On battery State of Charge estimation: A new mixed algorithm

Codecà, Fabio; Savaresi, Sergio M.; Rizzoni, Giorgio

doi:10.1109/cca.2008.4629635

Cited by 75 publications

(35 citation statements)

References 5 publications

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“…The combination of RC and hysteresis models was proposed to compensate deficiencies of the individual models [28]. Coulomb counting and EKF based estimation were integrated to achieve better performance [29][30][31]. An very accurate result that estimation error was less than 1 min in left time or 1% in SoC was also obtained under the together contributions of direct measurement of the electro-motive force and book-keeping algorithm [32], though it is not specially designed for EV application.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yan

Qian

et al. 2010

Energies

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Yan

Qian

et al. 2010

Energies

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“…4. This model is an enhanced Thevenin model with two time constant behaviors [24]- [26]. The dynamic model for this circuit is given by…”

Section: Kwmentioning

confidence: 99%

On Thermal and State-of-Charge Balancing using Cascaded Multi-level Converters

Altaf¹,

Johannesson²,

Egardt³

2013

Journal of Power Electronics

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In this study, the simultaneous use of a multi-level converter (MLC) as a DC-motor drive and as an active battery cell balancer is investigated. MLCs allow each battery cell in a battery pack to be independently switched on and off, thereby enabling the potential non-uniform use of battery cells. By exploiting this property and the brake regeneration phases in the drive cycle, MLCs can balance both the state of charge (SoC) and temperature differences between cells, which are two known causes of battery wear, even without reciprocating the coolant flow inside the pack. The optimal control policy (OP) that considers both battery pack temperature and SoC dynamics is studied in detail based on the assumption that information on the state of each cell, the schedule of reciprocating air flow and the future driving profile are perfectly known. Results show that OP provides significant reductions in temperature and in SoC deviations compared with the uniform use of all cells even with uni-directional coolant flow. Thus, reciprocating coolant flow is a redundant function for a MLC-based cell balancer. A specific contribution of this paper is the derivation of a state-space electro-thermal model of a battery submodule for both uni-directional and reciprocating coolant flows under the switching action of MLC, resulting in OP being derived by the solution of a convex optimization problem.

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“…Among them, Ah method is one of the most commonly used methods. It can be simply implemented by integrating the battery current over time [2]. However, the reliability of SOC estimation cannot be maintained when errors accumulate over the operation time or the initial value of SOC is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods of estimating the battery SOC have been proposed including ampere-hour counting (Ah), open circuit voltage (OCV), artificial neural networks (ANN), fuzzy logic (FL), particle filter (PF), and improved Kalman filters [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Among them, Ah method is one of the most commonly used methods.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

Luping

Wang

2017

Mathematical Problems in Engineering

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The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

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On battery State of Charge estimation: A new mixed algorithm

Cited by 75 publications

References 5 publications

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

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

On Thermal and State-of-Charge Balancing using Cascaded Multi-level Converters

State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

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