Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach
Hongwen He,Rui Xiong,Jinxin Fan +2 more
TLDR
In this article, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately.Abstract:
To improve the use of lithium-ion batteries in electric vehicle (EV) applications, evaluations and comparisons of different equivalent circuit models are presented in this paper. Based on an analysis of the traditional lithium-ion battery equivalent circuit models such as the Rint, RC, Thevenin and PNGV models, an improved Thevenin model, named dual polarization (DP) model, is put forward by adding an extra RC to simulate the electrochemical polarization and concentration polarization separately. The model parameters are identified with a genetic algorithm, which is used to find the optimal time constant of the model, and the experimental data from a Hybrid Pulse Power Characterization (HPPC) test on a LiMn2O4 battery module. Evaluations on the five models are carried out from the point of view of the dynamic performance and the state of charge (SoC) estimation. The dynamic performances of the five models are obtained by conducting the Dynamic Stress Test (DST) and the accuracy of SoC estimation with the Robust Extended Kalman Filter (REKF) approach is determined by performing a Federal Urban Driving Schedules (FUDS) experiment. By comparison, the DP model has the best dynamic performance and provides the most accurate SoC estimation. Finally, sensitivity of the different SoC initial values is investigated based on the accuracy of SoC estimation with the REKF approach based on the DP model. It is clear that the errors resulting from the SoC initial value are significantly reduced and the true SoC is convergent within an acceptable error.read more
Citations
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Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
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A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles
TL;DR: In this paper, the authors provide a review on two aspects that are battery thermal model development and thermal management strategies, and discuss thermal effects of lithium-ion batteries in terms of thermal runaway and response under cold temperatures.
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Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
TL;DR: The review presents the key feedback factors that are indispensable for accurate estimation of battery SoC, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
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A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur
TL;DR: In this paper, the authors reviewed and discussed various battery modelling approaches, including mathematical models, electrochemical models and electrical equivalent circuit models, and concluded that the state-of-the-art in battery modelling is not sufficient for this chemistry, and new modelling approaches are needed.
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Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles
TL;DR: In this article, the authors present a method to estimate the state-of-charge (SOC) of a lithium-ion battery, based on an online identification of its open-circuit voltage (OCV), according to the battery's intrinsic relationship between the SOC and the OCV for application in electric vehicles.
References
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State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
TL;DR: This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF).
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State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge
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Lithium-Ion Battery State of Charge and Critical Surface Charge Estimation Using an Electrochemical Model-Based Extended Kalman Filter
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Adaptive online state-of-charge determination based on neuro-controller and neural network
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