Vehicle Valve Regulated Lead Acid Battery Modeling and Fault Diagnosis

Hammad, Nabil

doi:10.4271/2010-01-0028

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…A key function of the BMS is to monitor the state of charge (SOC), state of health (SOH), instantaneous available power [i.e., state of power (SOP)], internal impedance, capacity, etc., during battery operation [3]. These parameters and states will offer the fault diagnostic and prognostic capability for the battery system [4]. It is wellunderstood that the parameters and states can only be obtained online, typically, from model-based estimation methods due to the absence of sensors for direct measurements of these quantities.…”

Section: Introductionmentioning

confidence: 99%

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

Kim

Wang

Şahinoğlu

et al. 2014

2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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This paper proposes a novel real-time model-based state of charge (SOC) estimation method for lithium-ion batteries. The proposed method includes: 1) an electrical circuit battery model incorporating the hysteresis effect, 2) a fast upper-triangular and D-diagonal recursive least square (FUDRLS)-based online parameter identification algorithm for the electrical battery model, and 3) an iterated smooth variable structure filter (ISVSF) for SOC estimation. The proposed method enables an accurate and robust condition monitoring for lithium-ion batteries. Due to its low complexity, the proposed method is suitable for the real-time embedded battery management system (BMS) application. Simulation and experiment are performed to validate the proposed method for a polymer lithium-ion cell. Index Terms-Battery model, fast UD recursive least square (FUDRLS), hysteresis, iterative smooth variable structure filter (ISVSF), lithium-ion battery, state of charge (SOC) estimation I.

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Section: Introductionmentioning

confidence: 99%

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

Kim

Wang

Şahinoğlu

et al. 2014

2014 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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“…Meanwhile, control strategies may take the SOH into consideration for long-term targets [4]. The maximum capacity (i.e., capacity fade) and the internal impedance (i.e., power fade) have been used to quantify the SOH, which is used to prevent catastrophic failures of the batteries [5]. The maximum capacity is generally difficult to measure and needs to be estimated from measurements.…”

Section: Introductionmentioning

confidence: 99%

A Rayleigh Quotient-Based Recursive Total-Least-Squares Online Maximum Capacity Estimation for Lithium-Ion Batteries

Kim¹,

Wang

Şahinoğlu

et al. 2015

IEEE Trans. Energy Convers.

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The maximum capacity, the amount of maximal electric charge that a battery can store, not only indicates the state of health, but also is required in numerous methods for state-ofcharge estimation. This paper proposes an alternative approach to perform online estimation of the maximum capacity by solving the recursive total-least-squares (RTLS) problem. Different from prior art, the proposed approach poses and solves the RTLS as a Rayleigh quotient optimization problem. The Rayleigh quotient-based approach can be readily generalized to other parameter estimation problems including impedance estimation. Compared with other capacity estimation methods, the proposed algorithm enjoys the advantages of existing RTLS-based algorithms for instance, low computation, simple implementation, and high accuracy, and thus is suitable for use in real-time embedded battery management systems. The proposed method is compared with existing methods via simulations and experiments.Index Terms-Lithium-ion battery, online capacity estimation, Rayleigh quotient, recursive total least squares, state of health.

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“…In order to ensure optimal performance and reliability of a battery system, the BMS should precisely monitor the SOC and electrical impedance of each battery cell. Moreover, the SOC and impedances offer not only the information of the power capability and available energy [1], but also the condition monitoring and diagnostic capability (e.g., SOH) for the battery system [2]. Therefore, cell-level SOC and electrical impedances are the parameters of main interest for management of multicell batteries [3].…”

Section: Introductionmentioning

confidence: 99%

Online state of charge and electrical impedance estimation for multicell lithium-ion batteries

Kim

Wang

2013

2013 IEEE Transportation Electrification Conference and Expo (ITEC)

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Vehicle Valve Regulated Lead Acid Battery Modeling and Fault Diagnosis

Cited by 10 publications

References 6 publications

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

State of charge estimation based on a realtime battery model and iterative smooth variable structure filter

A Rayleigh Quotient-Based Recursive Total-Least-Squares Online Maximum Capacity Estimation for Lithium-Ion Batteries

Online state of charge and electrical impedance estimation for multicell lithium-ion batteries

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