery to supply power or the lack thereof. If the BMS fails to do so at an early stage, this may compromise the health of the entire electric system. When a battery nears a region where the battery state-of-charge (SOC) is low, there is a risk of an abrupt drop in the terminal voltage. An early detection of such a region is crucial; otherwise, the BMS may not have enough time to react. To address this issue, our work provides a novel supervised learning approach towards an early detection of Li-ion battery terminal voltage collapse. No knowledge of initial SOC or battery model parameters is required. This is particularly important as batteries lose their capacity to store charge over time. The efficacy of the proposed approach is demonstrated by an early and accurate detection of terminal voltage collapse over a set of discharge tests conducted using multiple batteries.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised learning for early and accurate battery terminal voltage collapse detection

Obeid

Tariq

Mukhopadhyay

2020

IET Circuits, Devices & Systems

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“…The model-based methodologies attempt to constitute physical models of the lithium-ion battery for RUL prediction. Recently, various Bayesian filtering models such as Kalman filter [3], extended Kalman filter [4][5][6], particle filter [7][8][9], and unscented particle filter [10] have been extensively used to construct exhaustive models of deteriorating lithium-ion batteries. However, uncertainty due to assumptions and simplifications in the models may impose severe limitations upon their applicability in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Yang

Xiao

Zhou

et al. 2016

Shock and Vibration

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Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

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“…In contrast, the central difference Kalman filter (CDKF) is a stable algorithm and able to generate certain number of points for state estimation intelligently. References [21,22] indicate that CDKF, as one sigma-points Kalman filter (SPKF) method, is able to avoid the linearization error of the battery model and improve the model's precision for SoC estimation, which has the potential to solve the nonlinear estimation problems [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles

Gao

Zhang

2015

Energies

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Accurate state of charge (SoC) estimation of batteries plays an important role in promoting the commercialization of electric vehicles. The main work to be done in accurately determining battery SoC can be summarized in three parts. (1) In view of the model-based SoC estimation flow diagram, the n-order resistance-capacitance (RC) battery model is proposed and expected to accurately simulate the battery's major time-variable, nonlinear characteristics. Then, the mathematical equations for model parameter identification and SoC estimation of this model are constructed. (2) The Akaike information criterion is used to determine an optimal tradeoff between battery model complexity and prediction precision for the n-order RC battery model. Results from a comparative analysis show that the first-order RC battery model is thought to be the best based on the Akaike information criterion (AIC) values. (3) The real-time joint estimator for the model parameter and SoC is constructed, and the application based on two battery types indicates that the proposed SoC estimator is a closed-loop identification system where the model parameter identification and SoC estimation are corrected mutually, adaptively and simultaneously according to the observer values. The maximum SoC estimation error is less than 1% for both battery types, even against the inaccurate initial SoC. OPEN ACCESSEnergies 2015, 8 8595

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Condition Monitoring of Lithium Polymer Batteries Based on a Sigma-Point Kalman Filter

Cited by 18 publications

References 23 publications

Supervised learning for early and accurate battery terminal voltage collapse detection

Supervised learning for early and accurate battery terminal voltage collapse detection

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles

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