A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model

Xu, Jun; Mi, Chris; Cao, Binggang; Cao, Junyi

doi:10.1016/j.jpowsour.2013.01.094

Cited by 261 publications

(127 citation statements)

References 17 publications

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“…There are plenty of battery models reported in previous studies, including physics-based models and equivalent circuit models. Physics-based models [26][27][28], could be full order models, simplified reduced order models, single particle models and so forth, while the equivalent models could be the first order RC model [29], the second order RC model [29,30], the impedance model [20], etc. However, the Li-ion battery is a complex electrochemical system, which can show strong nonlinearity and uncertainty.…”

Section: The Analysis Of the Battery Modelmentioning

confidence: 99%

“…It is difficult to implement the impedance model in real-time applications. The authors have tried to implement the impedance model and proposed some new methods [20]. Though the results were satisfactory, the computation complexity should not be ignored.…”

Section: The Analysis Of the Battery Modelmentioning

confidence: 99%

“…According to the literatures, some model-based methods can be the Luenberger observer SOC estimation method [13][14][15][16], the Sliding mode observer SOC estimation method [17][18][19], the Kalman filter SOC estimation method [20][21][22] and the Proportional integral observer (PIO) SOC estimation method [23][24][25], etc. Such estimation methods are so popular these days that evaluation studies of these methods should be carried out.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Zou

et al. 2014

Energies

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Abstract:Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods.

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Section: The Analysis Of the Battery Modelmentioning

confidence: 99%

Section: The Analysis Of the Battery Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Zou

et al. 2014

Energies

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“…The existing SOC estimation algorithms can be divided into two categories, namely non-model-based type and model-based type. The former is typically based on Ampere-hour (Ah) or Coulomb counting [1,2], open-circuit voltage (OCV) [3][4][5], electrochemical impedance spectroscopy (EIS) [6,7], artificial neural networks (ANNs) [8][9][10][11] and fuzzy-logic (FL) [12,13]. The Ah counting method acquires the SOC by integrating the current over the time.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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“…[10][11][12][13][14][15][16][17] By investigating the impedance response, the charge transport resistance, including electron transport in the solid phase, ionic transport in the electrolyte, solid-state diffusion in the active material, and the intercalation process, can be quantified.…”

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confidence: 99%

Influence of Microstructure on Impedance Response in Intercalation Electrodes

Cho

Chen

Mukherjee

2015

J. Electrochem. Soc.

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The active particle morphology and microstructure affect the impedance behavior of intercalation electrodes due to the underlying charge transport, active material/electrolyte interfacial surface area, and solid-phase diffusion in lithium-ion batteries (LIB). In order to capture the impact of the electrode microstructural variability on the impedance response, an integrated electrochemical impedance predictive analysis is presented. In the analysis, stochastically reconstructed 3-D microstructures of representative LIB electrodes are considered with variations in the active material morphology and particle size distribution. With the properties evaluated from the virtual 3-D microstructures, the corresponding impedance response is predicted. The concept of electrochemical Sauter mean diameter (ESMD) has been introduced to investigate the effect of active particle morphology, such as particle agglomeration. This integrated analysis is envisioned to offer a virtual impedance response probing framework to elucidate the influence of electrode microstructural variability and underlying electrochemical and transport interactions. Lithium-ion batteries (LIBs), due to their favorable energy density and power capability, are considered as the candidate of choice for vehicle electrification. [1][2][3][4] To further improve the cell performance, different efforts have been undertaken to study the microstructural characteristics, including electrode thickness, 5 porosity, 6 particle size, 7 conductive additives 8 and composition. 9 Particularly, the influence of electrode microstructure on charge (i.e. electron and ion) transport, and solid-phase diffusion is critical, which ultimately affects the cell performance.One of the powerful methods for analyzing the electrochemical and transport behavior in porous electrodes is the electrochemical impedance spectroscopy (EIS), which measures the response of a small perturbation of potential or current.10-17 By investigating the impedance response, the charge transport resistance, including electron transport in the solid phase, ionic transport in the electrolyte, solid-state diffusion in the active material, and the intercalation process, can be quantified.14,18-26 Meyers et al. proposed a mathematical model to capture the impedance behavior of a spherical active particle dependent on the physical properties related to the faradaic and nonfaradaic impedance.14 The model also included the effect of particle size distribution on the impedance response.The impedance response of intercalation electrodes, which is sensitive to the interfacial processes, the surface morphology, and the charge transport, has been described mathematically by the fundamental physical approach for various electrochemical systems.14,27-39The effect of various properties, such as electrical and ionic conductivity, and layer thickness, have been investigated by Levi and Aurbach. 28 Besides the electrode properties (e.g. electrical conductivity, ionic conductivity, and layer thickness), the electrode microst...

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A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model

Cited by 261 publications

References 17 publications

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

Influence of Microstructure on Impedance Response in Intercalation Electrodes

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