Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.
A 3-year-old female mixed-breed dog was referred with a 2-day history of serious dyspnea, coughing, lethargy, anorexia, and a low-grade right anterior lameness. At presentation, the dog had an increased respiratory rate, dull heart and lung sounds, and cyanotic mucous membranes. It was hyperthermic and slightly dehydrated. Laboratory findings showed mild neutrophilia with a left shift, while serum biochemistry variables were in the normal range. However, urinalysis revealed mild proteinuria and rare erythrocytes and leukocytes on sediment examination. Thoracic radiographs showed a diffuse mixed interstitial and alveolar pattern with an air bronchogram, while appendicular radiographs showed a right humeral interrupted brush-like periosteal reaction. Thoracic ultrasonography revealed mediastinal lymph node enlargement. Cytology from a fine-needle aspirate of mediastinal lymph nodes revealed a pyogranulomatous lymphadenitis with numerous fungal hyphae. Culture on Sabouraud dextrose agar isolated dark fungal colonies with microscopic features consistent with Cladosporium spp. Sequencing of the internal transcribed spacer region identified the fungus as a species of the Cladosporium cladosporioides-complex.
The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehicle-level energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor.
The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred.
<div class="section abstract"><div class="htmlview paragraph">Fuel cell electrified powertrains are currently a promising technology towards decarbonizing the heavy-duty transportation sector. In this context, extensive research is required to thoroughly assess the hydrogen economy potential of fuel cell heavy-duty electrification. This paper proposes a real-time capable energy management strategy (EMS) that can achieve improved hydrogen economy for a fuel cell electrified heavy-duty truck. The considered heavy-duty truck is modelled first in Simulink® environment. A baseline heuristic map-based controller is then retained that can instantaneously control the electrical power split between fuel cell system and the high-voltage battery pack of the heavy-duty truck. Particle swarm optimization (PSO) is consequently implemented to optimally tune the parameters of the considered EMS. For the aim of this study, the calibration optimization objective involves minimizing the hydrogen consumption estimated by simulating the heavy-duty truck in the Simulink® model. Simulations entail different driving missions, some of which have been generated by using the VECTO software, i.e. the tool used in Europe to certify the CO2 emissions of new heavy-duty vehicles. Furthermore, dynamic programming (DP) is implemented as an off-line reference EMS approach that can identify the global optimal control trajectory over time by knowing the entire driving mission in advance. The real-time EMS calibrated by means of PSO is demonstrated achieving remarkable hydrogen saving potential, which results being only around 5% worse compared with the global optimal benchmark provided by DP.</div></div>
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