Data-driven design of a cascaded observer for battery state of health estimation

Hametner, Christoph; Jakubek, Stefan; Prochazka, Wenzel

doi:10.1109/icset.2016.7811778

Cited by 13 publications

(12 citation statements)

References 14 publications

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“…Lemma 1 (see Ding 6 ). Given the process model (1) and any parity vector v s = [ v s,0 · · · v s,s ] satisfying (7), then…”

Section: Preliminaries Of Luenberger Observer Designmentioning

confidence: 99%

“…Recently, data‐driven approaches were proposed for centralized observer design that does not require to identify a complete set of process models . It has been shown that the observers can be designed by using data, and the resulting algorithm has a more flexible structure . This observer can either be used for centralized state estimation, or for residual generation for fault detection .…”

Section: Introductionmentioning

confidence: 99%

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Distributed data‐driven observer for linear time invariant systems

Alipouri

Zhao

Huang

2020

Adaptive Control & Signal

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Summary This paper is concerned with distributed data‐driven observer design problem. The existing data‐driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model‐based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data‐driven approaches there is no such information available. The main goal of this paper is to extend the centralized data‐driven observer design approach to the distributed framework. The stability of the proposed data‐driven distributed observer is also proved analytically. A quadruple‐tank process is simulated to demonstrate the performance of the proposed scheme.

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“…Lemma 1 (see Ding 6 ). Given the process model (1) and any parity vector v s = [ v s,0 · · · v s,s ] satisfying (7), then…”

Section: Preliminaries Of Luenberger Observer Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed data‐driven observer for linear time invariant systems

Alipouri

Zhao

Huang

2020

Adaptive Control & Signal

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show abstract

“…The stochastic processes modeling methods use the probability theory and stochastic processes to analyze the variation law of historical monitoring data [49]. This type of methods often estimates the SOH of lithium-ion batteries by establishing a stochastic degradation model of battery capacity [22], [50], [51]. The SOH estimation results by these stochastic degradation model based methods do not rely on physics or engineering principle.…”

Section: B Literature Reviewmentioning

confidence: 99%

State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect

et al. 2019

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State-of-Health (SOH) is an intuitive reflection to the capacity during the degradation of lithium-ion batteries. Accurate SOH estimation can not only grasp the battery performance but also achieve a better balance between the safety and economic benefits for lithium-ion battery application system. Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during long rest time. This paper mainly studies the impact of relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel SOH estimation method based on the Wiener process. First, the life cycle of a lithium-ion battery is divided into three parts, i.e., the degradation process that eliminates the relaxation effect, the capacity regeneration process during the rest time, and the degradation process of the regenerated capacity. Next, the degradation model after eliminating the relaxation effect is established based on linear Wiener process, the capacity regenerated model is developed by the normal distribution, and the degradation model of regenerated capacity is established based on a nonlinear Wiener process. Then, for the degradation model based on nonlinear Wiener process, a two-step maximum likelihood estimation (MLE) method for prior parameters is proposed, and the random variables representing the personality features are updated online under the Bayesian framework. For the capacity regenerated model, a parameter estimation method based on MLE is proposed. In addition, a one-step and a multi-step SOH prediction method based on piecewise modeling are developed. Finally, the experiments are carried out based on the degradation data of lithium-ion batteries published by the NASA, and the results show that the method proposed in this paper can effectively improve the accuracy of the SOH estimation.INDEX TERMS Lithium-ion batteries, state of health, relaxation effect, Wiener process, Bayesian framework.

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“…Then, the advanced classification, machine learning and intelligent optimization algorithms will be applied to find the mapping relationship between inputs and targets, thereby realizing accurate prediction with the premise of enough training data. For SOH estimation, data driven methods do not require knowledge with respect to detailed battery degeneration mechanism and depend only on enough operational degradation data [24]. Usually, these methods extract key characteristic information of battery degradation from huge data sets by means of machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

et al. 2020

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Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which in turn speeds up aging process of the battery. To attain the precise SOH estimation, an efficient estimation manner based on machine learning is proposed in this study. Firstly, the voltage profile during charging and discharging process and incremental capacity variation are acquired through the cycle life test, and the healthy features correlating to battery degradation are extracted. Secondly, the grey relation analysis and entropy weight method are employed to analyze the healthy features. Finally, the long short-term memory is established to achieve the SOH estimation of battery. The experimental results highlight that the proposed method can effectively predict the battery SOH with preferable accuracy, stability and robustness. INDEX TERMS Healthy features (HFs), grey relational analysis (GRA), entropy weight method (EWM), long short-term memory (LSTM), state of health (SOH). NOMENCLATURE A. ABBREVIATIONS

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Data-driven design of a cascaded observer for battery state of health estimation

Cited by 13 publications

References 14 publications

Distributed data‐driven observer for linear time invariant systems

Distributed data‐driven observer for linear time invariant systems

State-of-Health Estimation for Lithium-Ion Batteries Based on Wiener Process With Modeling the Relaxation Effect

State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

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