Multi-objective Optimization for Connected and Automated Vehicles Using Machine Learning and Model Predictive Control

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.

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Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort

Energy and Buildings ◽

10.1016/j.enbuild.2015.11.033 ◽

2016 ◽

Vol 111 ◽

pp. 131-144 ◽

Cited By ~ 100

Author(s):

Fabrizio Ascione ◽

Nicola Bianco ◽

Claudio De Stasio ◽

Gerardo Maria Mauro ◽

Giuseppe Peter Vanoli

Keyword(s):

Model Predictive Control ◽

Thermal Comfort ◽

Predictive Control ◽

Building Energy ◽

Energy Performance ◽

Multi Objective Optimization ◽

Building Energy Performance ◽

Multi Objective ◽

Simulation Based

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Multi-Objective Dispatch of a Microgrid with Battery Energy Storage System Based on Model Predictive Control

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1070-1072.1384 ◽

2014 ◽

Vol 1070-1072 ◽

pp. 1384-1390

Author(s):

Yan Zhang ◽

Tao Zhang ◽

Bo Guo ◽

Yong Chao Cai

Keyword(s):

Energy Storage ◽

Model Predictive Control ◽

Predictive Control ◽

Storage System ◽

Energy Storage System ◽

Multi Objective Optimization ◽

Battery Energy Storage ◽

Multi Objective ◽

Interesting Approach ◽

Battery Energy

Microgrid has been considered as a new green and reliable power system technique, especially for remote regions. In recent years, there is a steady increasing in studying optimal microgrid deploying and operation strategies. Multi-objective optimization is the most interesting approach for resolving these issues. The multi-objective optimization includes energy operation cost and emission pollutant cost. Potential benefits of using model predictive control (MPC) strategy for multi-objective dispatch problem in microgrid with fluctuant energy resources, such as solar, wind and alike are also presented by comparing with the strategy of day-ahead programming strategy and normal strategy with no battery energy storage. Simulation results show that the proposed model in this paper could reflect the actual characteristics of microgrid more precisely.

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Heuristic multi-objective optimization for cost function weights selection in finite states model predictive control

2011 Workshop on Predictive Control of Electrical Drives and Power Electronics ◽

10.1109/precede.2011.6078690 ◽

2011 ◽

Cited By ~ 33

Author(s):

Pericle Zanchetta

Keyword(s):

Model Predictive Control ◽

Cost Function ◽

Predictive Control ◽

Multi Objective Optimization ◽

Multi Objective ◽

Finite States

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Multi-objective optimization strategy of adaptive cruise control considering regenerative energy

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019830200 ◽

2019 ◽

Vol 233 (14) ◽

pp. 3630-3645 ◽

Cited By ~ 2

Author(s):

Di Wu ◽

Bo Zhu ◽

Dongkui Tan ◽

Nong Zhang ◽

Jiaxin Gu

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Regenerative Braking ◽

Force Distribution ◽

Optimization Strategy ◽

Multi Objective Optimization ◽

Source Information ◽

Distribution Strategy ◽

Multi Objective ◽

Regenerative Energy

A multi-objective optimization strategy considering regenerative braking was proposed. Taking into account the impact of relative velocity, driver style, and road adhesion conditions, a variable time headway strategy was first proposed. Then, a multi-objective optimization strategy in adaptive cruise system was designed under the model predictive control framework. The incremental adaptive model predictive control with time-varying weights was constructed to be used as the upper controller, and slack variable was used to process the constraints. The constraints of regenerative braking were analyzed, and a new brake force distribution strategy based on multi-source information fusion was proposed to further optimize the economy. On the AMESim & Simulink co-simulation platform, a battery electric vehicle model was built and the proposed strategy was simulated. The results showed that, comparing to the constant-weight strategy, the proposed strategy had better robustness, which could rapidly and timely adjust the control target and guarantee the safety, comfort, economy, and following. The multi-source information braking force distribution strategy can guarantee several goals of the system while improving the economy. It can regenerate more braking energy, and the braking regenerative energy contribution increased by 5.68%.

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