Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications

Naseri, Farshid; Farjah, Ebrahim; Ghanbari, Teymoor; Kazemi, Zahra; Schaltz, Erik; Schanen, Jean‐Luc

doi:10.1109/tie.2019.2941151

Cited by 80 publications

(36 citation statements)

References 27 publications

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“…The matrix

boldΓ

represents the uncertainty of the state estimations. The KF obtains the optimal state estimation in two cascaded steps, namely time‐update and measurement‐update steps [34, 35]. The time‐update equations for the state vector X and the covariance matrix

boldΓ

can be obtained as follows:

{\hat{X}}^{-} false(k false) = F false(k - 1 false) {\hat{X}}^{+} false(k - 1 false)

Γ^{-} false(k false) = F false(k - 1 false) Γ^{+} false(k - 1 false) F^{T} false(k - 1 false) + bold-italicQ

where

{\hat{X}}^{-}

denotes a priori state estimate.…”

Section: Operating Principles Of the Proposed Methodsmentioning

confidence: 99%

Real‐time open‐switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter

Naseri

Schaltz

et al. 2020

IET power electron.

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“…The matrix

boldΓ

boldΓ

can be obtained as follows:

{\hat{X}}^{-} false(k false) = F false(k - 1 false) {\hat{X}}^{+} false(k - 1 false)

Γ^{-} false(k false) = F false(k - 1 false) Γ^{+} false(k - 1 false) F^{T} false(k - 1 false) + bold-italicQ

where

{\hat{X}}^{-}

denotes a priori state estimate.…”

Section: Operating Principles Of the Proposed Methodsmentioning

confidence: 99%

Real‐time open‐switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter

Naseri

Schaltz

et al. 2020

IET power electron.

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“…The

l_{1}

norm of the i th entry of the denoised estimated attack vector

{\tilde{a}}_{i}

is obtained as follows during each time interval with a window length of

t_{0}^{'}

∥ {\tilde{a}}_{i} false(t false) ∥_{1} = \int_{t - t_{0}^{'}}^{t} | {\tilde{a}}_{i} false(t false) | normald t

An attack on the i th sensor can be detected when the value of the signal

∥ {\tilde{a}}_{i} false(t false) ∥_{1}

exceeds a pre‐determined threshold value. Here, the threshold value for cyber‐attack localisation is chosen based on a trial and error approach [34, 35], by calculating and analysing the values of

∥ {\tilde{a}}_{i} false(t false) ∥_{1}

in extensive simulation cases considering different attack and non‐attack conditions.…”

Section: Operating Principles Of the Proposed Approachmentioning

confidence: 99%

Efficient resilient dynamic co‐estimation framework for cyber‐physical systems under sensor attacks

Kazemi

Safavi

Setoodeh

2020

IET Control Theory & Appl

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In this study, a novel co‐estimation approach is proposed for dynamic state estimation of cyber‐physical systems (CPSs) subjected to sensor attacks. In the proposed approach, the sensory measurements are preprocessed to extract some valuable information about the launched cyber‐attack. This information is then manipulated to form new state equations, which are augmented with the CPS model. The newly obtained model effectively represents the behaviour of both the CPS states as well as the launched cyber‐attack, and can be used for their co‐estimation. Two structures are suggested for the preprocessing of the sensory measurements that deploy Kalman filter (KF) and two‐step KF for co‐estimation of the states and cyber‐attacks. To effectively handle the concurrent effects of cyber‐attacks and unknown inputs on the CPS co‐estimation process, some auxiliary outputs are generated using high‐gain approximate differentiators to realise the required matching conditions. The proposed dynamic co‐estimation approach improves accuracy via considering the inter‐correlation between the CPS states and the cyber‐attacks. To demonstrate the viability of the proposed co‐estimation method, thorough stability and feasibility analysis are provided. Furthermore, extensive simulations are performed on a well‐known CPS‐the IEEE 14‐bus test system‐ to demonstrate the effectiveness of the proposed approach.

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“…e evaluation of the parameters such as the state of charge and the remaining useful life has guiding significance for the use, maintenance, and economic analysis of lithium batteries. e SOC of the compound energy storage system of electric vehicles is the basis of rational energy management [49][50][51], so accurate SOC information is of great significance to improve the dynamic performance [52,53] and range of electric vehicles [54][55][56][57][58].…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

Wang

Liu

Sun³

et al. 2021

Complexity

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This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.

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Online Parameter Estimation for Supercapacitor State-of-Energy and State-of-Health Determination in Vehicular Applications

Cited by 80 publications

References 27 publications

Real‐time open‐switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter

Real‐time open‐switch fault diagnosis in automotive permanent magnet synchronous motor drives based on Kalman filter

Efficient resilient dynamic co‐estimation framework for cyber‐physical systems under sensor attacks

State of Charge Estimation of Composite Energy Storage Systems with Supercapacitors and Lithium Batteries

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