Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Zhang, Caiping; Jiang, Jiuchun; Zhang, Weige; Sharkh, Suleiman M.

doi:10.3390/en5041098

Cited by 62 publications

(31 citation statements)

References 22 publications

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“…After being properly initialized (the cell's design limits are shown in Table 1), the battery then runs through a verification profile of the Federal Urban Driving Schedules (FUDS) [18,19] on the HIL bench downloaded from the xPC Target. The measured current and voltage profiles of the FUDS are shown in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach

et al. 2012

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Battery peak power capability estimations play an important theoretical role for the proper use of the battery in electric vehicles. To address the failures in relaxation effects and real-time ability performance, neglecting the battery's design limits and other issues of the traditional peak power capability calculation methods, a new approach based on the dynamic electrochemical-polarization (EP) battery model, taking into consideration constraints of current, voltage, state of charge (SoC) and power is proposed. A hardware-in-the-loop (HIL) system is built for validating the online model-based peak power capability estimation approach of batteries used in hybrid electric vehicles (HEVs) and a HIL test based on the Federal Urban Driving Schedules (FUDS) is used to verify and evaluate its real-time computation performance, reliability and robustness. The results show the proposed approach gives a more accurate estimate compared with the hybrid pulse power characterization (HPPC) method, avoiding over-charging or over-discharging and providing a powerful guarantee for the optimization of HEVs power systems. Furthermore, the HIL test provides valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms.

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Section: Resultsmentioning

confidence: 99%

Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach

et al. 2012

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“…Dynamic models representing the operation of battery cells have been widely studied. These include equivalent circuit models [9][10][11][12][13], intelligent neural network models [14,15], and physical-based electrochemical models [16][17][18][19]. In [9], an electrical battery model capable of predicting runtime and I-V performance was proposed.…”

Section: Introductionmentioning

confidence: 99%

Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System

Shiau

2013

Energies

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This paper analyzes and simulates the Li-ion battery charging process for a solar powered battery management system. The battery is charged using a non-inverting synchronous buck-boost DC/DC power converter. The system operates in buck, buck-boost, or boost mode, according to the supply voltage conditions from the solar panels. Rapid changes in atmospheric conditions or sunlight incident angle cause supply voltage variations. This study develops an electrochemical-based equivalent circuit model for a Li-ion battery. A dynamic model for the battery charging process is then constructed based on the Li-ion battery electrochemical model and the buck-boost power converter dynamic model. The battery charging process forms a system with multiple interconnections. Characteristics, including battery charging system stability margins for each individual operating mode, are analyzed and discussed. Because of supply voltage variation, the system can switch between buck, buck-boost, and boost modes. The system is modeled as a Markov jump system to evaluate the mean square stability of the system. The MATLAB based Simulink piecewise linear electric circuit simulation tool is used to verify the battery charging model.

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“…Also, according to (6) and (7), the trajectory of the estimated battery equivalent circuit parameters and OCV can be determined using ( ) u k and…”

Section: Recursive Penalized Wavelet Estimator For Online Plbm Identimentioning

confidence: 99%

“…Researchers worldwide have developed a wide variety of battery models for different purposes. Almost all the existing battery models can be classified into the following two types: (1) parametric models, e.g., electrochemical models [1,2] and electrical models [3][4][5][6]; and (2) nonparametric models, e.g., artificial neural network (ANN) models [7][8][9] and abstract mathematical models [10]. Parametric battery models have been developed in terms of the equivalent electric-circuit parameters (for electrical models) or electrochemical parameters (for electrochemical models).…”

Section: Introductionmentioning

confidence: 99%

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

Jiang

Zhang

2013

Energies

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Abstract:Battery model identification is very important for reliable battery management as well as for battery system design process. The common problem in identifying battery models is how to determine the most appropriate mathematical model structure and parameterized coefficients based on the measured terminal voltage and current. This paper proposes a novel semiparametric approach using the wavelet-based partially linear battery model (PLBM) and a recursive penalized wavelet estimator for online battery model identification. Three main contributions are presented. First, the semiparametric PLBM is proposed to simulate the battery dynamics. Compared with conventional electrical models of a battery, the proposed PLBM is equipped with a semiparametric partially linear structure, which includes a parametric part (involving the linear equivalent circuit parameters) and a nonparametric part [involving the open-circuit voltage (OCV)]. Thus, even with little prior knowledge about the OCV, the PLBM can be identified using a semiparametric identification framework. Second, we model the nonparametric part of the PLBM using the truncated wavelet multiresolution analysis (MRA) expansion, which leads to a parsimonious model structure that is highly desirable for model identification; using this model, the PLBM could be represented in a linear-in-parameter manner. Finally, to exploit the sparsity of the wavelet MRA representation and allow for online implementation, a penalized wavelet estimator that uses a modified online cyclic coordinate descent algorithm is proposed to identify the PLBM in a recursive fashion. The simulation and experimental results demonstrate that the proposed PLBM with the corresponding identification algorithm can accurately simulate the dynamic behavior of a lithium-ion battery in the Federal Urban Driving Schedule tests.

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Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering

Cited by 62 publications

References 22 publications

Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach

Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach

Li-Ion Battery Charging with a Buck-Boost Power Converter for a Solar Powered Battery Management System

Online Semiparametric Identification of Lithium-Ion Batteries Using the Wavelet-Based Partially Linear Battery Model

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