Support vector based battery state of charge estimator

Hansen, Terry; Wang, Chia-Jiu

doi:10.1016/j.jpowsour.2004.09.020

Cited by 293 publications

(135 citation statements)

References 3 publications

Supporting

Mentioning

130

Contrasting

Unclassified

Order By: Relevance

“…Hansen and Wang [30] applied a support vector machine (SVM) using both classification and regression to estimate the SOC without establishing a battery circuit model. SVM transforms a low-dimensional nonlinear problem into a high-dimensional linear problem.…”

Section: State Of Charge (Soc)mentioning

confidence: 99%

Battery Management Systems in Electric and Hybrid Vehicles

et al. 2011

View full text Add to dashboard Cite

Abstract:The battery management system (BMS) is a critical component of electric and hybrid electric vehicles. The purpose of the BMS is to guarantee safe and reliable battery operation. To maintain the safety and reliability of the battery, state monitoring and evaluation, charge control, and cell balancing are functionalities that have been implemented in BMS. As an electrochemical product, a battery acts differently under different operational and environmental conditions. The uncertainty of a battery's performance poses a challenge to the implementation of these functions. This paper addresses concerns for current BMSs. State evaluation of a battery, including state of charge, state of health, and state of life, is a critical task for a BMS. Through reviewing the latest methodologies for the state evaluation of batteries, the future challenges for BMSs are presented and possible solutions are proposed as well.

show abstract

Section: State Of Charge (Soc)mentioning

confidence: 99%

Battery Management Systems in Electric and Hybrid Vehicles

et al. 2011

View full text Add to dashboard Cite

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

“…All of these factors need to be considered when calculating the SoC. Many methods for estimating the SoC have been reported in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For instance, measuring cell voltage to calculate the SoC can work well for lead-acid battery chemistries, for which the cell voltage and SoC are fairly linearly related, but is ineffective for lithium-ion, for which the voltage is mostly flat over the battery discharge curve (except at the extremes, where it is non-linear).…”

Section: Introductionmentioning

confidence: 99%

A New Predictive Model for the State-of-Charge of a High-Power Lithium-Ion Cell Based on a PSO-Optimized Multivariate Adaptive Regression Spline Approach

Anton

Nieto

García–Gonzalo

et al. 2016

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

I. INTRODUCTIONHE rechargeable battery industry is experiencing significant growth driven by an upsurge in portable battery-powered devices, electric vehicles and other industrial applications. A number of different battery chemistries, such as lead-acid, nickel-metal-hydride and lithium-ion, among others, are used in these applications. One of the most popular types of rechargeable battery technologies is the lithium-ion battery. Its chemistry provides a high cell voltage, high energy density, long lifespan and exceptional cyclability. All lithiumion battery applications, especially those used in electric Manuscript received April 30, 2015; revised July 20, 2015. This work was supported in part by the Spanish Science and Innovation Ministry and the Regional Ministry of Principality of Asturias under Grants MINECO-13-DPI2013-46541-R, FC-15-GRUPIN14-073 and MINECO-15-TIN2014-56967-R J. C. Álvarez Antón, J. C. Viera, M. González and C. Blanco Viejo are with the Electrical Engineering Department, University of Oviedo, Campus de Viesques, Gijón, Spain 33204 (+34 985-182553; e-mail: anton@uniovi.es).P. J. Garcia Nieto and E. García Gonzalo are with the Mathematics Department, University of Oviedo, Spain 33007 (e-mail: pjgarcia@uniovi.es).vehicles, require a Battery Management System (BMS). The main objective of a BMS is to maintain the health of all the cells in the battery within the manufacturer's recommended operating conditions in order to prolong the lifespan of the battery pack. One of the most important BMS functional requirements is that of estimating the State-of-Charge (SoC) of the battery or of the individual cells in the battery pack. SoC is an expression of the current battery capacity as a percentage of its maximum capacity. The BMS needs to estimate the SoC in order to report the capacity left in the battery, typically called the "gas gauge". Several applications require accurate measurement of SoC to give users an indication of available runtime. The SoC is also needed to control the battery charging or discharging process. This control can avoid situations such as over-discharging or overcharging, which lead to premature wear-out of the battery. The lithium-ion chemistry operates safely within the designed operating voltages; however, the battery becomes unstable and may pose a safety hazard if overcharged. Over-charging stresses the battery and may lead to damage. Over-discharging also stresses the battery and reduces its lifespan.The available capacity of a battery depends on several factors such as cell chemistry, charging rates, discharging rates and temperature. All of these factors need to be considered when calculating the SoC. Many methods for estimating the SoC have been reported in the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For instance, measuring cell voltage to calculate the SoC can work well for lead-acid battery chemistries, for which the cell voltage and SoC are fairly linearly related, but is ineffective for lithium-ion, for which the volta...

show abstract

Support vector based battery state of charge estimator

Cited by 293 publications

References 3 publications

Battery Management Systems in Electric and Hybrid Vehicles

Battery Management Systems in Electric and Hybrid Vehicles

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

A New Predictive Model for the State-of-Charge of a High-Power Lithium-Ion Cell Based on a PSO-Optimized Multivariate Adaptive Regression Spline Approach

Contact Info

Product

Resources

About