Work

Predicting temperature of a Li-ion battery under dynamic current using long short-term memory

Year: 2024

Type: article

Abstract: The growing energy demands of modern society have led to an increased reliance on secondary batteries , particularly lithium-ion (Li-ion) batteries, due to their superior energy density and power output . These batteries perform most effectively and safely within a specific temperature range, making it essential to develop accurate models for predicting temperature variations under diverse operational and environmental conditions. In particular, it is crucial to forecast temperature changes resulting from random and dynamic current fluctuations, reflecting real-world usage scenarios while considering the surrounding battery system environment. In this study, we employed a long short-term memory (LSTM) network to develop a surrogate model capable of predicting the battery’s core temperature over time, given varying current loads and heat transfer coefficients . The LSTM model demonstrated remarkable accuracy, achieving an average prediction accuracy of 99% in simulating temperature changes induced by arbitrary currents.

Source: Case Studies in Thermal Engineering

DOI: 10.1016/j.csite.2024.105246

Authors: Jihye Han, Junyong Seo, Jihoon Kim, Yongrack Koo, Miran Ryu, Bong Jae Lee

Institutions: Korea Advanced Institute of Science and Technology, Korea Institute of Energy Research, Samsung (South Korea)

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $1,210