Lithium ion (Li-ion) batteries work as the basic energy storage components in modern\nrailway systems, hence estimating and improving battery efficiency is a critical issue in optimizing\nthe energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion\nbatteries accurately since it varies continuously under working conditions and is unmeasurable via\nexperiments. This paper offers a learning-based simulation method that employs experimental data\nto estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries,\ntaking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge\ncurrent rates are considered as the main variables that may affect the efficiencies. Over eight million\nempirical datasets are collected during a series of experiments performed to investigate the efficiency\nvariation. A back propagation (BP) neural network efficiency estimation and simulation model is\nproposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical\ndata collected in the experiments are used to train the BP network model, which reveals a test error of\n10âË?â??4. With the input of continuous SOC regions and discharge currents, continuous-time efficiency\ncan be estimated by the trained BP network model. The estimated and simulated result is proven to\nbe consistent with the experimental results.
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