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The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.

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Safety improvements in mountaineering gear have enabled the increasing popularity of rock climbing as a sport. Both amateurs and experts want to know the condition of their equipment with a high degree of reliability. For climbing ropes, diagnostics are only carried out qualitatively by visual inspection. The assessment is left to the personal judgment of the user, thus leaving considerable margins of uncertainty on the rope’s condition. To address this shortcoming, this article explores the possibility of estimating fatigue damage from the impact force on the rope. This value is estimated from the measurements of the climber’s acceleration using a wearable device. Then, force data are correlated to the fatigue characteristic of the rope. In this study, three ropes were used by professional climbers through different routes. After this field conditioning, the ropes were tested following the UIAA standard and compared to a control rope. The results show that the proposed method can estimate the rope cumulative damage, but it relies on the accuracy of the damage model. In particular, the parameter describing the contact between the rope and the runner is important for a correct estimate.

The richness of information generated by today's vehicles fosters the development of data-driven decision-making models, with the additional capability to account for the context in which vehicles operate. In this work, we focus on Adaptive Cruise Control (ACC) in the case of such challenging vehicle maneuvers as cut-in and cut-out, and leverages Deep Reinforcement Learning (DRL) and vehicle connectivity to develop a data-driven cooperative ACC application. Our DRL framework accounts for all the relevant factors, namely, passengers' safety and comfort as well as efficient road capacity usage, and it properly weights them through a two-layer learning approach. We evaluate and compare the performance of the proposed scheme against existing alternatives through the CoMoVe framework, which realistically represents vehicle dynamics, communication and traffic. The results, obtained in different real-world scenarios, show that our solution provides excellent vehicle stability, passengers' comfort, and traffic efficiency, and highlight the crucial role that vehicle connectivity can play in ACC. Notably, our DRL scheme improves the road usage efficiency by being inside the desired range of headway in cut-out and cut-in scenarios for 69% and 78% (resp.) of the time, whereas alternatives respect the desired range only for 15% and 45% (resp.) of the time. We also validate the proposed solution through a hardware-in-the-loop implementation, and demonstrate that it achieves similar performance to that obtained through the CoMoVe framework.