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Modeling and Analysing of Lithium Ion Batteries for Energy Storage and Electric Vehicle (EV) Applications

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Dr. Jingying Xie

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State Key Laboratory of Space Power-Sources Technology, Shanghai Institute of Space Power-Sources, Shanghai 200245, China
Interests: sodium-ion batteries; lithium-ion batteries; battery energy storage systems; battery management systems; battery modeling

Prof. Dr. Haifeng Dai

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Guest Editor

School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Jiading District, Shanghai 201804, China
Interests: electrical/hybrid vehicles; energy storage and battery management system; wireless power transfer
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Dear Colleagues,

The energy transition is pushing the world toward the trend of electrification. For the transitions of power system electrification and transportation electrification, lithium-ion batteries are widely used in the field of energy storage systems and electric vehicles, whether as an auxiliary service provider or energy supplier, due to their advantages in cycle performance, stability and safety. It is a key priority that the iterative update of battery management technology must make the use of lithium-ion batteries safer and more reliable. Fortunately, the development of smart sensors makes the detection of multi-dimensional and multi-scale signals of batteries achievable, which is helpful for analyzing internal battery information in more detail.

Based on measured massive data, the behavior characteristics and changes of battery states are should be better monitored. Therefore, the aim of this Special Issue is to explore the mechanisms of lithium-ion batteries, as well as multi-dimensional battery modeling and analysis methods.

Dr. Jingying Xie
Prof. Dr. Haifeng Dai
Guest Editors

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Keywords

intelligent sensors for lithium-ion batteries
advanced modeling methods for lithium-ion battery systems
the estimation of states of lithium-ion batteries, such as SOC, SOP, SOH and temperatures
research on battery aging mechanisms and RUL predictions
thermal simulation and thermal management of lithium-ion batteries
thermal failure mechanisms and thermal runway detection
digital tween for lithium-ion batteries
advanced methods for battery fault diagnosis and prognostics
advanced methods for battery pack management and control

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Research

20 pages, 5531 KiB

Open AccessArticle

Prediction of Remaining Useful Life of Lithium Batteries Based on WOA-VMD and LSTM

by Mingsan Ouyang and Peicheng Shen

Energies 2022, 15(23), 8918; https://doi.org/10.3390/en15238918 - 25 Nov 2022

Cited by 11 | Viewed by 1921

Abstract

The remaining useful life (RUL) of a lithium-ion battery is directly related to the safety and reliability of the electric system powered by a lithium-ion battery. Accurate prediction of RUL can ensure timely replacement and maintenance of the batteries of the power supply system, and avoid potential safety hazards in the lithium-ion battery power supply system. In order to solve the problem that the prediction accuracy of the RUL of lithium-ion batteries is reduced due to the local capacity recovery phenomenon in the process of the capacity degradation of lithium-ion batteries, a prediction model based on the combination of the whale optimization algorithm (WOA)-variational mode decomposition (VMD) and short-term memory neural network (LSTM) was proposed. First, WOA was used to optimize the VMD parameters, so that the WOA-VMD could fully decompose the capacity signal of the lithium-ion battery and separate the dual component with global attenuation trend and a series of fluctuating components representing the capacity recovery from the capacity signal of the lithium-ion battery. Then, LSTML was used to predict the dual component and fluctuation components, so that LSTM could avoid the interference of the capacity recovery to the prediction. Finally, the RUL prediction results were obtained by stacking and reconstructing the component prediction results. The experimental results show that WOA-VMD-LSTM can effectively improve the prediction accuracy of the RUL of lithium-ion batteries. The average cycle error was one cycle, the average RMSE was less than 0.69%, and the average MAPE was less than 0.43%. Full article

(This article belongs to the Special Issue Modeling and Analysing of Lithium Ion Batteries for Energy Storage and Electric Vehicle (EV) Applications)

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18 pages, 1246 KiB

Open AccessArticle

An Adaptive Noise Reduction Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

by Wenyu Qu, Guici Chen and Tingting Zhang

Energies 2022, 15(19), 7422; https://doi.org/10.3390/en15197422 - 10 Oct 2022

Cited by 8 | Viewed by 1659

Abstract

Lithium-ion batteries are widely used in the electric vehicle industry due to their recyclability and long life. However, a failure of lithium-ion batteries can cause some catastrophic accidents, such as electric car battery explosion fires and so on. To prevent such harm from occurring, it is essential to monitor the remaining useful life of lithium-ion batteries and give early warning. In this paper, an adaptive noise reduction approach is proposed to predict the RUL (Remaining Useful Life) of lithium-ion batteries, which uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) combined with wavelet decomposition to achieve adaptive noise reduction decomposition, and then inputs the obtained IMF (Intrinsic Mode Function) components into LS–RVM (Least Square Relevance Vector Machine) for training, prediction, and reconstruction, so as to achieve high-precision prediction of RUL. Moreover, in order to verify the validity of the model, the model in this paper is compared with other common models. The results demonstrate that the RMSE, MAPE, and MAE of the proposed model are 0.008678, 0.005002, and 0.006894, and that it has higher accuracy than the other common prediction models. Full article

(This article belongs to the Special Issue Modeling and Analysing of Lithium Ion Batteries for Energy Storage and Electric Vehicle (EV) Applications)

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