Work

Predicting battery degradation profiles of IoT device usage modes through Machine Learning utilization models

Year: 2024

Type: article

Abstract: IoT-enabled devices powered by rechargeable batteries often need to be compact, which results in using low-capacity batteries that require frequent charging. The high cycle count leads to noticeable battery health degradation. Therefore, understanding how different usage modes of IoT devices affect the State of Health (SoH) of the battery is crucial. Energy consumption and battery degradation vary significantly across usage modes, making it important to identify the specific impact each mode has on the battery health. Traditional lab-based testing is often time-consuming, costly, and may not accurately reflect real-world usage patterns. This paper introduces a systematic procedure that leverages established Machine Learning (ML) utilization models to reveal degradation profiles associated with various usage modes of IoT devices and their impact on battery health. These degradation profiles illustrate how the runtime in a given usage mode influences the SoH of a battery. This insight allows product developers to optimize existing usage modes and improve the efficiency of next-generation IoT products. Additionally, these degradation profiles enable accurate predictions of the battery’s Remaining useful Lifetime (RuL) by providing a measure for the decrease in SoH based on the runtime of a specific usage mode.

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2024.115062

Authors: Jonas Bokstaller, Johannes Schneider

Institution: University of Liechtenstein

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,030