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Technical Paper

Design Optimization of the Transmission System for Electric Vehicles Considering the Dynamic Efficiency of the Regenerative Brake

In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle.
Technical Paper

Preview based Vehicle Steering Control using Neural Networks

The motion of a vehicle along a desired path is possible due to steering action of the driver. Hence, vehicle dynamics and control simulations should take into consideration the action of the driver. This work presents a preview based vehicle steering controller using Neural Networks which can be used in the vehicle lateral dynamics simulations. The training data for the Neural Network is being obtained using a steering controller from the existing literature and its gains are determined using Optimization. Three different architectures are being designed and conclusions are presented. These Neural Network models are validated by testing against real track data.
Technical Paper

Trajectory Optimization of Airliners to Minimize Environmental Impact

With the rapid growth in passenger transportation through aviation projected to continue into the future, it is incumbent on aerospace engineers to seek ways to reduce the negative impact of airliner operation on the environment. Key metrics to address include noise, fuel consumption, Carbon Dioxide and Nitrous Oxide emissions, and contrail formation. The research presented in this paper generates new aircraft trajectories to reduce these metrics, and compares them with typical scheduled airline operated flights. Results and analysis of test cases on trajectory optimization are presented using an in-house aircraft trajectory optimization framework created under the European Clean Sky Joint Technology Initiative, Systems for Green Operation Integrated Technology Demonstrator. The software tool comprises an optimizer core and relatively high fidelity models of the aircraft's flight path performance, air traffic control constraints, propulsion and other systems.
Journal Article

Environmental Impact Assessment, on the Operation of Conventional and More Electric Large Commercial Aircraft

Global aviation is growing exponentially and there is a great emphasis on trajectory optimization to reduce the overall environmental impact caused by aircraft. Many optimization techniques exist and are being studied for this purpose. The CLEAN SKY Joint Technology Initiative for aeronautics and Air transport, a European research activity run under the Seventh Framework program, is a collaborative initiative involving industry, research organizations and academia to introduce novel technologies to improve the environmental impact of aviation. As part of the overall research activities, “green” aircraft trajectories are addressed in the Systems for Green Operations (SGO) Integrated Technology Demonstrator. This paper studies the impact of large commercial aircraft trajectories optimized for different objectives applied to the on board systems.
Journal Article

Cyber-Physical System Based Optimization Framework for Intelligent Powertrain Control

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.
Journal Article

A Global Optimal Energy Management System for Hybrid Electric off-road Vehicles

Energy management strategies greatly influence the power performance and fuel economy of series hybrid electric tracked bulldozers. In this paper, we present a procedure for the design of a power management strategy by defining a cost function, in this case, the minimization of the vehicle’s fuel consumption over a driving cycle. To explore the fuel-saving potential of a series hybrid electric tracked bulldozer, a dynamic programming (DP) algorithm is utilized to determine the optimal control actions for a series hybrid powertrain, and this can be the benchmark for the assessment of other control strategies. The results from comparing the DP strategy and the rule-based control strategy indicate that this procedure results in approximately a 7% improvement in fuel economy.
Journal Article

Robustness Testing of Real-Time Automotive Systems Using Sequence Covering Arrays

Testing real-time vehicular systems challenges the tester to design test cases for concurrent and sequential input events, emulating unexpected user and usage profiles. The vehicle response should be robust to unexpected user actions. Sequence Covering Arrays (SCA) offer an approach which can emulate such unexpected user actions by generating an optimized set of test vectors which cover all possible t-way sequences of events. The objective of this research was to find an efficient nonfunctional sequence testing (NFST) strategy for testing the robustness of real-time automotive embedded systems measured by their ability to recover (prove-out test) after applying sequences of user and usage patterns generated by combinatorial test algorithms, considered as “noisy” inputs. The method was validated with a case study of an automotive embedded system tested at Hardware-In-the-Loop (HIL) level. The random sequences were able to alter the system functionality observed at the prove-out test.
Journal Article

Application of Genetic Algorithm for Preliminary Trajectory Optimization

The aviation sector has played a significant role in shaping the world into what it is today. The rapid growth of global economies and the corresponding sharp rise in the number of people now wanting to travel on business and for pleasure, has largely been responsible for the development of this industry. With a predicted rise in Revenue Passenger Kilometers (RPK) by over 150% in the next 20 years, the industry will correspondingly be a significant contributor to environmental emissions. Under such circumstances optimizing aircraft trajectories for lowered emissions will play a critical role amongst various other measures, in mitigating the probable environmental effects of increased air traffic. Aircraft trajectory optimization using evolutionary algorithms is a novel field and preliminary studies have indicated that a reduction in emissions is possible when set as objectives.