Influence of an Automatic Transmission with a Model Predictive Control and an On-Demand Clutch Actuator on Vehicle Fuel Consumption

2016 ◽  
Author(s):  
Thomas Huth ◽  
Stefan Pischinger
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Automatic Transmission ◽  
On Demand

scholarly journals Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

2018 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Nicolas Sockeel ◽  
Jian Shi ◽  
Masood Shahverdi ◽  
Michael Mazzola
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Control Solution ◽  
Current Time ◽  
Battery Model ◽  
Hybrid Electric

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

CI Engine Model Predictive Control With Availability Destruction Minimization

2018 ◽  
Author(s):  
Muataz Abotabik ◽  
Richard T. Meyer
Keyword(s):  
Energy Source ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Alternative Fuels ◽  
Combustion Engine ◽  
Tracking Error ◽  
Specific Fuel Consumption ◽  
Nox Emissions ◽  
Compression Ignition Engine

Major interests in the automotive industry include the use of alternative fuels and reduced fuel usage to address fuel supply security concerns and regulatory requirements. The majority of previous internal combustion engine (ICE) control strategies consider only the First Law of Thermodynamics (FLT). However, FLT is not able to distinguish losses in work potential due to irreversibilities, e.g., up to 25% of fuel exergy may be lost to irreversibilities. To account for these losses, the Second Law of Thermodynamics (SLT) is applicable. The SLT is used to identify the quality of an energy source via availability since not all the energy in a particular energy source is available to produce work; therefore optimal control that includes availability may be another path toward reduced fuel use. Herein, Model Predictive Control (MPC) is developed for both FLT and SLT approaches where fuel consumption is minimized in the former and availability destruction in the latter. Additionally, both include minimization of load tracking error. The controls are evaluated in the simulation of a single cylinder naturally aspirated compression ignition engine that is fueled with either 20% biodiesel and 80% diesel blend or diesel only. Control simulations at a constant engine speed and changing load profile show that the SLT approach results in higher SLT efficiency, reduced specific fuel consumption, and decreased NOx emissions. Further, compared to use of diesel only, use of the biodiesel blend resulted in less SLT efficiency, higher specific fuel consumption, and lower NOx emissions.

scholarly journals Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 220 ◽  
Author(s):  
Juan Chen ◽  
Yuxuan Yu ◽  
Qi Guo
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Traffic Congestion ◽  
Control Method ◽  
Optimization Method ◽  
Speed Limit ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.

Scalable Model Predictive Control for Autonomous Mobility-on-Demand Systems

2019 ◽  
pp. 1-10
Author(s):  
Andrea Carron ◽  
Francesco Seccamonte ◽  
Claudio Ruch ◽  
Emilio Frazzoli ◽  
Melanie N. Zeilinger
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Demand Systems ◽  
On Demand ◽  
Autonomous Mobility
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