Design and implementation of a battery charger with a state-of-charge estimator

Liu, Tian‐Hua; Chen, Der-Fa; Fang, Chun-Chieh

doi:10.1080/002072100132354

Cited by 30 publications

(9 citation statements)

References 7 publications

(6 reference statements)

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“…According to the choice of the battery model, some commonly used methods can be approximately categorized into three major types. The first type is nonmodel-based Coulomb counting method used by many HEV/BEV battery management systems [2][3][4]. This approach samples the battery's current and computes the accumulated charge to estimate SoC.…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

2010

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Abstract:In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.

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Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

2010

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“…In the past, an assortment of techniques have been presented to estimate or determine the SOC of battery packs or cells [13][14][15][16][17][18][19][20]. Every technique has its own merits and demerits.…”

Section: Introductionmentioning

confidence: 99%

“…Non-model-based methodologies for estimating the SOC are Ampere-hour counting (Coulomb counting) and the open circuit voltage (OCV) methods. The Ampere-hour counting method is quite simple, reliable and easily implementable using very low-priced current sensors [13][14][15][16][17][18][19][20][21]. This method requires a very precise initial estimate of the SOC, which is almost impossible to measure in real applications.…”

Section: Introductionmentioning

confidence: 99%

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

et al. 2018

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Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications.

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“…Many people have studied the approaches to the state-of-charge estimation. Liu presented two methods in [10] to estimate the state-of-charge, whereas the ampere-hours method requires the information of initial state-of-charge and the recursive method needs a lot of offline experimental data to obtain the many parameters. In this application, a simple and practical approach may be desired to estimate the stateof-charge based on the measured charging current and voltage of the battery.…”

Section: Introductionmentioning

confidence: 99%

Strategy for Active Power Sharing in a Fuel-Cell-Powered Charging Station for Advanced Technology Batteries

Jiang¹,

Dougal²

2003

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Design and implementation of a battery charger with a state-of-charge estimator

Cited by 30 publications

References 7 publications

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

Strategy for Active Power Sharing in a Fuel-Cell-Powered Charging Station for Advanced Technology Batteries

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