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ISSN

2424-8460(Online)

2251-2608(Print)

Article Processing Charges (APCs)

US$800

Publication Frequency

Quarterly

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Published

2025-07-21

Issue

Vol 12 No 2 (2025): published

Section

Articles

Research on estimation method of electric vehicle power battery’s state of charge based on BP neural network

Lesheng Liu

School of Mechanical and Electrical Engineering, Wuhan Business University


DOI: https://doi.org/10.59429/esta.v12i2.10569


Keywords: BP neural network; SOC prediction; Electric vehicles; Battery charged


Abstract

BP neural network algorithm can help the battery charged state data estimation, along with the development of the car networking and cloud platform technology, more and more state of the electric car manufacturers of electric vehicles in the vehicle and the battery status information through the car terminal uploaded to the cloud platform, through the analysis of the data of upload, the staff can better grasp the status of the vehicle, early warning to the potential risk of electric cars.If the current SOC is predicted using the batteryrelated data on the cloud platform and verified with the SOC estimated by BMS, it is of great significance for the online diagnosis of the data monitoring cloud platform.


References

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[2]Sun H H, Bi J, Shao S. The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter [J]. Advanced Materials Research, 2014, 953-954: 796-799.

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[7]Li J, Liu M. SOC estimation for lithium batteries based on the full parallel nonlinear autoregressive neural network with external inputs [J]. Journal of Renewable and Sustainable Energy, 2018, 10(6).

[8]Wu Z, Shang M, Shen D, et al. Prediction of SOC of lead-acid battery in pure electric vehicle based on BSA-RELM [J]. Journal of Renewable and Sustainable Energy, 2018, 10(5).

[9]B, Ufnalski, L. M, et al. Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle [J]. Bulletin of the Polish Academy of Sciences: Technical Sciences, 2012, 60(3): 661-667.

[10]Min K, Yeon K, Jo Y, et al. Vehicle Deceleration Prediction Based on Deep Neural Network at Braking Conditions[J]. International Journal of Automotive Technology, 2020, 21(1): 91-102.



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