Search

Hybrid electric vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric all-wheel drive (eAWD) capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring (TV) at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in cosimulation with carsim. The paper proposes a control system, which utilizes slip ratio optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal slip ratio targets to at each tire such that the targets avoid tire saturation. The Youla parameterization technique is used to develop robust tracking controllers for each axle. The proposed control system is ultimately tested on the drivetrain model with a high fidelity carsim vehicle model for validation. Simulation results show that the control system is able to maximize vehicle longitudinal performance while avoiding tire saturation on a low µ surface. More importantly, the control system is able to track the desired yaw moment request on a high-speed double-lane change (DLC) maneuver through the use of the TVeRAD to improve the handling characteristic of the vehicle. [ABSTRACT FROM AUTHOR]

The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%-9% being realizable. [ABSTRACT FROM AUTHOR]

Since the automotive industry is shifting towards electrification, brake-by-wire technologies are becoming more prevalent. However, there has been little research comparing and optimizing brake-by-wire actuators in terms of their energy expenditure and response time. This paper investigates the comparison of three different smart brake-by-wire actuators, Electro-Hydraulic Brakes (EHB), Electro-Mechanical Brakes (EMB), and Electronic Wedge Brakes (EWB), first by defining an objective metric and then using both linear and nonlinear optimization techniques. Modeling of the actuators is performed using the bond graph method. Then, the controllers are designed using a robust control strategy, Youla parameterization. After designing the controllers, two types of optimization are performed on the actuators. Optimizations are performed in two ways: 1. by linearizing the plants and optimizing using their transfer functions and 2. by nonlinear optimization of the plants in the closed-loop following a specific clamp force target. The objective metrics or the cost functions for these optimizations are chosen to be the energy usage of the plants during the closed-loop operation, maximum power requirement, and their dynamic responsiveness. Using this optimization framework, we can show a significant improvement in the energy usage of the actuators and slight improvements in their responsiveness. In the end, the actuators are compared in terms of their energy usage for sets of initial and optimized physical parameters. [ABSTRACT FROM AUTHOR]