119 citations · 144 across the 11 of their papers we have counts for
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Combining physics-based and machine learning methods to accelerate innovation in sustainable transportation and beyond: a control perspective
Gabriele Pozzato, Simona Onori
Lithium-ion batteries are playing a key role in the sustainable energy transition. To fully exploit the potential of this technology, a variety of modeling, estimation, and predict…
Core-shell enhanced single particle model for lithium iron phosphate batteries: model formulation and analysis of numerical solutions
Gabriele Pozzato, Aki Takahashi, Xueyan Li +3
In this paper, a core-shell enhanced single particle model for iron-phosphate battery cells is formulated, implemented, and verified. Starting from the description of the positive…
Effect of State of Charge Uncertainty on Battery Energy Storage Systems
Sonia Martin, Simona Onori, Ram Rajagopal
Battery energy storage systems (BESSs) provide many benefits to the electricity grid, including stability, backup power, and flexibility in introducing more clean energy sources. A…
Core-shell enhanced single particle model for LiFePO batteries
Aki Takahashi, Gabriele Pozzato, Anirudh Allam +5
In this paper, a novel electrochemical model for LiFePO battery cells that accounts for the positive particle lithium intercalation and deintercalation dynamics is proposed. St…
Evaluating feasibility of batteries for second-life applications using machine learning
Aki Takahashi, Anirudh Allam, Simona Onori
This paper presents a combination of machine learning techniques to enable prompt evaluation of retired electric vehicle batteries as to either retain those batteries for a second-…
Extending life of Lithium-ion battery systems by embracing heterogeneities via an optimal control-based active balancing strategy
Vahid Azimi, Anirudh Allam, Simona Onori
This paper formulates and solves a multi-objective fast charging-minimum degradation optimal control problem (OCP) for a lithium-ion battery module made of series-connected cells e…
Sensitivity analysis of a mean-value exergy-based internal combustion engine model
Gabriele Pozzato, Denise Rizzo, Simona Onori
In this work, we conduct a sensitivity analysis of the mean-value internal combustion engine exergy-based model, recently developed by the authors, with respect to different drivin…
Mean-value exergy modeling of internal combustion engines: characterization of feasible operating regions
Gabriele Pozzato, Denise Rizzo, Simona Onori
In this paper, a novel mean-value exergy-based modeling framework for internal combustion engines is developed. The characterization of combustion irreversibilities, thermal exchan…
Exergy-based modeling framework for hybrid and electric ground vehicles
Federico Dettù, Gabriele Pozzato, Denise M. Rizzo +1
Exergy, or availability, is a thermodynamic concept representing the useful work that can be extracted from a system evolving from a given state to a reference state. It is also a…
Modeling Degradation for Second-life Battery: Preliminary Results
Gabriele Pozzato, Seong Beom Lee, Simona Onori
This paper presents a novel battery modeling framework based on the enhanced single particle model (ESPM) to account for degradation mechanisms of second-life batteries. While acco…
On-line Capacity Estimation for Lithium-ion Battery Cells via an Electrochemical Model-based Adaptive Interconnected Observer
Anirudh Allam, Simona Onori
Battery aging is a natural process that contributes to capacity and power fade, resulting in a gradual performance degradation over time and usage. State of Charge (SOC) and State…