State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

Mejdoubi, Asmae El; Oukaour, Amrane; Chaoui, Hicham; Gualous, Hamid; Sabor, Jalal; Youssef, S.

doi:10.1109/tie.2015.2509916

Cited by 141 publications

(66 citation statements)

References 50 publications

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“…Then, we can simplify the design procedure of model-based adaptive parameter state estimators. The parameters and SOC are both estimated in [27,28]. In [27], the SOC is estimated based on an EKF and the obtained results are satisfactory under various laboratory conditions.…”

Section: Discussionmentioning

confidence: 93%

“…The parameters and SOC are both estimated in [27,28]. In [27], the SOC is estimated based on an EKF and the obtained results are satisfactory under various laboratory conditions. However, obtaining good estimates of the results not only need voltage and current signals, but also the temperature signal.…”

Section: Discussionmentioning

confidence: 93%

“…Then, the accuracy of battery states is estimated based on the equivalent circuit model will degrade when the operating conditions change. Many studies [14,16,[23][24][25][26][27][28] are based on Kalman filter (KF) and its derivatives to deal with the time-varying parameter and system nonlinear problems. The KF is an optimal recursive estimation method for linear systems under the condition that the noises are Gaussian white noises.…”

Section: Introductionmentioning

confidence: 99%

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Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

et al. 2017

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This paper mainly investigates the sensitive characteristics of lithium-ion batteries so as to provide scientific basises for simplifying the design of the state estimator that adapt to various environments. Three lithium-ion batteries are chosen as the experimental samples. The samples were tested at various temperatures (−20 • C, −10 • C, 0 • C , 10 • C , 25 • C) and various current rates (0.5C, 1C, 1.5C) using a battery test bench. A physical equivalent circuit model is developed to capture the dynamic characteristics of the batteries. The experimental results show that all battery parameters are time-varying and have different sensitivity to temperature, current rate and state of charge (SOC). The sensitivity of battery to temperature, current rate and SOC increases the difficulty in battery modeling because of the change of parameters. The further simulation experiments show that the model output has a higher sensitivity to the change of ohmic resistance than that of other parameters. Based on the experimental and simulation results obtained here, it is expected that the adaptive parameter state estimator design could be simplified in the near future.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

et al. 2017

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“…For example, in [45] a discontinuous discharging method to establish OCV-SoC characterization is used to calculate the internal resistance of the model battery and, in combination with an ECM and an extended Kalman observer, estimates the SoC. In [46,47], it is also used for determining the parameters of an ECM of a cell.…”

Section: Impedance Measurement-based Estimationmentioning

confidence: 99%

“…Due to the electrochemical and thermal models of the batteries being highly nonlinear, EKF is used for estimating its parameters, followed by SoC [45,80,90]. The EKF method is also used in combination with CC and/or OCV, as proposed in [11,45,50,103].…”

Section: Real Batterymentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

et al. 2019

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Summary The state of charge and state of health estimations are two of the most crucial functions of a battery management system, which are the quantified evaluation of driving mileage and remaining useful life of electric vehicles. This paper investigates a novel data‐driven–enabled battery states estimation method by combining recurrent neural network modeling and particle‐filtering–based errors redress. First, a recurrent neural network with long‐short time memory is employed to learn the long‐term nonlinear relation between batteries states and measurable signals of lithium‐ion batteries, such as current, voltage, and temperature. Second, to denoise the estimation errors of the neural network model, particle filtering is employed to smooth the state of charge estimation results. Third, the terminal voltage difference of battery is highly related to the internal resistance of the battery, which is thus taken as a new input to track the internal resistance of the battery. The performance of the proposed method is verified by multiple comparisons with conventional techniques under randomized loading profiles and different temperatures.

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State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

Cited by 141 publications

References 50 publications

Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

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