Stefano Arrigoni

Comparison between different energy management algorithms for an urban electric bus with hybrid energy storage system based on battery and supercapacitors

Electric vehicles are an interesting research field for the automotive industry, especially for fully electrical urban buses. Their particular path-defined frequent and consecutive stops close together encourage the usage of supercapacitors, which have a longer service life than rechargeable batteries, and the battery would only be used as a backup energy source. This means a hybrid energy system where an energy management function splits the power request between the two onboard energy storage systems. Two different real-time control algorithms previously developed are briefly presented and numerically tested by means of virtual simulation in order to compare their different behaviour and evaluate their performance compared to an optimal offline control logic based on the dynamic programming approach.

Design and Modeling of a Joystick Control Scheme for an Upper Limb Powered Exoskeleton

This paper presents a control scheme for a 5 DOFs upper limb powered exoskeleton. A joystick-based interface was chosen in order to receive the desired cartesian position and relative orientation of the end effector from the user. The control scheme verifies the belonging of the commanded position to the exoskeleton workspace before proceeding with the inverse kinematic optimization. As assistive device, the exoskeleton has to operate according to user’s intention. Therefore, control scheme’s capacity to track the desired cartesian trajectory is investigated. Robot redundancy is here exploited in order to minimize joint torques and avoiding singularities.

Energy Management Algorithms Comparison for an Electric Bus with an Hybrid Energy Storage System by Means of Dynamic Programming

Low emission vehicles are an attractive research field for the automotive industry. A feasible solution for urban buses is a full electrical powertrain powered both by a supercapacitor, rechargeable at each bus stop while passengers are getting on and off, and, in the worst operating conditions such as traffic jams or long runs, by a conventional battery. The result is a hybrid energy system where an energy management function is required in order to divide the power request between the two onboard energy storage systems. Different energy management functions have been previously developed and validated using numerical simulation. This paper presents a comparison of various algorithms developed with an optimal algorithm defined using Dynamic Programming.

On Board Energy Management Algorithm Based on Fuzzy Logic for an Urban Electric Bus with Hybrid Energy Storage System

Nowadays considerable resources have been invested on low emission passenger vehicle both for private and public transportation. A feasible solution for urban buses is a full electrical traction system fed by supercapacitor, that can be recharged at each bus stop while people are getting on and off. Moreover, in order to consider the worst operating condition for the bus (like traffic jam of higher distance to be covered), a conventional battery is also installed, obtaining an hybrid energy storage system. An energy management function, able to manage the two on board energy storage system based on fuzzy control logic, has been developed and validated by means of numerical simulations and compared to a previously presented one in order to evaluate its performances.

Decentralized fast-MPC path planner for automated vehicles

Safe path-planning in an environment subject to the presence of other vehicles or obstacles in general is a crucial task for autonomous vehicle development. A complete control scheme is designed in order to test a decentralized fast MPC path planner algorithm. The proposed control scheme is composed by a receding replanner based on a fast numerical implementation of a nonlinear optimization problem and PIDs as trajectory tracker. A detailed description of the control problem formulation is provided as well as the mathematical models of vehicle considered. Numerical simulations of the control are discussed and finally conclusions are drawn.