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Energies 2017, 10(5), 597; doi:10.3390/en10050597

A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries

1
National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
2
Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing 100044, China
3
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Peter J S Foot
Received: 17 March 2017 / Revised: 21 April 2017 / Accepted: 25 April 2017 / Published: 29 April 2017
View Full-Text   |   Download PDF [5431 KB, uploaded 29 April 2017]   |  

Abstract

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. View Full-Text
Keywords: lithium titanate battery; energy efficiency; coulombic efficiency; back propagation (BP) neural network; continuous-time efficiency estimation lithium titanate battery; energy efficiency; coulombic efficiency; back propagation (BP) neural network; continuous-time efficiency estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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. Energies 2017, 10, 597.

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