Prediction of Li-Ion Battery State of Charge Using Multilayer Perceptron and Long Short-Term Memory Models

Asadullah Khalid, Aditya Sundararajan, Ipsita Acharya, Arif I. Sarwat

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

57 Scopus citations

Abstract

Lithium-ion batteries are used in different applications such as electric vehicles and grid-scale energy storage. These applications rely greatly on the accurate measurement and prediction of state of charge (SOC) to ascertain the battery's available capacity. Although multiple methods exist in the literature to predict SOC and other battery parameters, they have low accuracy, make offline predictions, and do not consider enough battery parameters. The battery's nonlinear characteristics and time-variance have direct impacts on the applications connected to it, which make the prediction a complex but more necessary problem to solve. This paper bridges the gap by comparing the performance of two widely used data-driven learning models: long short-term memory (LSTM) and a multilayer perceptron (MLP), for predicting SOC using predictors such as cell current, cell voltage, elapsed time, and cell temperature. The models are run using mean squared error as the loss function, and different loss function optimizers. The models are also applied to datasets from different charging/discharging rates to demonstrate their efficacy. A recommendation is finally made on the model for SOC prediction subject to specific conditions considered in the paper, and the groups of optimizers that work better to minimize the loss function.

Original languageEnglish
Title of host publicationITEC 2019 - 2019 IEEE Transportation Electrification Conference and Expo
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538693100
DOIs
StatePublished - Jun 2019
Externally publishedYes
Event2019 IEEE Transportation Electrification Conference and Expo, ITEC 2019 - Novi, United States
Duration: Jun 19 2019Jun 21 2019

Publication series

NameITEC 2019 - 2019 IEEE Transportation Electrification Conference and Expo

Conference

Conference2019 IEEE Transportation Electrification Conference and Expo, ITEC 2019
Country/TerritoryUnited States
CityNovi
Period06/19/1906/21/19

Funding

The material published is a result of the research supported by the National Science Foundation (NSF) under the grant number 1553494, CNS division.

Keywords

  • Lithium Ion
  • Long Short-Term Memory (LSTM)
  • Loss Function Optimizer and Battery Management System
  • Multilayer Perceptron (MLP)
  • State of Charge (SOC)

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