Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology

Salkind, Alvin J.; Fennie, Craig J.; Singh, Pritpal; Atwater, Terrill B; Reisner, David

doi:10.1016/s0378-7753(99)00079-8

Cited by 393 publications

(182 citation statements)

References 5 publications

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“…One of the model based methods for SOC estimation is based on the black-box battery models, such as neural networks (NN) [8], fuzzy logic (FL) [9], and support vector machine (SVM) [10]. Eddahech et al [8] developed a recurrent neural network as a SOC predictor that takes into account operational conditions, the results show that the predictor allows very precise SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Eddahech et al [8] developed a recurrent neural network as a SOC predictor that takes into account operational conditions, the results show that the predictor allows very precise SOC estimation. Salkind et al [9] utilized the fuzzy logic to estimate the battery SOC by using the training datasets obtained by impedance spectroscopy and coulomb counting techniques. Anton et al introduced a support vector machine based SOC estimator for a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell, using cell current, cell voltage, and cell temperature as independent variables.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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“…In this paper, we used the combined mode which consists of Sherpherd Model, Unnewehr Model and Nernst Model. The model can be viewed as a combination of the previous three models to obtain the most accurate performance [8,9].…”

Section: Battery Modelmentioning

confidence: 99%

SOC Estimation Based Combined Model For Vehicle Batteries

Gu¹,

Fu²,

Dongyong³

et al. 2015

Advances in Intelligent Systems Research

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Abstract. Batteries play an essential role in electric vehicles, In order to achieve an optimum operation of systems with batteries it is necessary to estimate accurate of the state of charge (SOC). In this paper a combined model for SOC estimation in Vehicle Batteries, based on extended Kalman filter (EKF) is presented. The influence of the environmental temperature and charge-discharge rate are considered in the combined model, which makes it suitable for the acute change of current in driving. The effectiveness of the proposed method is verified using an Extensive experiment. This approach has strong capacity of resisting disturbance and will be implemented easily by hardware for an online SOC estimation.

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“…The existing SOC estimation algorithms can be divided into two categories, namely non-model-based type and model-based type. The former is typically based on Ampere-hour (Ah) or Coulomb counting [1,2], open-circuit voltage (OCV) [3][4][5], electrochemical impedance spectroscopy (EIS) [6,7], artificial neural networks (ANNs) [8][9][10][11] and fuzzy-logic (FL) [12,13]. The Ah counting method acquires the SOC by integrating the current over the time.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology

Cited by 393 publications

References 5 publications

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

SOC Estimation Based Combined Model For Vehicle Batteries

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

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