Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

Abstract

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

An adaptive cascade predictive control strategy for connected and automated vehicles

“…An in‐depth analysis of automotive opportunities is shown in Reference 35, where the need of more powerful CPUs with respect to traditional micro‐controller has been analyzed. Some recent real‐time applications are shown in References 6,7,36‐39, but are not referred to an MPC cascade control strategy.…”

“…Therefore, the present work defines a cascade control strategy to be performed on an intelligent OBU (I‐OBU). In particular, the strategy is composed of two MPCs: (i) the first one features the outer control loop and is based on a vehicle kinematic prediction model, which calculates time‐varying set points of the steering angle at the wheels and the reference trajectory for the vehicle speed, and guarantees the tracking of reference trajectories in the inertial frame considering information retrieved from the Smart Road and the vehicle navigation system; (ii) the second one, featuring the inner loop, inherited from previous works, 5‐7 acts on longitudinal and lateral vehicle dynamics so as to guarantee speed tracking, lane keeping and a safety relative distance from an ahead vehicle, by acting on the steering angle at the wheels and providing the vehicle power‐train with a torque request. More in detail, this paper is the result of an advancement of the last work, 7 in which the adaptive MPC acting on the longitudinal and lateral dynamics was tested in an Hardware‐In‐the‐Loop environment (using a Raspberry Pi 4 as real‐time target device), without considering a control layer devoted to the tracking of reference trajectories based on vehicle kinematics.…”

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

“…In particular, the main topics concern platooning control, [23][24][25] Cooperative Adaptive Cruise Control (CACC), 26 cooperative driving with multiple CAVs (e.g., during lane changing) in mixed traffic conditions featured by human driven and automated vehicles, 27,28 maneuvers management at roundabouts 29 and intersections, [30][31][32] and control strategies for CAVs involving Energy Management Systems (EMS). [33][34][35] From the state-of-the-art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control. In particular, most of the recent published methods concern predictive control strategies for ADAS and Automated Vehicles (AVs), mainly adaptive [36][37][38][39] and nonlinear.…”

“…Many topics have been studied, 20‐22 considering aspects related to both the road infrastructure and vehicles. In particular, the main topics concern platooning control, 23‐25 Cooperative Adaptive Cruise Control (CACC), 26 cooperative driving with multiple CAVs (e.g., during lane changing) in mixed traffic conditions featured by human driven and automated vehicles, 27,28 maneuvers management at roundabouts 29 and intersections, 30‐32 and control strategies for CAVs involving Energy Management Systems (EMS) 33‐35 . From the state‐of‐the‐art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control.…”

Research on Parameter Optimization Design Method for Dual-Motor Coupled Drive System

“…In recent years, researchers around the world have made notable progress in cooperative optimization methods and double-layer control strategies [21][22][23][24][25]. Angelo et al proposed a novel double-layer control architecture designed to drive the longitudinal motion of electric vehicles.…”