The well-balanced combination of high energy density and competitive cycle performance has established lithium-ion batteries as the technology of choice for Electric Vehicles (EVs) energy storage. Nevertheless, battery degradation continues to pose challenges to EV range, safety, and long-term reliability, making accurate estimation of their State of Health (SoH) crucial for efficient battery management, safety, and improved longevity. This paper addresses a compelling research question surrounding the possibility of developing a real-time, non-invasive, and efficient methodology for estimating lithium-ion battery SoH without battery removal, relying solely on voltage and current data. Our approach integrates the fitting abilities of Maximum Likelihood Estimation (MLE) with the dynamic uncertainty propagation of Bayesian Filtering to provide accurate and robust online SoH estimation. By reconstructing the open-circuit voltage curve from real-time data, the MLE estimates battery capacity during discharge cycles, while Bayesian Filtering refines these estimates, accounting for uncertainties and variations. The methodology is validated using an available dataset from Stanford University, demonstrating its effectiveness in tracking battery degradation under driving profiles. The results indicate that the approach can reliably estimate battery SoH with mean absolute errors below 1%, confirming its suitability for scalable EV applications.

Thermal effects exert a crucial influence on the electrical behavior of lithium-ion batteries, significantly impacting key parameters such as the open circuit voltage curve, internal impedance, and cell degradation rate. Furthermore, these effects may give rise to electrolyte loss, resulting in a reduction in capacity. The cycling of batteries inherently generates internal heat, establishing a direct relationship between cell temperature and power demand. This article aims to provide a methodology to model electrothermal relations and temperature influence on electrical behavior in lithium-ion cells, as well as a simulation of extended cell operation under arbitrary power loads, presenting a novel approach not previously explored. It does this by considering three models: the Bernardi model for heat generation within the cell, a thermal lumped model for the cell’s temperature, and the Vogel-Fulcher-Tammann model for the capacity change as a function of temperature. These models are then connected to a state-of-the-art open circuit voltage model of a cell, providing a connection from the thermal world back into the electrical world. Experiments with different power demands occur on the simulation, including estimation of thermal parameters with relative errors under 1%, visualizing the effects of the integrated models and potential for real-cell applications.

The future behavior of a system is largely determined by its manufacturing process, as variations in production quality can lead to different performance outcomes over time. In the context of prognostics, all sources of uncertainty that exist prior to the system’s commencement of operation are collectively referred to as past uncertainty, yet its role is rarely recognised in the prognostics and health management (PHM) community. Most existing approaches to uncertainty and its management focus only on model parameters, leaving the management of past uncertainty largely unexplored. This work introduces a framework to explicitly manage this source by incorporating manufacturing quality control (MQC) data into hidden semi‑Markov model (HSMM) prognostics. The method creates quality‑specific HSMMs, each tailored to a particular manufacturing quality (MQ) type, and combines them during inference using MQC‑informed Bayesian model averaging.The framework is validated on composite specimens with pristine, oil‑induced, and Teflon‑induced defects. Ultrasonic scans provide MQC inputs, while strain data describes degradation. Results show that accounting for MQ reduces uncertainty in RUL predictions and highlights the importance of correctly identifying the MQ type for effective uncertainty management in prognostics.