LAPSE:2019.1504
Published Article
LAPSE:2019.1504
A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries
December 10, 2019
Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical data collected in the experiments are used to train the BP network model, which reveals a test error of 10−4. With the input of continuous SOC regions and discharge currents, continuous-time efficiency can be estimated by the trained BP network model. The estimated and simulated result is proven to be consistent with the experimental results.
Record ID
Keywords
back propagation (BP) neural network, continuous-time efficiency estimation, coulombic efficiency, Energy Efficiency, lithium titanate battery
Subject
Suggested Citation
Liu Y, Zhang L, Jiang J, Wei S, Liu S, Zhang W. A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries. (2019). LAPSE:2019.1504
Author Affiliations
Liu Y: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China; Department of Civil a [ORCID]
Zhang L: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Jiang J: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Wei S: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Liu S: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Zhang W: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
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Zhang L: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Jiang J: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Wei S: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Liu S: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
Zhang W: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China; Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
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Journal Name
Energies
Volume
10
Issue
5
Article Number
E597
Year
2017
Publication Date
2017-04-29
Published Version
ISSN
1996-1073
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Original Submission
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PII: en10050597, Publication Type: Journal Article
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LAPSE:2019.1504
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External Link
doi:10.3390/en10050597
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[v1] (Original Submission)
Dec 10, 2019
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Dec 10, 2019
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v1
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https://psecommunity.org/LAPSE:2019.1504
Original Submitter
Calvin Tsay
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