Adaptive techniques used for lifetime estimation of lithium-ion batteries

Khayat, Nachaat; Karami, Nabil

doi:10.1109/eecea.2016.7470773

Cited by 17 publications

(16 citation statements)

References 12 publications

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“…Adaptive systems usually use feedback to change the current output according to the varying input [13].…”

Section: Adaptive Filter-based Estimation Methodsmentioning

confidence: 99%

“…For example, in a HEV, the input conditions are always changing and simulating these inputs will be hard. On the other hand, ANN is not effective in extrapolation, which limits its ability to calculate the remaining charge time of a battery [13].…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

“…A short (13 references) review of adaptive techniques including the KF, Artificial Neural Networks (ANNs), and Fuzzy Logic (FL) systems was presented [13].…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

“…The operating principle of a FL technique can be structured into four simple stages [13]: (1) Fuzzification: in this stage the measured system values are converted into linguistic fuzzy sets, and classified into membership functions. Fuzzy sets specify the degree of belonging to each logical set; (2) Fuzzy rule base: a fuzzy rule base is designed using professional experience and system operating method; (3) Inference Engine: through this stage all the fuzzy rules are transformed into fuzzy linguistic outputs; (4) Defuzzification: translates the linguistic fuzzy rules into analog output values.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

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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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“…Adaptive systems usually use feedback to change the current output according to the varying input [13].…”

Section: Adaptive Filter-based Estimation Methodsmentioning

confidence: 99%

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

“…A short (13 references) review of adaptive techniques including the KF, Artificial Neural Networks (ANNs), and Fuzzy Logic (FL) systems was presented [13].…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

See 2 more Smart Citations

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

2017

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“…Therefore, it is the main indicator of the system state and its knowledge allows the system security level to be increased (avoiding overcharge and over-discharge situations) and the success rate of optimization algorithms oriented to the maximum performance exploitation to be improved [1][2][3]. SoC estimation methods are classified in [4] as direct methods (such as open circuit voltage estimation, Coulomb counting, and electrochemical impedance methods) [5][6][7]) and indirect methods (such as those based on artificial intelligence, adaptive filters, and models [8][9][10][11]). In [4], the advantages and disadvantages of the different methods and the related open issues are also analyzed.…”

Section: Introductionmentioning

confidence: 99%

Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

et al. 2019

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In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers.

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Performance evaluation of lithium battery pack based on MATLAB simulation with lumped parameter thermal model

Zeng

Pan

2023

Energy Science & Engineering

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The capacity of lithium batteries varies under different temperature conditions. However, many studies still neglect the influence of temperature on battery capacity. Besides, some studies only considered the combination of the heat and electricity of the battery monomer and failed to study the performance of the battery pack in groups. This paper investigates a new method for dynamic situations that takes the influence of temperature on battery capacity into consideration. After acquiring the parameter through experiment, we can substitute the performance parameters into the thermoelectric coupling model and use MATLAB to realize the iterative calculation of thermoelectric coupling. By combining the grouping mode with the model, we can simulate the thermodynamic and electrical properties of the battery cell in the pack. The novelty is evident because the paper focused on the battery pack instead of a single cell and the lumped parameter thermal model is adopted. Besides, the method proposed in this paper considered the influence of temperature on battery capacity. The program of calculation process can be solidified on the vehicle system. After the simulation, we can get the available capacity of the battery pack at different initial temperatures, with different grouping modes and different inconsistency of parameters. The results predicted by MATLAB model are compared with the results of MATLAB‐FLUENT thermoelectric coupling simulation. The result reflects that the prediction by the MATLAB model about the available capacity, temperature, and residual electricity is of good precision.

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Adaptive techniques used for lifetime estimation of lithium-ion batteries

Cited by 17 publications

References 12 publications

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

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

Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect

Performance evaluation of lithium battery pack based on MATLAB simulation with lumped parameter thermal model

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