Assessing the Performance of Model-Based Energy Saving Charging Strategies in Li-Ion Cells. - Andrea Pozzi; Marcello Torchio; Davide Martino Raimondo (CCTA, 2018)

Optimal Design of Experiment for Parameter Estimation of a Single Particle Model for Lithiumion Batteries. - Andrea Pozzi; Gabriele Ciaramella; Krishnakumar Gopalakrishnan; Stefan Volkwein; Davide Martino Raimondo (CDC, 2018)

Film growth minimization in a Li-ion cell: a Pseudo Two Dimensional model-based optimal charging approach. - Andrea Pozzi; Marcello Torchio; Davide Martino Raimondo (ECC, 2018)

Optimal design of experiments for a lithium-ion cell: parameters identification of a single particle model with electrolyte dynamics. - Andrea Pozzi; Gabriele Ciaramella; Stefan Volkwein; Davide Martino Raimondo (CoRR, 2018)

Optimal Charging of an Electric Vehicle Battery Pack: A Real-Time Sensitivity-Based MPC approach. - Andrea Pozzi; Marcello Torchio; Richard D. Braatz; Davide Martino Raimondo (CoRR, 2019)

Reinforcement Learning-based Fast Charging Control Strategy for Li-ion Batteries. - Saehong Park; Andrea Pozzi; Michael Whitmeyer; Hector Eduardo Perez; Won Tae Joe; Davide Martino Raimondo; Scott J. Moura (CCTA, 2020)

Ecological Velocity Planning through Signalized Intersections: A Deep Reinforcement Learning Approach. - Andrea Pozzi; Sangjae Bae; Yongkeun Choi; Francesco Borrelli; Davide Martino Raimondo; Scott J. Moura (CDC, 2020)

Reinforcement Learning-based Fast Charging Control Strategy for Li-ion Batteries. - Saehong Park; Andrea Pozzi; Michael Whitmeyer; Won Tae Joe; Davide Martino Raimondo; Scott J. Moura (CoRR, 2020)

Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context. - Andrea Pozzi; Xiangzhong Xie; Davide Martino Raimondo; René Schenkendorf (CoRR, 2020)

Balancing-Aware Charging Strategy for Series-Connected Lithium-Ion Cells: A Nonlinear Model Predictive Control Approach. - Andrea Pozzi; Massimo Zambelli; Antonella Ferrara; Davide Martino Raimondo (IEEE Trans. Control. Syst. Technol., 2020)

SOS-based Stability Region Enlargement of Bilinear Power Converters through Model Predictive Control. - Behrouz Zoghdar-Moghadam-Shahrekohne; Andrea Pozzi; Davide Martino Raimondo (MED, 2021)

Deep Learning-Based Predictive Control for the Optimal Charging of a Lithium-Ion Battery with Electrochemical Dynamics. - Andrea Pozzi; Scott J. Moura; Daniele Toti (CCTA, 2022)

A Neural Network-Based Approximation of Model Predictive Control for a Lithium-Ion Battery with Electro-Thermal Dynamics. - Andrea Pozzi; Scott J. Moura; Daniele Toti (ICCA, 2022)

Lexicographic model predictive control strategy in ageing-aware optimal charging procedure for lithium-ion batteries. - Andrea Pozzi; Daniele Toti (Comput. Chem. Eng., 2022)

Review and Alignment of Domain-Level Ontologies for Materials Science. - Anne F. de Baas; Pierluigi Del Nostro; Jesper Friis; Emanuele Ghedini; Gerhard Goldbeck; Ilaria Maria Paponetti; Andrea Pozzi; Arkopaul Sarkar; Lan Yang; Francesco Antonio Zaccarini; Daniele Toti (IEEE Access, 2023)

Controlling Bias Between Categorical Attributes in Datasets: A Two-Step Optimization Algorithm Leveraging Structural Equation Modeling. - Enrico Barbierato; Andrea Pozzi; Daniele Tessera (IEEE Access, 2023)

Imitation Learning for Agnostic Battery Charging: A DAGGER-Based Approach. - Andrea Pozzi; Daniele Toti (IEEE Access, 2023)

Modeling experts, knowledge providers and expertise in Materials Modeling: MAEO as an application ontology of EMMO's ecosystem. - Pierluigi Del Nostro; Gerhard Goldbeck; Andrea Pozzi; Daniele Toti (Appl. Ontology, 2023)

A deep learning-based predictive controller for the optimal charging of a lithium-ion cell with non-measurable states. - Andrea Pozzi; Scott J. Moura; Daniele Toti (Comput. Chem. Eng., 2023)

Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters. - Andrea Pozzi; Enrico Barbierato; Daniele Toti (Sensors, 2023)

Cryptoblend: An AI-Powered Tool for Aggregation and Summarization of Cryptocurrency News. - Andrea Pozzi; Enrico Barbierato; Daniele Toti (Informatics, 2023)

Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging. - Jorge Espin; Dong Zhang 011; Daniele Toti; Andrea Pozzi (ACC, 2024)

Comparing UX Profile of Generative Artificial Intelligence Tools and Traditional Tools in Brainstorming Sessions: A Pilot Study. - Katusha Gerardini; Eleonora D. Sarcinella; Francesca Borghesi; Andrea Pozzi; Andrea Gaggioli; Alice Chirico (MetroXRAINE, 2024)

Bayesian Learning of Causal Networks for Unsupervised Fault Diagnosis in Distributed Energy Systems. - Federico Castelletti; Fabrizio Niro; Marco Denti; Daniele Tessera; Andrea Pozzi (IEEE Access, 2024)

Deep Learning-Based Predictive Control for Optimal Battery Management in Microgrids. - Silvana Matrone; Andrea Pozzi; Emanuele Ogliari; Sonia Leva (IEEE Access, 2024)

Breaking Away From AI: The Ontological and Ethical Evolution of Machine Learning. - Enrico Barbierato; Alice Gatti; Alessandro Incremona; Andrea Pozzi; Daniele Toti (IEEE Access, 2025)

Neural Network-Based Imitation Learning for Approximating Stochastic Battery Management Systems. - Andrea Pozzi; Alessandro Incremona; Daniele Toti (IEEE Access, 2025)

Supporting Brainstorming with a Collaborative Platform or a Generative Artificial Intelligence Tool: An Exploratory Study. - Katusha Gerardini; Eleonora Diletta Sarcinella; Francesca Borghesi; Andrea Pozzi; Andrea Gaggioli; Alice Chirico (Cyberpsychology Behav. Soc. Netw., 2025)

Mitigating exposure bias in large language model distillation: an imitation learning approach. - Andrea Pozzi; Alessandro Incremona; Daniele Tessera; Daniele Toti (Neural Comput. Appl., 2025)

Imitation learning-driven approximation of stochastic control models. - Andrea Pozzi; Alessandro Incremona; Daniele Toti (Appl. Intell., 2025)