Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Power Management of a Plug-in Hybrid Electric Vehicle Based on Cycle Energy Estimation

2012 Workshop on Engine and Powertrain Control,Simulation and ModelingThe International Federation of Automatic ControlRueil-Malmaison, France, October 23-25, 2012Plug-in Hybrid Electric Vehicles (PHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. There are many potential advantages of hybridization such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. In order to solve the power management problem, the controller proposed in this work is divided into two levels: the upper one calculates the power that must be supplied by the engine at each moment taking into account the estimation of the energy that must be supplied by the powertrain until the end of the journey. The lower one manages the torque/speed set points for all the devices. Besides, the operation modes are changed according to some heuristic rules. Several simulation results are presented, showing that the proposed control strategy can provide good performance with low computational load

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

A predictive dynamic model of a smart cogeneration plant fuelled with fast pyrolysis bio-oil

Small scale biomass-based cogeneration has the potential to contribute significantly to a clean, flexible, secure, and cost-efficient energy system. It provides flexibility to future energy systems by balancing variable intermittent renewable energy sources. To exploit its flexibility, a smart control unit is needed. To enable smart control of a cogeneration unit, and to determine its optimal working points, a dynamic system model is required. The purpose of this study is to develop, parameterize and tune a dynamic model of a cogeneration plant fuelled with fast pyrolysis bio-oil. The system is a hybrid diesel generator/flue gas boiler plant for electricity generation and water/space heating. The plant has two unique features: (i) pyrolysis bio-oil is a new fuel for both engine and boiler, and as such it influences their operation and emissions, (ii) power and heat generation are partially decoupled hence non-linearly correlated. The paper presents the integration of the components’ dynamic models into a system model. The model is parameterized and partially validated using measurements from a turbocharged four-cylinder diesel engine and a swirl burner both running on FPBO. Preliminary controls are designed and evaluated. Results show applicability and usefulness of the model for cogeneration system analysis and control design evaluation