Estimation of Battery State of Charge With $H_{\infty}$  Observer: Applied to a Robot for Inspecting Power Transmission Lines

Zhang, Fei; Liu, Guangjun; Fang, Lijin; Wang, Hongguang

doi:10.1109/tie.2011.2159691

Cited by 164 publications

(85 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, for the existing H ∞ observer-based SOC estimation methods, the observer's gain in the work [21] is constant and difficult to calculate to adapt the nonlinear battery model. The H ∞ observer in [22] is also constant and used to deal with the linear battery model. On the contrary, the H ∞ method in this paper is employed to design the observer with dynamic gain for the nonlinear battery model, which is not a trivial work.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

View full text Add to dashboard Cite

This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H ∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF).

show abstract

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The performances of the four methods are much different due to their own particular characteristics, which have been verified by simulations and experiments. The differences in the time consumptions were obvious: the feedback coefficient of the Luenberger observer SOC estimation method is the Luenberger gain which is constant, while the Kalman filter SOC estimation method has to calculate the Kalman gain through Equations (9) to (13), where complex covariances have to be calculated, leading to a longer calculation time. The PIO SOC estimation method takes advantage of the integral of the voltage errors and thus the estimation errors could be smaller and the rise time could be shorter, while the time consumption would be longer than Luenberger SOC estimation method for the added integral part, but shorter than the covariance computation of the Kalman filter SOC estimation method.…”

Section: Discussionmentioning

confidence: 99%

“…The Luenberger Observer [13] has been widely used in different applications for its simple properties. It was recently introduced to estimate the SOC of batteries [14][15][16].…”

Section: The Luenberger Observer Soc Estimation Methodsmentioning

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%

See 1 more Smart Citation

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

Zou

et al. 2014

Energies

View full text Add to dashboard Cite

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.

show abstract

“…In recent years, many scholars have proposed substantial methods to improve the accuracy of SOC estimation, such as Ampere hour counting [6], Kalman Filter (KF) [7], H∞ observer [8], sliding model observer [9], etc. Ampere hour counting is an extensively utilized method, which applies integrals with respect to the battery current, but it heavily depends on the accuracy of the current measurement and the initial SOC value [10].…”

Section: Introductionmentioning

confidence: 99%

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

et al. 2017

View full text Add to dashboard Cite

An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation.

show abstract

Estimation of Battery State of Charge With $H_{\infty}$ Observer: Applied to a Robot for Inspecting Power Transmission Lines

Cited by 164 publications

References 18 publications

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method

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

A New State of Charge Estimation Algorithm for Lithium-Ion Batteries Based on the Fractional Unscented Kalman Filter

Contact Info

Product

Resources

About