Model-based powertrain design and control system development for the ideal all-wheel drive electric vehicle

The transfer case based all-wheel drive electric vehicle (TCAWDEV) and dual-axle AWDEV have been investigated to balance concerns about energy consumption, drivability and stability of vehicles. However, the mentioned powertrain architectures have the torque windup issue or the wheel skidding issue. The torque windup is an inherent issue of mechanical linked all-wheel drive systems. The hydraulic motor-based or the electric motor-based ideal all-wheel drive powertrain can provide feasible solutions to the mentioned issues. An ideal AWDEV (IAWDEV) powertrain architecture and its control schemes were proposed by this research; the architecture has four independent driving motors in powertrain. The IAWDEV gives more control freedoms to implement active torque controls and traction mode controls. In essence, this research came up with the distributed powertrain concept, and developed control schemes of the distributed powertrain to replace the transfer case and differential devices. The study investigated the dual-loop motor control, the hybrid sliding mode control (HSMC) and the neural network predictive control to reduce energy consumption and achieve better drivability and stability by optimizing the torque allocation of each dependent wheel. The mentioned control schemes were respectively developed for the anti-slip, differential and yaw stability functionalities of the IAWDEV powertrain. This study also investigated the sizing method that the battery capacity was estimated by using cruise performance at 3% road grade. In addition, the model-based verification was employed to evaluate the proposed powertrain design and control schemes. The verification shows that the design and controls can fulfill drivability requirements and minimize the existing issues, including torque windup and chattering of the slipping wheel. In addition, the verification shows that the IAWDEV can harvest around two times more energy while the vehicle is running on slippery roads than the TCAWDEV and the dual-axle AWDEV; the traction control can achieve better drivability and lower energy consumption than mentioned powertrains; the mode control can reduce 3% of battery charge depleting during the highway driving test. It also provides compelling evidences that the functionalities achieved by complicated and costly mechanical devices can be carried out by control schemes of the IAWDEV; the active torque controls can solve the inherent issues of mechanical linked powertrains; the sizing method is credible to estimate the operation envelop of powertrain components, even though there is some controllable over-sizing

Multi-axle Vehicle Modeling and Stability Control: A Reconfigurable Approach

Multi-axle vehicles, such as trucks and buses, have been playing a vital role in trucking industry, public transportation system, and long-distance transport services. However, at the same time, statistics suggest more than one million lives are lost in road accidents each year over the world. The high adoption and utilization of multi-axle vehicles hold a significant portion of road accidents and death. To improve the active safety of vehicles, active systems have been developed and commercialized over the last decades to augment the driver's actions. However, unlike two-axle vehicles (e.g., passenger cars), multi-axle vehicles come in a rich diversity and variety to meet with many different transportation needs. Specifically, vehicle configurations are seen in different numbers of axles, numbers of articulations, powertrain modes, and active actuation systems. In addition, multi-axle vehicles are usually articulated, which makes the dynamics and control more complex and challenging as more instability modes appear, such as, trailer sway and jackknife. This research is hence motivated by an essential question: how can a universal and reconfigurable control system be developed for any multi-axle/articulated vehicle with any configuration? Leveraging the matrix approach and optimization-based techniques, this thesis developed a reconfigurable and universal modeling and control framework to this aim. Specifically, a general dynamics modeling that unifies any multi-axle and articulated vehicles in one formulation is developed in an intuitive manner. It defines the `Boolean Matrices' to determine any configuration of the articulation, the number of axles, and the active actuation systems. In this way, the corresponding dynamics model can be easily and quickly formulated when axles, articulations or actuators are added or removed. The general modeling serves to achieve the universality and reconfigurability in controller design. Therefore, a hierarchical, i.e., two-layer, control system is proposed. In the high layer, the optimization process of a model predictive control (MPC) calculates corrective Center of Gravity (CG) forces/moments, which are universal to any vehicle. The lower-level controller is achieved by a Control Allocation (CA) algorithm. It aims to realize the MPC commands by regulating the steering or torque (driving or braking) at each wheel optimally. In addition, the optimization takes into account real-time constraints, such as actuator limits, tire capacity, wheel slips, and actuators failure. Simulations are conducted on different vehicle configurations to evaluate control performance, reconfigurability, and robustness of the system. Additionally, to evaluate the real-time performance of the developed controller, experimental validation is carried out on an articulated vehicle with multiple configurations of differential braking systems. It is observed that the controller is very effective in dynamics control and has a promising reconfigurability when moving from one configuration to another

