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Knocking combustions heavily limits the efficiency of Spark Ignition engines. The compression ratio is limited in the design stage of the engine development, letting to Spark Advance control the task of reducing the odds of abnormal combustions. A detailed analysis of knocking events can help improving engine performance and diagnosis strategies. An effective way is to use advanced 3D CFD (Computational Fluid Dynamics) simulation for the analysis and prediction of combustion performance. Standard 3D CFD approach is based on RANS (Reynolds Averaged Navier Stokes) equations and allows the analysis of the mean engine cycle. However knocking phenomenon is not deterministic and it is heavily affected by the cycle to cycle variation of engine combustions. A methodology for the evaluation of the effects of CCV (Cycle by Cycle Variability) on knocking combustions is here presented, based on both the use of Computation Fluid Dynamics (CFD) tools and experimental information.

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.

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment have given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Lamborghini has recently invested in the development of a hybrid super sport car, due to performance and comfort reasons. Aventador series gearbox is an Independent Shift Rod gearbox with a single clutch and during gear shifts, as all the single clutch gearbox do, it generates a torque gap. To avoid the additional weight of a Dual Clutch Transmission, a 48V Electric Motor has been connected to the wheels, in a P3 configuration, to fill the torque gap, and to habilitate regenerative braking and electric boost functions.

As the essential of future driver assistance system, brake-by-wire system is capable of performing autonomous intervention to enhance vehicle safety significantly. Regenerative braking is the most effective technology of improving energy consumption of electrified vehicle. A novel brake-by-wire system scheme with integrated functions of active braking and regenerative braking, is proposed in this paper. Four pressure-difference-limit valves are added to conventional four-channel brake structure to fulfill more precise pressure modulation. Four independent isolating valves are adopted to cut off connections between brake pedal and wheel cylinders. Two stroke simulators are equipped to imitate conventional brake pedal feel. The operation principles of newly developed system are analyzed minutely according to different working modes. High fidelity models of subsystems are built in commercial software MATLAB and AMESim respectively.

Hybrid electric vehicles (HEV) are considered as the most cost effective solution, in the short term perspective, for the achievement of improved fuel economy (FE) and reduced emissions. This paper focuses on regenerative braking in a mild hybrid powertrain with infinitely variable transmission (IVT) and specifically on how its control strategy can be formulated and optimized. The study is conducted using a previously validated fully dynamic powertrain model. An initial investigation of the dynamic vehicle behaviour under braking conditions serves as the basis for the development of a control strategy for best braking performance and maximum energy recovery, the implementation of which requires a fully active and integrated brake control system. Limitations and constraints due to driveline configuration and driveability issues are considered and their effect evaluated. Simulation results show that fuel consumption reductions of 12% are achievable along a standard drive cycle.

Knocking combustions heavily influence the efficiency of Spark Ignition engines, limiting the compression ratio and sometimes preventing the use of Maximum Brake Torque (MBT) Spark Advance (SA). A detailed analysis of knocking events can help in improving the engine performance and diagnostic strategies. An effective way is to use advanced 3D Computational Fluid Dynamics (CFD) simulation for the analysis and prediction of the combustion process. The standard 3D CFD approach based on RANS (Reynolds Averaged Navier Stokes) equations allows the analysis of the average engine cycle. However, the knocking phenomenon is heavily affected by the Cycle to Cycle Variation (CCV): the effects of CCV on knocking combustions are then taken into account, maintaining a RANS CFD approach, while representing a complex running condition, where knock intensity changes from cycle to cycle.

In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition.