Quantum Fourier transform to estimate drive cycles

Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.

Validation of a multi-physics simulation platform for engine emissions modelling

This paper introduces a comprehensive and systematic Design of Experiments based methodology deployed in conjunction with a multi-physics engine air-path and combustion co-simulation, leading to the development of a global transient simulation capability for engine out NOx emissions. The proposed multi-physics engine simulation framework couples a real-time one-dimensional air flow model with a Probability Density Function based Stochastic Reactor Model that accounts for detailed in-cylinder combustion chemistry to predict combustion emissions. The integration challenge stemming from the different computation complexities and time scales required to ensure adequate fidelity levels across multi-physics simulations was addressed through a comprehensive Design of Experiments methodology to develop a reduction of the slower Stochastic Reactor Model simulation to enable a transient simulation focussed on NOx emissions. The Design of Experiments methodology, based on Optimal Latin Hypercube design experiments, was deployed on the multi-physics engine co-simulation platform and systematically validated against both steady state and transient light-duty Diesel engine test data. The surrogate selection process included the evaluation of a range of metamodels, with Kriging metamodels selected based on both the statistical performance criteria and consideration of physical phenomena trends. The transient validation was carried out on a simulated New European Drive Cycle against the experimental data available, showing good capability to capture transient NOx emission behaviour in terms of trends and values. The significance of the results is that it proves the transient and drive cycle capability of the multi-physics simulation platform, suggesting a promising potential applicability for early powertrain development work focussed on drive cycle emissions.

PERFORMANCE EVALUATION OF AN ELECTRIC VEHICLE INTEGRATED WITH REGENERATIVE BRAKING SYSTEM USING PI CONTROLLER

Battery electric vehicles (BEVs) without regenerative braking mechanisms often suffer major drawbacks of limited driving range. Although extensive research works exist in electric vehicles integrated with regenerative braking, the performance evaluation of an electric ambulance, in the context of aerodynamic as well as energy recovery assessment from a complete vehicle modeling perspective based on the difference between the controlled dynamic speed and the drive cycle reference speed is not well reported. To compensate for the problem mentioned above, this paper aims to evaluate the performance of an electric ambulance (EA), integrated with a regenerative braking system (RBS) in comparison to an EA without a regenerative braking system (No RBS), in terms of aerodynamic drag coefficient values, state of charge (SOC), endurance efficiency, statistical correlation and mean absolute error (MAE) using proportional-integral (PI) controller. The SOLIDWORKS and SOLIDWORKS Flow Simulator were used to develop the EA CAD model and conduct aerodynamic analysis. MATLAB Simulink was used to model the EA complete EA system. The EA drive system was evaluated using three drive cycles (UDDS, FTP, and US06). The EA had an aerodynamic coefficient of 0.29. From the perspective of energy recycling, the EV-RBS yielded an extended drive range and appreciable gain in state of charge compared to EV-No RBS on the mentioned drive cycles. Generally, as the deceleration frequency increases from one drive cycle to another, the energy recycling increases, and the range increases correspondingly. In addition, the PI controller, which relied on speed error as a means of regulating the controlled speed, was found to be efficient, as the controlled speed was highly correlated to the reference speed. Overall, very low mean absolute errors in the vehicle speed were observed for the drive cycles considered.

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred.