Model-based multiobjective evolutionary algorithm optimization for HCCI engines

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual

Real-time velocity optimization to minimize energy use in passenger vehicles

Energy use in internal combustion engine passenger vehicles contributes directly to CO 2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver

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Available from TIB Hannover: FR 5361(2)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Coexistence conservation: Reconciling threatened species and invasive predators through adaptive ecological and evolutionary approaches

Invasive predators are responsible for declines in many animal species across the globe. To redress these declines, conservationists have undertaken substantial work to remove invasive predators or mitigate their effects. Yet, the challenges associated with removal of invasive predators mean that most successful conservation programs have been restricted to small islands, enclosures (“safe havens”), or refuge habitats where threatened species can persist. While these approaches have been, and will continue to be, crucial for the survival of many species, in some contexts they may eventually lock in a baseline where native species vulnerable to invasive predators are accepted as permanently absent from the wild (shifting baseline syndrome). We propose an explicit theme in conservation biology termed “coexistence conservation,” that is distinguished by its pursuit of innovative solutions that drive or enable adaptive evolution of threatened species and invasive predators to occur over the long term. We argue evolution has a large role to play but using it to adapt native species to a new environmental order requires a shift in mindset from small, isolated, and short-term leaps to deliberate, staged steps within a long-term strategy. A key principle of coexistence conservation is that predation is treated as the threat, rather than the predator, driving a focus on the outcome rather than the agent. Without a long-term strategy, we face the permanent loss of many species in the wild. Coexistence conservation is a complementary approach to current practice and will play an important role in shifting our current trajectory from continued and rapid invasive predator-driven defaunation to a world where invasive predators and native prey can coexist