Abstract
The coordinated control of vehicle actuators is gaining more and more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Furthermore, wheel individual motors allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimisation of performance and energy consumption issues. In this paper, the problem of minimum-time manoeuvring in EVs is addressed, and the proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.

Abstract
This paper is concerned with the study of combined sizing and energy management algorithms for electric vehicles (EVs) endowed with batteries and supercapacitors (SCs). The main goal is to find the number of cells of each source that minimizes the installation and running costs of the EV, taking into account the performance requirements specified for the vehicle and the technical constraints of the energy sources. To tackle this problem, two methodologies will be investigated. The first considers a filter-based approach to perform the power split among the sources; it will be shown that, under some practical assumptions, the resultant sizing problem can be posed as a linear programming problem and solved using efficient numerical techniques. The second methodology employs an optimal noncausal energy management, which, when integrated with the sizing problem, yields a nonlinear optimization problem. These two methodologies will be then applied to size the storage unit of a small EV. The results indicate that the filter-based approach, although simple and numerically efficient, generally requires an oversized storage unit. Furthermore, it was also concluded that, if the range requirements of the EV are not very high (below 50 km, in our case study), the use of SCs enables energy savings of up to 7.8%.

Abstract
Contemporary safety standards prescribe processes in which system safety requirements, captured early and expressed in the form of Safety Integrity Levels (SILs), are iteratively allocated to architectural elements. Different SILs reflect different requirements stringencies and consequently different development costs. Therefore, the allocation of safety requirements is not a simple problem of applying an allocation "algebra" as treated by most standards; it is a complex optimisation problem, one of finding a strategy that minimises cost whilst meeting safety requirements. One difficulty is the lack of a commonly agreed heuristic for how costs increase between SILs. In this paper, we define this important problem; then we take the example of an automotive system and using an automated approach show that different cost heuristics lead to different optimal SIL allocations. Without automation it would have been impossible to explore the vast space of allocations and to discuss the subtleties involved in this problem.

Abstract
While a great number of battery balancing circuit topologies have been proposed, the unique control objective typically pursued is equalization of single cell charge. However, a balancing circuit could offer potentially more control features, especially with topologies able to provide bidirectional power flow control. This has not been explored yet in literature or at least not with enough thoroughness. Thus, in addition to charge balancing, up to three more objectives could be pursued simultaneously. Firstly, virtual resistance control, in order to provide dynamic compensation for variations in terminal cell voltage. Secondly, thermal management, to achieve a more uniform temperature distribution within a battery pack. Third, on-board diagnosis or fault detection tools, e.g. to perform characterization tests or to identify and even isolate problematic cells. In this paper, this issue is discussed and evaluated for a battery pack made up of 48 large format Li-Ion cells in series in a e-mobility application. Simulation results demonstrate the technical feasibility of this newly defined concept.

Abstract
Autonomous vehicles are becoming a reality that in the next future will most probably start populating everyday roads. Such vehicles can, on the one hand, increase safety through automated driving, and, on the other, be a means of transportation also for people with disabilities who cannot move alone on commercial cars. Within this class of vehicles, mechanical layouts that allow an actuator redundancy coupled with electric propulsion appear particularly interesting, as they make it possible to design motion controller that can optimally blend multiple objectives, both dynamic, safety and driver-oriented. This paper considers such setting and concentrates on the design of a path-following algorithm with minimum-time features, with the aim of combining performance and energy-oriented optimization of the vehicle motion. The effectiveness of the approach is assessed by means of simulation tests carried out on the CarSim vehicle simulation environment. © IFAC.

Abstract
Visual motion perception from a moving observer is the most often encountered case in real life situations. It is a complex and challenging problem, although, it can promote the arising of new applications. This article presents an innovative and autonomous robotic system designed for active surveillance and a dense optical flow technique. Several optical flow techniques have been proposed for motion perception however, most of them are too computationally demanding for autonomous mobile systems. The proposed HybridTree method is able to identify the intrinsic nature of the motion by performing two consecutive operations: expectation and sensing. Descriptive properties of the image are retrieved using a tree-based scheme and during the expectation phase. In the sensing operation, the properties of image regions are used by a hybrid and hierarchical optical flow structure to estimate the flow field. The experiments prove that the proposed method extracts reliable visual motion information in a short period of time and is more suitable for applications that do not have specialized computer devices. Therefore, the HybridTree differs from other techniques since it introduces a new perspective for the motion perception computation: high level information about the image sequence is integrated into the estimation of the optical flow. In addition, it meets most of the robotic or surveillance demands and the resulting flow field is less computationally demanding comparatively to other state-of-the-art methods.