Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery

Wei, Zhongbao; Lim, Tuti Mariana; Skyllas‐Kazacos, Maria; Wai, Nyunt; Tseng, King Jet

doi:10.1016/j.apenergy.2016.03.103

Cited by 158 publications

(70 citation statements)

References 32 publications

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“…However, these approaches either require considerable computational overhead or incur significant cross interference between the states and parameters [21]. This section designs a lightweight parameter identifier which can effectively address these deficiencies.…”

Section: On-line Parameter Identificationmentioning

confidence: 99%

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Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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To achieve accurate state-of-charge (SoC) estimation for LiFePO 4 (lithium iron phosphate) batteries under harsh conditions, this paper resorts to the Peukert's law to accommodate different temperatures and load excitations. By analyzing battery heat generation and dissipation, a thermal evolution model (TEM) is elaborated and exploited for on-line parameter identification of the equivalent circuit model (ECM). Then, a SoC estimation framework is proposed based on the Adaptive Extended Kalman Filter (AEKF) algorithm. Experimental results on a LiFePO 4 pack subject to the Federal Urban Driving Schedule (FUDS) profile under different temperatures and initial states suggest that the proposed SoC estimator provides good robustness and accuracy against changing temperature and highly dynamic loads.

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Section: On-line Parameter Identificationmentioning

confidence: 99%

“…Many on-line approaches have been reported to identify ECM parameters [21][22][23][24]. However, these approaches either require considerable computational overhead or incur significant cross interference between the states and parameters [21].…”

Section: On-line Parameter Identificationmentioning

confidence: 99%

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Xie

2018

Energies

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“…Basically, the parameter identification techniques are the recursive least squares (RLS) type or adaptive filtering (AF) type of methods [16][17][18][19][20][21][22][23][24][25], e.g., Xiong et al [23] proposed a data-driven estimation approach which can simultaneously obtain the model parameter and the internal state of the battery. These techniques have been widely proven to be effective in online parameter identification.…”

Section: Online Model Parameter Identificationmentioning

confidence: 99%

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

et al. 2017

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Abstract:The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.

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“…However, some of the correlations are hard to know for the following reasons: (i) measurements are time-consuming [2]; (ii) control experiments are usually difficult to perform; and (iii) there is no current physical model that can precisely connect the relationships between external settings and intrinsic properties for SWH. Currently, there are some state-of-the-art methods for the estimation of energy system properties [3][4][5] and for the optimization of performances [6][7][8][9][10][11]. However, most of them are not suitable for the solar energy system.…”

Section: Introductionmentioning

confidence: 99%

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Liu

et al. 2017

International Journal of Photoenergy

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Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a highperformance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

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Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery

Cited by 158 publications

References 32 publications

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Peukert-Equation-Based State-of-Charge Estimation for LiFePO4 Batteries Considering the Battery Thermal Evolution Effect

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

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