Intelligent energy management control systems for autonomous vehicles
Creators: name: PHAN, Duong
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400703
subject: Autonomous vehicle systems
classificationCode: 401703
subject: Energy generation, conversion and storage (excl. chemical and electrical)

description: Autonomous vehicles (AVs) have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Both vehicle travel demand and energy consumption can be considered a complex system because they are subject to continuous fluctuations from environmental conditions and driver behaviour. Intelligent control systems have been proven to be competent for regulating highly complex and nonlinear procedures, which account for intermittent disturbances. This thesis concentrates on developing Intelligent Energy Management Control Systems (IEMCS) to enhance the fuel efficiency of AVs. Additionally, modeling and simulation are utilized to improve their effectiveness and to reduce their development and evaluation times. An AV can be powered by hybrid electric engines in a Parallel Hybrid Electric Autonomous Vehicle (PHEAV) and an internal combustion engine in a Conventional Autonomous Vehicle (CAV). Therefore, in this thesis, the proposed IEMCS are constructed to reduce fuel consumption for CAVs and PHEAVs by using the Road Power Demand model (RPD). The RPD model utilizes three impact factors (i) Environment conditions, (ii) Driver behavior, and (iii) Vehicle specifications (EDV) that are involved in vehicle energy consumption. 

The IEMCS models are built for each of the followings: a CAV and a PHEAV. The proposed systems consist of a main fuzzy logic controller and a PID controller unit. For a CAV, the intelligent fuzzy logic system is used to generate a desired engine torque to satisfy the RPD. Secondly, the PID controller controls the air/fuel ratio by changing the throttle angle of the vehicle to improve the energy efficiency. A novel technique for further reducing fuel consumption is to use a form of particle swarm optimization to tune the parameters of the membership function in an intelligent fuzzy logic system. In addition, the designed IEMCS includes two new Neuro-Fuzzy (NF) systems to produce the optimal torque of the engine. This control system uses the dynamic RPD of CAVs as an input, and a PID controller to regulate the air mass flow rate into the cylinder by changing the throttle angle. Two NF systems were trained by Grid Partition (GP) and Subtractive Clustering (SC) methods to obtain the membership functions by finding the suboptimal solutions as initial values for optimization. Afterward, the adaptive controllers apply their learning capability while they are run and make necessary adjustments to the way they operate.

Typically, PHEAV's IEMCS use a type 1 fuzzy logic controller for autonomous driving because it is robust to small amounts of uncertainty. However, an interval type 2 fuzzy logic controller was designed to be robust to high levels of uncertainty which is often seen in driving and environmental conditions. The potentials of the energy management control system based on the interval type 2 fuzzy logic control, especially for reducing the fuel consumption of the vehicles is also examined.
 
The proposed IEMCSs are simulated and evaluated under the standard Highway Fuel Economy Test Cycle (HWFET). This test cycle is considered under the dynamic driver behavior, the real-world parameters associated with vehicle specifications and artificial environmental conditions. The simulation results are presented and discussed. It is demonstrated that the proposed intelligent energy management systems can reduce the fuel consumption without sacrificing the performance of the vehicle.