An approach to determine the state of charge of a lithium iron phosphate cell using classification methods based on frequency domain data

Jansen, Patrick; Gebel, T.; Renner, Daniel; Vergossen, David; John, W.; Götze, J.

doi:10.1049/cp.2016.0312

Cited by 4 publications

(4 citation statements)

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“…The mathematical junction between the input and the output layer is realized by a hidden layer and its neurons, where the neurons are interconnected as in Figure 12. Every neuron except the inputs consist of a sum of the products of the output O pred of the neurons in the predecessor layer and a particular weight W pred,actual between the neurons of the predecessor and the actual layer [129]: There are two basic architectures of ANNs: feed-forward networks and feedback (recurrent) networks. Meanwhile, there are three main training scenarios for ANNs: supervised, unsupervised, and hybrid.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

“…In [129] a BPNN was used for the SoC determination based on frequency domain data. Here, the real and imaginary parts of the measured impedance, plus the frequency of each measurement are used as X1, X2, and X3 inputs of a three-input BPNN like that presented in Figure. The corresponding output layer provides with its neurons the dedicated SoC classes {c i } corresponding to the SoC specific impedance spectra.…”

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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Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

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

2017

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“…The field of battery cell modeling and state estimation is vast, and numerous approaches with varying degrees of benefits have been developed in the past [18][19][20][21][22][23][24] Because of their simplicity and ability to capture battery dynamics, equivalent circuit model-based techniques such as Thevenin model, 25,26 the Partnership for a new generation of Vehicles model, 27,28 and the double polarization model have been popular. However, in terms of material qualities and the physical structure of the cell, these models are often difficult to comprehend.…”

Section: Introductionmentioning

confidence: 99%

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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“…The LFP battery has a wide voltage platform within the 20% to 90% SOC range, and the OCV difference within the voltage platform range is no more than 20 mV. The flatness of the middle section of the SOC-OCV curve, which makes system observability poor, will affect the rate of convergence of iterative algorithms such as the Kalman filter, and also reduces the accuracy of the initial SOC value acquired by the OCV-based correction method [13,14]. In addition, LFP batteries have hysteresis characteristics [15], path dependence [16], and memory effects [17].…”

Section: Introductionmentioning

confidence: 99%

A SOC Correction Method Based on Unsynchronized Full Charge and Discharge Control Strategy in Multi-Branch Battery System

Huang,

Kang,

Zhao

et al. 2023

Energies

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LiFePO4 batteries exhibit voltage plateau and voltage hysteresis characteristics during charging and discharging processes; however, the estimation of state-of-charge relies on voltage detection. Thus, the estimation accuracy of SOC is low in a traditional method. In this paper, a full charge and discharge SOC correction method is proposed; i.e., the SOC is corrected to 100% when the battery is fully charged and to 0% when fully discharged, and the actual usable capacity is corrected using the fully discharged capacity after being fully charged. Thereby, the cumulative error of the ampere-hour integration method is dynamically corrected. In engineering applications, however, the battery systems do not always undergo full charge and discharge cycling due to the operating conditions. By making full use of the distributed control characteristics of the multi-branch topology battery system, the present work proposes an optimized system control strategy to realize the unsynchronized full charge and discharge cluster by cluster, which extends the application of the full charge and discharge SOC correction method. The experimental results verify the accuracy of the proposed SOC correction method and the feasibility of the control strategy. A more reliable and efficient battery management scheme is provided for LFP battery system, which has high practical value in engineering.

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An approach to determine the state of charge of a lithium iron phosphate cell using classification methods based on frequency domain data

Cited by 4 publications

References 0 publications

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

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

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

A SOC Correction Method Based on Unsynchronized Full Charge and Discharge Control Strategy in Multi-Branch Battery System

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