Riccardo M.G. Ferrari

The transportation sector currently account for 24% of the CO2 worldwide emissions (Teter, 2020), as well as 3.2 million premature deaths due to pollution alone . Solutions to make the sector greenier, healthier and safer include, amongst other, the introduction of electric and autonomous vehicles. Fleets of road freight vehicles are ideal candidates for electrification and for early introduction of autonomous driving capabilities, including cooperative functionalities such as platooning.

We provided a first set of theoretical contributions to such pressing challenge, by developing detection methods that can make tomorrow’s cooperative autonomous vehicles safer (Keijzer & Ferrari, 2019; Keijzer et al., 2021). As part of the project SPARSITY, in collaboration with AB Volvo, we are now investigating how to extend system identification and leverage physics-informed Machine Learning to obtain mathematical models of key electric drive train components. Such models will be instrumental in optimizing the life cycle of those components and implement circular economy solutions.

Joint work with (mostly): Twan Keijzer, Tushar Desai, Yang Wang, Paula Chanfreut, José M. Maestre, Fabian Jarmolowitz, Kausihan Selvam.

Publications

APENERGY

Multi timescale battery modeling: Integrating physics insights to data-driven model

Desai, Tushar, Gallo, Alexander J, and Ferrari, Riccardo MG

Applied Energy 2025

DOI
IEEE_TITS

Collaborative Vehicle Platoons With Guaranteed Safety Against Cyber-Attacks

Keijzer, Twan, Chanfreut, Paula, Maestre, José Maria, and Ferrari, Riccardo Maria Giorgio

IEEE Transactions on Intelligent Transportation Systems 2024

DOI
CDC25

Direct Continuous-Time LPV System Identification of Li-Ion Batteries Via L1-Regularized Least Squares

Wang, Yang, and Ferrari, Riccardo

In IEEE 64th Conference on Decision and Control (CDC) 2025

DOI
ECC25

Continuous-Time System Identification and OCV Reconstruction of Li-Ion Batteries Via Regularized Least Squares

Wang, Yang, Ferrari, Riccardo M.G., and Verhagen, Michel

In European Control Conference 2025
SYSID24

Online Lithium-ion Battery Modeling and State of Charge Estimation via Concurrent State and Parameter Estimation

Li, Jimei, Wang, Yang, Ferrari, Riccardo M.G., Swevers, Jan, and Ding, Feng

IFAC-PapersOnLine 2024

Abs DOI URL

This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.
ECC2024

Concurrent Li-ion Battery Parameter Estimation and Open-Circuit Voltage Reconstruction via L1-Regularized Least Squares

Wang, Yang, Ferrari, Riccardo M.G., and Verhagen, Michel

In European Control Conference 2024
VPPC2023

Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach

Desai, Tushar, and Ferrari, Riccardo MG

In 2023 IEEE Vehicle Power and Propulsion Conference (VPPC) 2023

DOI
IFAC23

Real-time Battery State of Charge and parameters estimation through Multi-Rate Moving Horizon Estimator

Desai, Tushar, Oliva, Federico, Ferrari, Riccardo M.G., and Carnevale, Daniele

In IFAC World Congress 2023
IFAC23

A Coalitional MPC Approach to Control of Collaborative Vehicle Platoons

Chanfreut, Paula, Keijzer, Twan, Maestre, José Maria, and Ferrari, Riccardo M.G.

In IFAC World Congress 2023
CCTA22

Autonomous RC Cars for Control Research and Education: Implementation of Virtual Potential Based Navigation and Platooning

De Jager, TR, Meinders, NK, Van Vugt, TA, and Zomerdijk, W

In 2022 IEEE Conference on Control Technology and Applications (CCTA) 2022
ECC21

Detection of Cyber-Attacks in a Collaborative Intersection Control Scenario

Keijzer, Twan, Jarmolowitz, Fabian, and Ferrari, Riccardo M.G.

In European Control Conference 2021

arXiv video
ECC21

Detection of Network and Sensor Cyber-Attacks in Platoons of Cooperative Autonomous Vehicles: a Sliding-Mode Observer Approach

Keijzer, Twan, and Ferrari, Riccardo M.G.

In European Control Conference 2021

arXiv video
CDC19

A Sliding Mode Observer Approach for Attack Detection and Estimation in Autonomous Vehicle Platoons using Event Triggered Communication

Keijzer, Twan, and Ferrari, Riccardo M.G.

In Conference on Decision and Control 2019
NECSYS18

Attack Detection and Estimation in Cooperative Vehicles Platoons: A Sliding Mode Observer Approach

Jahanshahi, Niloofar, and Ferrari, Riccardo M.G.

In IFAC Workshop on Distributed Estimation and Control in Networked Systems 2018

Additional References

Tracking transport 2020

Teter, Jacob

IEA 2020

URL