Real time energy management of electrically turbocharged engines based on model learning

Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery. The capability of generating power strongly depends on the engine operating point. One big challenge is that the calibration of generating power capability is time-consuming in experiments. This paper proposes a neuro-fuzzy approach to model the engine. Based on the virtual engine model, the capability of generating power at arbitrary engine operating point can be obtained fast and accurately, which is applicable to implement in real time

Real-time modelling and parallel optimisation of a gasoline direct injection engine

With the increasing complexity of engines and number of control parameters, optimal engine parameter sets need to be searched in the high dimensionality. Traditional calibration methods are too complicated, expensive and timeconsuming. The model-based optimisation is of critical importance for engine fuel efficiency improvement and exhaust emissions reduction. The optimisation highly depends on the model accuracy. In this paper, a multi-layer modelling method is proposed, which can be used to generate the engine model at arbitrary operating points in real time with high accuracy. An enhanced heuristic-algorithm-based optimiser is combined with the real-time modelling method to perform a parallel optimisation. The proposed modelling and optimisation strategy can achieve the minimal fuel consumption fast and accurately. This strategy has been successfully verified using experimental data sets

A review of intelligent road preview methods for energy management of hybrid vehicles

Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV)

A review of intelligent road preview methods for energy management of hybrid vehicles

Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV)

Real-time modelling and parallel optimisation of a gasoline direct injection engine

With the increasing complexity of engines and number of control parameters, optimal engine parameter sets need to be searched in the high dimensionality. Traditional calibration methods are too complicated, expensive and timeconsuming. The model-based optimisation is of critical importance for engine fuel efficiency improvement and exhaust emissions reduction. The optimisation highly depends on the model accuracy. In this paper, a multi-layer modelling method is proposed, which can be used to generate the engine model at arbitrary operating points in real time with high accuracy. An enhanced heuristic-algorithm-based optimiser is combined with the real-time modelling method to perform a parallel optimisation. The proposed modelling and optimisation strategy can achieve the minimal fuel consumption fast and accurately. This strategy has been successfully verified using experimental data sets

Real time energy management of electrically turbocharged engines based on model learning

Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery. The capability of generating power strongly depends on the engine operating point. One big challenge is that the calibration of generating power capability is time-consuming in experiments. This paper proposes a neuro-fuzzy approach to model the engine. Based on the virtual engine model, the capability of generating power at arbitrary engine operating point can be obtained fast and accurately, which is applicable to implement in real time

Distribution of penguin exposed temperature at end of simulation (2 hours).

Distribution of penguin exposed temperature at end of simulation (2 hours).</p

Multifunctional Three-Dimensional Europium Metal–Organic Framework for Luminescence Sensing of Benzaldehyde and Cu²⁺ and Selective Capture of Dye Molecules

A multifunctional three-dimensional lanthanide metal–organic framework has been rationally constructed. Highly selective sensing of benzaldehyde and Cu2+ ions makes it a potential bifunctional sensor. Also, it could serve as a good candidate material for the removal of dyes from effluents based on the size exclusion

Summary of descriptive statistics.

Mean exposted temperature, range, percent of penguins in the thermo-neutral zone, and huddle density for each fo the time periods presented in Fig 6.</p

Relationship between individual penguin exposed temperature and distance to huddle center.

Exposed temperature for penguins on windward, leeward, side, or interior of the huddle. Dashed vertical lines indicate the huddle edge.</p