research-article

Prediction of Remaining Time of Tram Charging Based on Bi-GRU

Authors:

Hongfeng Yang,

Shaofei WuAuthors Info & Claims

IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers

Pages 521 - 524

https://doi.org/10.1145/3544109.3544201

Published: 18 July 2022 Publication History

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Abstract

In the previous prediction of the remaining charging time of automobile battery, it is not only limited by the difficulty of feature acquisition, but also has the problem of unidirectional prediction. To solve the above problems, a prediction method of tram charging remaining time based on BRNN is proposed is Bi-GRU. Through the analysis of battery charging process data, based on the previous input characteristics, part from the charging curve is added as the feature input to train the model. The battery data in line with GB / T 32960 collected from the electric vehicles management system of a company is used as the data set. Compare with the conventional LSTM and SVR models, Bi-GRU prediction results perform better in accuracy and stability.

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Mou JYu CDuan PLi JZhang CLiu YLiu XGarg ASu S(2024)Multi-Scenario Deep Learning-Based Framework to Estimate the Remaining Charge Time of Lithium-Ion Power BatteriesCHAIN10.23919/CHAIN.2024.0000021:3(229-248)Online publication date: Sep-2024
https://doi.org/10.23919/CHAIN.2024.000002
Zhang QLi ZDong WWei SLiu YZuo M(2023)A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different EnvironmentsInternational Journal of Environmental Research and Public Health10.3390/ijerph2005412020:5(4120)Online publication date: 25-Feb-2023
https://doi.org/10.3390/ijerph20054120
Li ZZhang QDong WLiu YWei SZuo M(2023)FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage EnvironmentsFoods10.3390/foods1208168112:8(1681)Online publication date: 18-Apr-2023
https://doi.org/10.3390/foods12081681

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IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers

April 2022

1065 pages

ISBN:9781450395786

DOI:10.1145/3544109

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 18 July 2022

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IPEC2022: 2022 3rd Asia-Pacific Conference on Image Processing, Electronics and Computers

April 14 - 16, 2022

Dalian, China

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Mou JYu CDuan PLi JZhang CLiu YLiu XGarg ASu S(2024)Multi-Scenario Deep Learning-Based Framework to Estimate the Remaining Charge Time of Lithium-Ion Power BatteriesCHAIN10.23919/CHAIN.2024.0000021:3(229-248)Online publication date: Sep-2024
https://doi.org/10.23919/CHAIN.2024.000002
Zhang QLi ZDong WWei SLiu YZuo M(2023)A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different EnvironmentsInternational Journal of Environmental Research and Public Health10.3390/ijerph2005412020:5(4120)Online publication date: 25-Feb-2023
https://doi.org/10.3390/ijerph20054120
Li ZZhang QDong WLiu YWei SZuo M(2023)FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage EnvironmentsFoods10.3390/foods1208168112:8(1681)Online publication date: 18-Apr-2023
https://doi.org/10.3390/foods12081681

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Expert deep learning techniques for remaining useful life prediction of diverse energy storage Systems: Recent Advances, execution Features, issues and future outlooks

A review of expert hybrid and co-estimation techniques for SOH and RUL estimation in battery management system with electric vehicle application

Efficient design of an operation profile for wireless charging electric tram systems

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