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.

Lithium-ion battery (LIB) lifetime prognosis remains a decisive enabler for safe, economical, and sustainable electrification across mobility and stationary energy sectors. Despite the maturity of this storage technology, the impact of capacity reduction due to battery aging remains a subject of interest to this day. This study presents a degradation model that computes the incremental loss of usable capacity for every equivalent cycle experienced by the battery. The model depends on the State of Charge values and the mean ambient temperature at which the cycle occurred. The methodology generalizes the results of a previous study where an initial degradation model was calibrated on long‑term cycling data at which multiple commercial cells were driven to their end of life under varied state of charge ranges in addition to a chemical analysis of cell's degradation due to different operating temperatures. Unlike traditional calendar-plus-cycling additives or chemistry-specific semi-empirical methods, the proposed model is formulated—and further validated—under the assumption that cells exposed to comparable operating conditions degrade at comparable rates, independent of their electrode chemistry. Consequently, only the data of a single degradation campaign at a given operational mode is needed, which is typically found in the cell's datasheet, eliminating the need for time-consuming aging tests. Validation is performed using a public experimental dataset provided by the University of Stanford dataset. The results show the model's ability to predict the end-of-life within one percent of the reported battery State of Health (SOH), even for an electric vehicle usage profile. By embedding the framework in battery management systems, fleet operators can estimate remaining useful life (RUL) based on historical operational data, optimize battery usage based on less degrading SoC values and cooling systems, schedule preventive maintenance before catastrophic capacity loss, and compare alternative uses without the need for degradation studies. These contributions align with conference themes on prognostics, health management, and data-driven energy system optimization, offering a practical pathway towards longer-lived and better-valued electrified assets.

Existing open-source traffic tools accurately reproduce driver behavior and congestion for conventional internal-combustion vehicles. However, in the case of electric vehicles (EVs), they often fail to incorporate critical electrical variables, such as battery voltage, power demand, and State-of-Health, which limits their applicability in operational planning and decision-making. This paper introduces a simulation platform tailored for EVs that bridges the gap between traditional transportation models and the needs of the PHM community in electromobility. The proposed platform combines power and energy consumption profiles derived from Gaussian Mixture Models with physics-based representations of battery behavior. Model parameters are calibrated using a publicly available dataset collected in Ann Arbor, Michigan. Each trip is partitioned into segments based on abrupt changes in speed, ensuring uniform operating conditions within segments and enhancing model transferability across routes. The platform simulates vehicle speed, electrical power demand, State-of-Charge (SoC), terminal voltage, and incremental capacity loss at each simulation step. Battery degradation is estimated through an empirical model fitted to long-term cycling data. A case study demonstrates the simulator’s ability to compare route alternatives between a shared origin and destination. Results show that the shortest path is not always the most energy-efficient nor the least degrading, highlighting the value of health-aware routing. The platform will be publicly released to enable reproducible testing of SoC estimation, range prediction, and degradation forecasting without requiring extensive instrumentation or prolonged field testing.