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.
Highlights
Lithium ion (Li-ion) batteries exhibit better performance with regard to energy density [1,2], power density [3], life cycle [4], operating temperature range [5], and safety [6,7] when compared with other types of rechargeable batteries, such as lead-acid batteries [8], nickel-cadmium batteries [1,9], and nickel-metal hydride (Ni-MH) batteries [2]
Lithium ion batteries are widely used as basic energy storage components in transportation applications [10,11,12]
This paper is organized as follows: Section 2 presents the theoretical calculation method for the energy efficiency and coulombic efficiency of lithium ion batteries; Section 3 offers basic information about the experiments and lists the properties of the lithium titanate batteries used in this research; Section 4 shows the experimental results of efficiency variation based on different state of charge (SOC) regions and discharge current rates when batteries are fully charged and not fully discharged; in Section 5, the experimental results of efficiency variation when the batteries are fully discharged and not fully charged are presented; Section 6 shows the back propagation (BP) network continuous-time efficiency estimation model and, in the final part of this section, the estimated efficiency is proven to be consistent with the experimental results
Summary
Yuechen Liu 1,2,3 , Linjing Zhang 1,2 , Jiuchun Jiang 1,2, *, Shaoyuan Wei 1,2 , Sijia Liu 1,2 and.
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