Ni–MH batteries state-of-charge prediction based on immune evolutionary network

Cheng, Bo; Zhou, Yanlu; Zhang, Jiexin; Wang, Junping; Cao, Binggang

doi:10.1016/j.enconman.2009.08.010

Cited by 29 publications

(14 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this kind of method, the key point is how to improve the prediction ability of the black-box model. Consequently many computational intelligence-based and optimization-based approaches such as artificial neural networks based models [6][7][8][9][10][11][12][13] and fuzzy logic models [14][15][16][17][18] have been chosen to implement the processes of input selection, training and validation, to establish an adequately accurate SoC estimation model. Support vector regression (SVR) based methods were also applied to realize the SoC estimation of batteries, such as the standard  -SVR model [19], the least squares SVR model [20], the  -SVR model [21] and the fuzzy clustering based SVR model [22].…”

Section: Introductionmentioning

confidence: 99%

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

2010

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

2010

View full text Add to dashboard Cite

show abstract

“…Consequently, if the uncertainty Λ(k) satisfies the condition in Equation (8) and eigenvalues' modules of matrix A' are smaller than one, Z(k) is bound. Also, the estimation errorx(k) is bounded.…”

Section: Appendix: Stability Analysismentioning

confidence: 99%

“…Thus, if the battery is changed, then the coefficients must also be re-calculated. An artificial neural network used for SOC estimation is presented in [8]. Fundamentally, because the battery dynamics exhibit nonlinear behavior, this method usually produces a more accurate SOC estimate than the other methods.…”

Section: Introductionmentioning

confidence: 99%

Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model

et al. 2013

View full text Add to dashboard Cite

A second-order discrete-time sliding mode observer (DSMO)-based method is proposed to estimate the state of charge (SOC) of a Li-ion battery. Unlike the first-order sliding mode approach, the proposed method eliminates the chattering phenomenon in SOC estimation. Further, a battery model with a dynamic resistance is also proposed to improve the accuracy of the battery model. Similar to actual battery behavior, the resistance parameters in this model are changed by both the magnitude of the discharge current and the SOC level. Validation of the dynamic resistance model is performed through pulse current discharge tests at two different SOC levels. Our experimental results show that the proposed estimation method not only enhances the estimation accuracy but also eliminates the chattering phenomenon. The SOC estimation performance of the second-order DSMO is compared with that of the first-order DSMO.

show abstract

“…To avoid the difficulty of battery modeling and identification, machine learning strategies were also introduced to establish black-boxes mapping measurable data to SoC, including Neural Network (NN) [21], fuzzy NN [22,23], evolutionary NN [24,25] and support vector machine [26,27]. These data-oriented methods can not avoid their intrinsic problems such as large number of training data covering the whole possible range of operation, the selection of model structure and the balance between under-fitting and over-fitting.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

Yan

Qian

et al. 2010

Energies

View full text Add to dashboard Cite

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studies in simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

show abstract

Ni–MH batteries state-of-charge prediction based on immune evolutionary network

Cited by 29 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

Second-Order Discrete-Time Sliding Mode Observer for State of Charge Determination Based on a Dynamic Resistance Li-Ion Battery Model

Robust State of Charge Estimation for Hybrid Electric Vehicles: Framework and Algorithms

Contact Info

Product

Resources

About