Lithium‐ion batteries (LIBs) are vital energy‐storage devices in modern society. However,
the performance and cost are still not satisfactory in terms of energy density, power density …

Abstract Machine learning is a specific application of artificial intelligence that allows
computers to learn and improve from data and experience via sets of algorithms, without the …

Due to the significantly complex and nonlinear behavior of li-ion batteries, forecasting the
state of charge (SOC) of the batteries is still a great challenge. Therefore, accurate SOC …

The ongoing electrification of mobility comes with the challenge of charging electric vehicles
(EVs) sufficiently while charging infrastructure capacities are limited. Smart charging …

This paper proposes a novel, informed deep-learning-based prognostics framework for on-
board state of health and remaining useful life estimations of lithium-ion batteries, which are …

Accurate estimation of state of charge (SOC) is crucial for battery management system
(BMS). Since most of the existing estimation methods are based on laboratory data, the …

We have proposed a data-driven approach for designing the mesoscale porous structures of
Li-ion battery electrodes, using three-dimensional virtual structures and machine learning …

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as
well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional …

Numerical physics-based models for Li-ion batteries under abuse conditions are useful in
understanding failure mechanisms and deciding safety designs. Since battery design is …

The battery system is one of the key components of electric vehicles (EV) which has brought
groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to …