[Poster Presentation]A comparison of charging voltage image coding methods for lithium-ion battery state of health estimation - International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence ((ICSMD2024))

A comparison of charging voltage image coding methods for lithium-ion battery state of health estimation

ID：173 Submission ID：183 View Protection：ATTENDEE Updated Time：2024-10-23 10:02:36 Hits：70 Poster Presentation

Start Time：Pending （Asia/Shanghai）

Duration：Pending

Session：[No Session] » [No Session Block]

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Abstract

Accurate estimation of the state of health (SOH) of lithium-ion batteries is a key initiative to guarantee their service reliability in complex operating environments. Using one-dimensional time series data to transform two-dimensional image for battery degradation feature extraction can improve the accuracy of battery SOH evaluation, reduce the complexity of evaluation model and the demand for the amount of test data. Although existing studies have attempted to apply image coding techniques to enhance the degradation features of original data, the advantages and disadvantages of different image coding methods have not been systematically compared. Therefore, in this work, five commonly used image coding methods including recurrence plots, Gramian angular summation field, Gramian angular difference field, relative position matrix, and time series data folding are selected and comprehensively compared. Firstly, the original one-dimensional voltage signal is encoded into a two-dimensional image, which is then inputted into the CNN-GRU-based SOH prediction model, and finally the future battery SOH value is output. The experimental results show that there are differences in the applicable stages and conditions of different coding methods, so they need to be adapted with specific application scenarios, which is the next research direction.

Keywords

State of health estimation, lithium-ion battery, charging voltage image coding

Speaker

WangHang

Dr. Anhui University

Submission Author

WangHang Anhui University

ZhouYuanyuan Anhui University

FanZhongding Anhui University

HuZhiyong Anhui University

MaoLei University of Science and Technology of China

LiuYongbin Anhui University

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Important Dates

~~15th August~~ ~~2024~~ 31st August 2024- Manuscript Submission

15th September 2024 - Acceptance Notification

1st October 2024 - Camera Ready Submission

1st October 2024 – Early Bird Registration

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