Driving behavior-guided battery health monitoring for electric vehicles using machine learning

no code implementations • 25 Sep 2023 • Nanhua Jiang, Jiawei Zhang, Weiran Jiang, Yao Ren, Jing Lin, Edwin Khoo, Ziyou Song

To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions.

Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling

no code implementations • 21 Sep 2023 • Riko I Made, Jing Lin, Jintao Zhang, Yu Zhang, Lionel C. H. Moh, Zhaolin Liu, Ning Ding, Sing Yang Chiam, Edwin Khoo, Xuesong Yin, Guangyuan Wesley Zheng

Battery health assessment and recuperation play a crucial role in the utilization of second-life Li-ion batteries.

Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis

no code implementations • 25 Oct 2021 • Jing Lin, Yu Zhang, Edwin Khoo

Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change.