Search Results

The ongoing global demand for greater energy efficiency plays an essential role in the automotive industry, as the focus is moving from ICEs to hybrid (HEVs) and electric (EVs) vehicles. New virtual methodologies are necessary to reduce the development effort of these technologies. In this context, the thermal management of the vehicle high voltage battery pack is becoming increasingly important, with significant impact on the vehicle’s range in different environmental scenarios. In this paper, an advanced method is proposed to compute 3D temperature distribution of the cells of a high voltage battery pack for Plug-in Hybrid (PHEV) or full electric (EV) applications. The thermal FE model of a complete PHEV vehicle was integrated with an electrical NTG equivalent circuit model of the HV battery to compute the heat loads of the cells.

This paper presents the design and experimental validation of a localization method for autonomous driving. The investigated method proposes and compares the application of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to the sensor fusion of onboard data streaming from a Global Positioning System (GPS) sensor and an Inertial Navigation System (INS). In the paper, the design of the hardware layout and the proposed software architecture is presented. The method is experimentally validated in real time by using a properly instrumented all-wheel-drive electric racing vehicle and a compact Sport Utility Vehicle (SUV). The proposed algorithm is deployed on a high-performance computing platform with an embedded Graphical Processing Unit that is mounted on board the considered vehicles.

Hybrid electric vehicle (HEV) powertrains are characterized by a complex design environment as a result of both the large number of possible layouts and the need for dedicated energy management strategies. When selecting the most suitable hybrid powertrain architecture at an early design stage of HEVs, engineers usually focus solely on fuel economy (directly linked to tailpipe emissions) and vehicle drivability performance. However, high voltage batteries are a crucial component of HEVs as well in terms of performance and cost. This paper introduces a multitarget assessment framework for HEV powertrain architectures which considers both fuel economy and battery lifetime. A multi-objective formulation of dynamic programming is initially presented as an off-line optimal HEV energy management strategy capable of predicting both fuel economy performance and battery lifetime of HEV powertrain layout options.

Nowadays, the 48 V vehicle architecture seems to be the perfect bridge between the 12 V system and the costly High Voltage (HV) electrification towards the crucial goal of CO2 and pollutants emissions reduction in combination with enhanced performance. However, this approach leads to an increased complexity in the interaction between different sub-systems targeting the optimization of the Energy Management System (EMS). Therefore, it becomes essential to perform a preliminary hardware assessment, exploring the interactions between the different components and quantifying the cost vs benefit trade-off. To this purpose, an integrated experimental/numerical methodology has been adopted: a comprehensive map-based Hybrid Electric Vehicle (HEV) model has been built, allowing the simulation of a variety of hybrid architectures, including both HV and 48 V systems.

The trend towards powertrain electrification is expected to grow significantly in the next future also for super-cars. The aim of this paper is therefore to assess, through numerical simulation, the impact on both fuel economy and performance of different 48 Volt mild hybrid architectures for a high-performance sport car featuring a Turbocharged Direct Injection Spark Ignition (TDISI) engine. In particular the hybrid functionalities of both a P0 (Belt Alternator Starter - BAS) and a P2 (Flywheel Alternator Starter - FAS) architecture were investigated and optimized for this kind of application through a global optimization algorithm. The analysis pointed out CO2 emission reductions potential of about 6% and 25% on NEDC, 7% and 28% on WLTC for P0 and P2 respectively. From the performance perspective, a 10% reduction in the time-to-torque was highlighted for both architectures in a load step maneuver at 2000 RPM constant speed.

The aim of this work is to apply an innovative adaptive ℒ1 techniques to control flutter phenomena affecting highly flexible wings and to evaluate the efficiency of this control algorithm and architecture by performing the following tasks: i) adaptation and analysis of an existing simplified nonlinear plunging/pitching 2D aeroelastic model accounting for structural nonlinearities and a quasi-steady aerodynamics capable of describing flutter and post-flutter limit cycle oscillations, ii) implement the ℒ1 adaptive control on the developed aeroelastic system to perform initial control testing and evaluate the sensitivity to system parameters, and iii) perform model validation and calibration by comparing the performance of the proposed control strategy with an adaptive back-stepping algorithm. The effectiveness and robustness of the ℒ1 adaptive control in flutter and post-flutter suppression is demonstrated.

This paper deals with the design and development of an innovative airship concept which is remotely-controlled and intended to be used for monitoring, surveillance, exploration and reconnaissance missions. Two potential solutions have been analyzed: the first consists of a double-hull configuration, characterized by the presence of a primary support structure connected by appropriated bindings to a couple of twin inflatable hulls. The second architecture is a soap-shaped exoskeleton configuration which features a single inflated section, incorporating two separate elements held internally by a system of ribs. The aim of this study is to analyze and compare the two configurations, to determine the most appropriate solution in terms of performance, cost and maneuvering capabilities