Two-Level MPC Speed Profile Optimization of Autonomous Electric Vehicles Considering Detailed Internal and External Losses

Abstract

This paper proposes a novel two-level model predictive control (MPC) speed control algorithm for autonomous vehicles as a successive convex optimization problem focused on both energy use and arrival time. Internal losses such as detailed motor/inverter efficiency and battery loss, as well as external losses, such as wind and grade, are considered. The effect of the higher accessory energy usage of autonomous vehicles on the energy-optimal speed profile is considered in the algorithm and investigated in the paper. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle on-board computing resources. An electric vehicle model is created in MATLAB/Simulink and validated to real-world logged driving data. This vehicle model is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile.INDEX TERMS Electric vehicles, optimization, autonomous agents, modeling, model predictive control.

Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

“…In addition, in [13], multiple parameters in the trajectory planning have been considered which tuning of the weights associated with each parameter is challenging. Majority of previous works concerning A-EVs, such as [3], [16], [17], [21], [22], [34], have analyzed A-EVs for sensors, signal processing and related accurate path following issues, while trajectory planning and charging scheduling of A-EVs have not been well explored. Based on the presented motivation and existing gaps, the main contributions of the current work are.…”

Self-Diagnostic Opportunities for Battery Systems in Electric and Hybrid Vehicles

“…The integration of various electronic systems, such as edge computing, artificial intelligence (AI), and advanced driver assistance systems (ADAS), significantly affects the power consumption and overall energy efficiency of autonomous vehicles [14,15]. In addition, deploying numerous sensors and computing resources on board autonomous vehicles significantly increases the continuous vehicle load, resulting in higher power consumption [16]. Furthermore, using deep learning approaches in autonomous vehicles introduces high computational complexity and power consumption, which may affect the driving range of these vehicles [15,17].…”

LQR-MPC-Based Trajectory-Tracking Controller of Autonomous Vehicle Subject to Coupling Effects and Driving State Uncertainties

“…In the past few years, numerous control techniques have been studied to address the problem of trajectory tracking in autonomous vehicles. The existing control methods, such as sliding mode control (SMC) [ 3 , 4 , 5 ], robust control [ 6 ], model predictive control (MPC) [ 7 , 8 , 9 , 10 , 11 , 12 ], the linear quadratic regulator (LQR) [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], and the classic PID control [ 8 , 19 ], were proposed to pursue the task of lateral and longitudinal control. However, most of these studies aimed to address the lateral and longitudinal control separately.…”

“…Compared with the path tracking approach presented in [ 10 ], the designed controller is tested in a wider speed range (30–118 km/h). Moreover, compared with the longitudinal control approach presented in [ 7 , 9 ], an EKF observer is established to estimate the longitudinal velocity, which is sensitive to the control process and is hard to measure directly.…”