SOH prediction of lithium battery based on IC curve feature and BP neural network

Wen, Jianping; Li, Xianghe; Li, Yikun

doi:10.1016/j.energy.2022.125234

Cited by 118 publications

(34 citation statements)

References 25 publications

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“…They cannot be used to determine whether a battery with EoL power remains healthy throughout its full life cycle [40]. Monitoring and predicting the battery's state of health (SOH) is extremely important for the future intelligent battery management system (BMS) [89]. Intensive research has been conducted on evaluating SOH in commercial power batteries [90].…”

Section: Theoretical Basis Of the Recycle Systemmentioning

confidence: 99%

Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective

Nie

Wang

et al. 2023

IJERPH

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Developing new energy vehicles (NEVs) is necessary to grow the low-carbon vehicle industry. Many concentrated end-of-life (EoL) power batteries will cause large-scale environmental pollution and safety accidents when the time comes to replace the first generation of batteries if improper recycling and disposal methods are utilized. Significant negative externalities will result for the environment and other economic entities. When recycling EoL power batteries, some countries need to solve problems about lower recycling rates, unclear division of echelon utilization scenarios, and incomplete recycling systems. Therefore, this paper first analyzes representative countries’ power battery recycling policies and finds out the reasons for the low recycling rate in some countries. It is also found that echelon utilization is the critical link to EoL power battery recycling. Secondly, this paper summarizes the existing recycling models and systems to form a complete closed-loop recycling process from the two stages of consumer recycling and corporate disposal of batteries. The policies and recycling technologies are highly concerned with echelon utilization, but few studies focus on analyzing application scenarios of echelon utilization. Therefore, this paper combines cases to delineate the echelon utilization scenarios clearly. Based on this, the 4R EoL power battery recycling system is proposed, which improves the existing recycling system and can recycle EoL power batteries efficiently. Finally, this paper analyzes the existing policy problems and existing technical challenges. Based on the actual situation and future development trends, we propose development suggestions from the government, enterprises, and consumers to achieve the maximum reused of EoL power batteries.

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Section: Theoretical Basis Of the Recycle Systemmentioning

confidence: 99%

Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective

Nie

Wang

et al. 2023

IJERPH

View full text Add to dashboard Cite

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“…However, this causes time-consuming problems and is inflexible. From the present research, BP neural network [26], support vector regression (SVR) [27], and Gaussian process regression (GPR) [28] have been employed for battery capacity estimation combined with meaningful HFs.…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

Liu

Xia

2023

International Journal of Energy Research

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Health monitoring is an essential task for lithium battery systems. Recently, with the development of data-driven methods, deep learning has been successfully deployed for state-of-health (SOH) estimation. However, existing models trained using raw samples directly usually contain noise due to sensor errors. To enhance the performance of SOH prediction, short-term segments are extracted for SOH estimation based on reasonable SOC ranges. To address the measuring error that exists in the voltage and temperature samples, the reconstructed feature series (RFSs) is designed to restrain the signals’ noise. Then, a CNN-GRU network with attention mechanism is proposed to achieve SOH estimation based on short-term RFSs’ samples. To further enhance accuracy, a parallel structure is designed to fuse the feature information from both streams, raw samples, and RFSs in a reasonable manner. The performance of our proposed method is validated over a wide range of experiments on the Oxford battery degradation dataset, where the RMSE and MAE averaged 0.582% and 0.524%, respectively, demonstrating its forward estimation performance.

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“…To ensure the safety and reliability of the battery system, the monitoring of the basic state of the battery are necessary, including the monitoring of the state of charge (SOC), [8][9][10][11] states of power, 12,13 and state of health. [14][15][16][17] According to the experimental data obtained, the health status and available power of LIBs are significantly affected by temperature. Considering the particular working environment of AUVs power battery packs, such as confined space, heat dissipation becomes a challenge to the battery working under high temperatures.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, LIBs are used as the power system of most AUVs. To ensure the safety and reliability of the battery system, the monitoring of the basic state of the battery are necessary, including the monitoring of the state of charge ( SOC ), 8–11 states of power, 12,13 and state of health 14–17 . According to the experimental data obtained, the health status and available power of LIBs are significantly affected by temperature.…”

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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SOH prediction of lithium battery based on IC curve feature and BP neural network

Cited by 118 publications

References 25 publications

Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective

Literature Review on Power Battery Echelon Reuse and Recycling from a Circular Economy Perspective

State-of-Health Estimation for Lithium-Ion Battery Based on an Attention-Based CNN-GRU Model with Reconstructed Feature Series

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

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