PARAMETRIC MODELLING OF WEIGHT AND VOLUME EFFECTS IN BATTERY ELECTRIC VEHICLES, WITH FOCUS ON THE GEARBOX

The modeling of battery electric vehicles (BEVs) still represents a challenge for vehicle manufacturers. The installation of the new types of components needed for BEVs gives rise to uncertainties in the quantification of parameters like the vehicle's weight. Indeed, vehicle weight plays a key role, since it has a drastic effect on the vehicle's range, which is an important selling point for BEVs. Uncertainties in weight estimation create weight fluctuations during the early development phase and the need to resize components like the electric machine or battery. This in turn affects the components' volume and weight. However, such resizing can also lead to component collision and unfeasibility of the vehicle architecture. To solve this problem and to support concept engineers during the early development phase, an iterative approach is required that is capable of estimating weight and volume fluctuations in the relevant components. The approach should also consider the geometrical interdependencies of the components, to ensure that no collisions occur between them. Taking the gearbox as an example application, this paper presents a novel approach that satisfies these requirements.

Preliminary Study for AUV: Longitudinal Stabilization Method Based on Takagi-Sugeno Fuzzy Inference System

The paper shows the steps for the preliminary studies of an AUV for shallow water: the first part illustrates the vehicle architecture and the philosophy that permeates the various design choices. In the second part illustrates an innovative method for increasing longitudinal stability based on Takagi-Sugeno (T-S) Fuzzy Inference System: it saves a lot of computational time and, by simplifying the calculation, it is also suitable for remarkably simple computers such as Arduino. in the third part is simulated the behavior of the AUV: thanks to the data taken from the previous hydrodynamic simulation, we can establish the behavior of its longitudinal stability and the computational savings due to the T-S method.

ARCog: An Aerial Robotics Cognitive Architecture

SUMMARY Efficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.

Design Parameters for the Early Development Phase of Battery Electric Vehicles

The derivation of a battery electric vehicle (BEV) architecture represents a challenging task for car manufacturers. For the early development of combustion engine architectures, the required design parameters can be derived from the analysis of previously-built model series. Regarding BEV architectures, the manufacturers do not yet have a reference series of vehicles on the basis of which they can derive the essential design parameters. Therefore, these parameters are mainly estimated at high cost in the early development phase. To avoid cost-intensive changes in the further course of development it is crucial to choose the right set of design parameters. For this reason, the aim of this paper is the identification of a minimum set of design parameters, derived from the current state-of-the-art of vehicle development by a structured literature comparison. We group the results according to our definition of vehicle architecture and discuss each identified parameter to explain its relevance. The sum of all parameters presented in this paper builds a minimum set of design parameters, which can be employed as a guideline for the definition of BEV architectures in the early development stage.