scholarly journals Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan

2021 ◽  
Vol 13 (16) ◽  
pp. 9173
Author(s):  
Zifei Nie ◽  
Hooman Farzaneh
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Urban Areas ◽  
Urban Transport ◽  
Transport Sector ◽  
Traffic Situation ◽  
Cruise Control ◽  
Car Following ◽  
Driving Scenario

Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal anticipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, respectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest considerable energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.

scholarly journals Adaptive Cruise Control Based on Model Predictive Control with Constraints Softening

2020 ◽  
Vol 10 (5) ◽  
pp. 1635
Author(s):  
Lie Guo ◽  
Pingshu Ge ◽  
Dachuan Sun ◽  
Yanfu Qiao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Car Following ◽  
Control Precision ◽  
Performance Indexes ◽  
Preceding Vehicle ◽  
And Control

In this paper, with the aim of meeting the requirements of car following, safety, comfort, and economy for adaptive cruise control (ACC) system, an ACC algorithm based on model predictive control (MPC) using constraints softening is proposed. A higher-order kinematics model is established based on the mutual longitudinal kinematics between the host vehicle and the preceding vehicle that considers the changing characteristics of the inter-distance, relative velocity, acceleration, and jerk of the host vehicle. Performance indexes are adopted to represent the multi-objective demands and constraints of the ACC system. To avoid the solution becoming unfeasible because of the overlarge feedback correction, the constraint softening method was introduced to improve robustness. Finally, the proposed ACC method is verified in typical car-following scenarios. Through comparisons and case studies, the proposed method can improve the robustness and control precision of the ACC system, while satisfying the demands of safety, comfort, and economy.

Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control

Energy ◽  
2021 ◽  
pp. 122793
Author(s):  
Chaofeng Pan ◽  
Aibao Huang ◽  
Jian Wang ◽  
Liao Chen ◽  
Jun Liang ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Adaptive Cruise Control ◽  
Cruise Control

scholarly journals Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm

2020 ◽  
Vol 10 (15) ◽  
pp. 5271
Author(s):  
Zifei Nie ◽  
Hooman Farzaneh
Keyword(s):  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Car Following ◽  
Preceding Vehicle ◽  
Variation Rate ◽  
Model Predictive Control Algorithm

An adaptive cruise control (ACC) system is developed based on eco-driving for two typical car-following traffic scenes. The ACC system is designed using the model predictive control (MPC) algorithm, to obtain objectives of eco-driving, driving safety, comfortability, and tracking capability. The optimization of driving comfortability and the minimization of fuel consumption are realized in the manner of constraining the acceleration value and its variation rate, so-called the jerk, of the host vehicle. The driving safety is guaranteed by restricting the vehicle spacing always larger than minimum safe spacing from the host vehicle to the preceding vehicle. The performances of the proposed MPC-based ACC system are evaluated and compared with the conventional proportional-integral-derivative (PID) controller-based ACC system in two representative driving scenarios, through a simulation bench and an instantaneous emissions and fuel consumption model. In addition to meeting the other driving objectives mentioned above, the simulation results indicate an improvement of 13% (at the maximum) for fuel economy, which directly shows the effectiveness of the presented MPC-based ACC system.

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