Battery health monitoring methods including machine learning (ML) models rely on
trustworthiness of battery sensor data and features. As more battery systems require network …

Neural networks are vulnerable to malicious data poisoning attacks, injecting imperceptible
noise into features, severely compromising predictive model accuracy. This challenge is …

Precisely assessing the state of health (SOH) has emerged as a critical approach to
ensuring the safety and dependability of lithium-ion batteries. One of the primary issues …

Modern battery systems exhibit a cyber-physical nature due to the extensive use of
communication technologies in battery management. This makes modern cyber-physical …

Neural networks are subject to malicious data poisoning attacks affecting the ability of the
model to make accurate predictions. The attacks are generated using adversarial …

An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe
and reliable operation of electric vehicles (EVs). Feature-based machine learning methods …

Health monitoring of lithium-ion batteries is a major task to ensure the performance and
reliability of electronic vehicles. A precise state of health prediction is still a challenging …

Battery health assessment is crucial for the safe and stable operation of electric vehicles.
Accurate and efficient estimation state of health (SOH) ensures effective battery …

Battery State of Health (SoH) estimation is critical for battery management systems, and
traditional machine learning approaches face challenges in terms of prediction accuracy …

In order to solve the problem of inaccurate estimation of the state of health (SOH) of electric
vehicle batteries, this paper proposes a novel SOH estimation algorithm based on particle …