Optimal control of connected and automated vehicles at highway on-ramps to reduce vehicles fuel consumption and increase passenger comfort

2021 ◽  
Vol 109 ◽  
pp. 104747
Author(s):  
Mohammad-Reza Sonbolestan ◽  
Saeed Monajjem
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Automated Vehicles ◽  
Passenger Comfort

scholarly journals Optimal Control for a Hydraulic Recuperation System Using Endoreversible Thermodynamics

2021 ◽  
Vol 11 (11) ◽  
pp. 5001
Author(s):  
Robin Masser ◽  
Karl Heinz Hoffmann
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Energy Savings ◽  
Combustion Engine ◽  
Mechanical Energy ◽  
Hydraulic Systems ◽  
Commercial Vehicles ◽  
Hybrid Drive ◽  
Environmental Considerations ◽  
Onboard System

Energy savings in the traffic sector are of considerable importance for economic and environmental considerations. Recuperation of mechanical energy in commercial vehicles can contribute to this goal. One promising technology rests on hydraulic systems, in particular for trucks which use such system also for other purposes such as lifting cargo or operating a crane. In this work the potential for energy savings is analyzed for commercial vehicles with tipper bodies, as these already have a hydraulic onboard system. The recuperation system is modeled based on endoreversible thermodynamics, thus providing a framework in which realistic driving data can be incorporated. We further used dissipative engine setups for modeling both the hydraulic and combustion engine of the hybrid drive train in order to include realistic efficiency maps. As a result, reduction in fuel consumption of up to 26% as compared to a simple baseline recuperation strategy can be achieved with an optimized recuperation control.

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.

scholarly journals A Novel On-Ramp Merging Strategy for Connected and Automated Vehicles Based on Game Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Haigen Min ◽  
Yukun Fang ◽  
Runmin Wang ◽  
Xiaochi Li ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Game Theory ◽  
Travel Time ◽  
Fuel Consumption ◽  
Transportation Network ◽  
Control Algorithms ◽  
Traffic Density ◽  
Automated Vehicles ◽  
Network Efficiency ◽  
Total Travel Time ◽  
The Cost

Connected and automated vehicles (CAVs) have attracted much attention of researchers because of its potential to improve both transportation network efficiency and safety through control algorithms and reduce fuel consumption. However, vehicle merging at intersection is one of the main factors that lead to congestion and extra fuel consumption. In this paper, we focused on the scenario of on-ramp merging of CAVs, proposed a centralized approach based on game theory to control the process of on-ramp merging for all agents without any collisions, and optimized the overall fuel consumption and total travel time. For the framework of the game, benefit, loss, and rules are three basic components, and in our model, benefit is the priority of passing the merging point, represented via the merging sequence (MS), loss is the cost of fuel consumption and the total travel time, and the game rules are designed in accordance with traffic density, fairness, and wholeness. Each rule has a different degree of importance, and to get the optimal weight of each rule, we formulate the problem as a double-objective optimization problem and obtain the results by searching the feasible Pareto solutions. As to the assignment of merging sequence, we evaluate each competitor from three aspects by giving scores and multiplying the corresponding weight and the agent with the higher score gets comparatively smaller MS, i.e., the priority of passing the intersection. The simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. Moreover, the proposed method improved the fuel economy and saved the travel time.

scholarly journals Nonlinear Optimal Control of Continuously Variable Transmission Powertrain

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Reza Kazemi ◽  
Mohsen Raf’at ◽  
Amir Reza noruzi
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Gear Ratio ◽  
Driver Model ◽  
Control Rules ◽  
Fuel Consumption Reduction ◽  
Consumption Reduction ◽  
Variable Transmission ◽  
Ratio Change ◽  
Computational Systems

Optimization of gear ratio with the objectives of fuel consumption reduction and vehicle longitudinal performance improvement has been the subject of many studies for years. Finding a strategy for changing gears with specific control objectives, especially in the design of vehicles equipped with Continuously Variable Transition system (CVT), which has advantage of arbitrary selection of gear ratio, has been the aim of some recent researches. Optimal control theory has rarely been used in the previous control approaches applied to such systems due to the limitations in the use of fast computational systems. The aim of this study is to design the aforementioned gear ratio change strategy and related control rules on the basis of optimal control. A driver model is also designed for the simulation of driving cycle using MATLAB Simulink Toolbar. Results of implementing optimal control rules in vehicle longitudinal movement simulation with the aim of fuel consumption reduction are finally represented. The presented method has the remarkable advantage of considerable fuel consumption reduction in comparison to other proposed approaches for gear ratio change strategies.

scholarly journals Optimal Control of Diesel Engines: Numerical Methods, Applications, and Experimental Validation

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Jonas Asprion ◽  
Oscar Chinellato ◽  
Lino Guzzella
Keyword(s):  
Optimal Control ◽  
Numerical Methods ◽  
Fuel Consumption ◽  
Diesel Engines ◽  
Large Scale ◽  
Experimental Validation ◽  
Control Unit ◽  
Pollutant Emissions ◽  
Control Input ◽  
Dynamic Optimisation

In response to the increasingly stringent emission regulations and a demand for ever lower fuel consumption, diesel engines have become complex systems. The exploitation of any leftover potential during transient operation is crucial. However, even an experienced calibration engineer cannot conceive all the dynamic cross couplings between the many actuators. Therefore, a highly iterative procedure is required to obtain a single engine calibration, which in turn causes a high demand for test-bench time. Physics-based mathematical models and a dynamic optimisation are the tools to alleviate this dilemma. This paper presents the methods required to implement such an approach. The optimisation-oriented modelling of diesel engines is summarised, and the numerical methods required to solve the corresponding large-scale optimal control problems are presented. The resulting optimal control input trajectories over long driving profiles are shown to provide enough information to allow conclusions to be drawn for causal control strategies. Ways of utilising this data are illustrated, which indicate that a fully automated dynamic calibration of the engine control unit is conceivable. An experimental validation demonstrates the meaningfulness of these results. The measurement results show that the optimisation predicts the reduction of the fuel consumption and the cumulative pollutant emissions with a relative error of around 10% on highly transient driving cycles.

Sign in / Sign up

Export Citation Format

Share Document