Energy Storage Systems

Battery packs are prone to capacity, thermal, and aging imbalances in their cells, which limit power delivery to the vehicle. Spurred by this issue, I have been investigating a new class of battery balancing systems, called hybrid battery balancing, capable of simultaneously equalizing battery capacity and temperature while enabling hybridization with additional storage systems, such as supercapacitors. My research departs from the current research paradigm, which regards battery equalization and hybridization as two independent functions performed by two separated power converters. In contrast, my concept integrates these two functions into a single system, paving the way for a lower cost of power conversion in hybrid energy storage units. In addition to hybrid balancing systems, my research also covers:

  • Energy and Thermal Management

  • Hybrid Energy Storage Systems

  • Power Conversion

  • Active Diagnosis

Automated Vehicles

Electric mobility introduces profound modifications, not only to the vehicle’s energy storage, but also to the powertrain. Unlike vehicles with internal combustion engines, electric vehicles can be propelled by compact in-wheel electric motors, enabling a fast, accurate and energy efficient control of the torque applied to the wheels. I have leveraged these emerging actuators to enhance control of automated vehicles and estimation of road surface grip. Additionally, my contributions seek to seamlessly incorporate user preferences— such as journey time, comfort and energy—in the design and safe execution of maneuvers. Towards the fulfillment of this goal, I have investigated methods to maneuver automated vehicles in (i) minimum travel time along a given path, (ii) with minimum energy under travel time constraints or (iii) trade-off between travel time and energy consumption. The computation of minimum-time trajectories and control actions is particularly challenging because of nonlinear tire models, actuation constraints and vehicle handling limits. To overcome these hurdles, I have developed optimization frameworks, which recast the motion planning and control into convex reformulations for which unique optimal solutions can be guaranteed and reliable and numerically efficient solvers can be deployed in real time. My research interests also cover:

  • Cooperative Driving

  • Learning-based Motion Control

  • Diagnosis and Fault-Tolerance Control

  • Cloud-aided Vehicle Control an Sensing