Active suspension control of electric vehicle with in-wheel motors

In-wheel motor (IWM) technology has attracted increasing research interests in recent years due to the numerous advantages it offers. However, the direct attachment of IWMs to the wheels can result in an increase in the vehicle unsprung mass and a significant drop in the suspension ride comfort performance and road holding stability. Other issues such as motor bearing wear motor vibration, air-gap eccentricity and residual unbalanced radial force can adversely influence the motor vibration, passenger comfort and vehicle rollover stability. Active suspension and optimized passive suspension are possible methods deployed to improve the ride comfort and safety of electric vehicles equipped with inwheel motor. The trade-off between ride comfort and handling stability is a major challenge in active suspension design. This thesis investigates the development of novel active suspension systems for successful implementation of IWM technology in electric cars. Towards such aim, several active suspension methods based on robust H∞ control methods are developed to achieve enhanced suspension performance by overcoming the conflicting requirement between ride comfort, suspension deflection and road holding. A novel fault-tolerant H∞ controller based on friction compensation is in the presence of system parameter uncertainties, actuator faults, as well as actuator time delay and system friction is proposed. A friction observer-based Takagi-Sugeno (T-S) fuzzy H∞ controller is developed for active suspension with sprung mass variation and system friction. This method is validated experimentally on a quarter car test rig. The experimental results demonstrate the effectiveness of proposed control methods in improving vehicle ride performance and road holding capability under different road profiles. Quarter car suspension model with suspended shaft-less direct-drive motors has the potential to improve the road holding capability and ride performance. Based on the quarter car suspension with dynamic vibration absorber (DVA) model, a multi-objective parameter optimization for active suspension of IWM mounted electric vehicle based on genetic algorithm (GA) is proposed to suppress the sprung mass vibration, motor vibration, motor bearing wear as well as improving ride comfort, suspension deflection and road holding stability. Then a fault-tolerant fuzzy H∞ control design approach for active suspension of IWM driven electric vehicles in the presence of sprung mass variation, actuator faults and control input constraints is proposed. The T-S fuzzy suspension model is used to cope with the possible sprung mass variation. The output feedback control problem for active suspension system of IWM driven electric vehicles with actuator faults and time delay is further investigated. The suspended motor parameters and vehicle suspension parameters are optimized based on the particle swarm optimization. A robust output feedback H∞ controller is designed to guarantee the system’s asymptotic stability and simultaneously satisfying the performance constraints. The proposed output feedback controller reveals much better performance than previous work when different actuator thrust losses and time delay occurs. The road surface roughness is coupled with in-wheel switched reluctance motor air-gap eccentricity and the unbalanced residual vertical force. Coupling effects between road excitation and in wheel switched reluctance motor (SRM) on electric vehicle ride comfort are also analysed in this thesis. A hybrid control method including output feedback controller and SRM controller are designed to suppress SRM vibration and to prolong the SRM lifespan, while at the same time improving vehicle ride comfort. Then a state feedback H∞ controller combined with SRM controller is designed for in-wheel SRM driven electric vehicle with DVA structure to enhance vehicle and SRM performance. Simulation results demonstrate the effectiveness of DVA structure based active suspension system with proposed control method its ability to significantly improve the road holding capability and ride performance, as well as motor performance