The battery electric vehicles (BEVs) equipped with automated manual transmission (AMT) can realize gear-shift automatically and show many advantages in terms of reduction of fuel consumption and improvement of driving comfort and shifting quality. This paper focuses on the gear-shift control strategy for a clutchless AMT in a battery electric bus. Compared with the traditional ICE vehicles, the studied battery electric bus has the different powertrain structure, which consequently requires a different gear-shift strategy. First, the so-called clutchless AMT means that the clutch usually used in the conventional AMT vehicles is removed. Second, the synchronizer within the transmission is omitted and that means only the sleeves are employed to accomplish engagement of gears. In order to find the key factors that affect gear-shift operation of AMT in battery electric bus, the several gear-shift phases including “Gear Release”, “Synchronization”, and “Gear Engagement” are modeled. The analyses show that the gear-shift operation of clutchless AMT requires the motor with the capabilities of fast mode-switching (between torque output, speed control and free mode) and high-precise speed regulation. The corresponding gear-shift control strategy based on the previous modeling and analyzing is designed in this paper. Finally, the on-road test results are investigated and the conclusion is drawn.

The energy recovered with regenerative braking system can greatly improve energy efficiency of range-extended electric vehicle (R-EEV). Nevertheless, maximizing braking energy recovery while maintaining braking performance remains a challenging issue, and it is also difficult to reduce the adverse effects of regenerative current on battery capacity loss rate (Q_loss,%) to extend its service life. To solve this problem, a revised regenerative braking control strategy (RRBCS) with the rate and shape of regenerative braking current considerations is proposed. Firstly, the initial regenerative braking control strategy (IRBCS) is researched in this paper. Then, the battery capacity loss model is established by using battery capacity test results. Eventually, RRBCS is obtained based on IRBCS to optimize and modify the allocation logic of braking work-point. The simulation results show that compared with IRBCS, the regenerative braking energy is slightly reduced by 16.6% and Q_loss,% is reduced by 79.2%. It means that the RRBCS can reduce Q_loss,% at the expense of small braking energy recovery loss. As expected, RRBCS has a positive effect on prolonging the battery service life while ensuring braking safety while maximizing recovery energy. This result can be used to develop regenerative braking control system to improve comprehensive performance levels.

Currently, the researches on the regenerative braking system (RBS) of the range-extended electric vehicle (R-EEV) are inadequate, especially on the comparison and analysis of the multi-objective optimization (MOO) problem. Actually, the results of the MOO problem should be mutually independent and balanced. With the aim of guaranteeing comprehensive regenerative braking performance (CRBP), a revised regenerative braking control strategy (RRBCS) is introduced, and a method of the MOO algorithm for RRBCS is proposed to balance the braking performance (BP), regenerative braking loss efficiency (RBLE), and battery capacity loss rate (BCLR). Firstly, the models of the main components related to the RBS of the R-EEV for the calculation of optimization objectives are built in MATLAB/Simulink and AVL/Cruise. The BP, RBLE, and BCLR are selected as the optimization objectives. The non-dominated sorting genetic algorithm (NSGA-II) is applied in RRBCS to solve the MOO problem, and a group of the non-inferior Pareto solution sets are obtained. The simulation results show a clear conflict that three optimization objectives cannot be optimal at the same time. Then, we evaluate the performance of the proposed method by taking the individual with the optimal CRBP as the final optimal solution. The comparation among BP, RBLE, BCLR, and CRBP before and after optimization are analyzed and discussed. The results illustrate that characteristic parameters of RRBCS is crucial to optimization objectives. After parameters optimization, regenerative braking torque works early to increase braking energy recovery on low tire-road adhesion condition, and to reduce the battery capacity loss rate at the expense of small braking energy recovery on the medium tire-road adhesion condition. In addition, the results of the sensitivity analysis show that after parameter optimization, RRBCS is proved to perform better road adaptability regarding the distribution of solutions. These results thoroughly validate the proposed approach for multi-objective optimization of RRBCS and have a strong directive to optimize the control strategy parameters of RBS.

Trailing-edge serrations have proven to be valid applications of trailing edge noise mitigation for an airfoil, while the physical noise reduction mechanism has not been adequately studied. We performed simulations employing Large-eddy simulation and the Lighthill–Curle method to reveal the variation in the hydrodynamic field and sound source due to the trailing edge serrations. The grid resolution and computational results were validated against experimental data. The simulation results show that: the trailing edge serrations impede the growth of spanwise vortices and promote the development of streamwise vortices near the trailing edge and the wake; the velocity fluctuations in the vertical cross-section of the streamwise direction near the trailing edge are reduced for the serrated airfoil, thereby obviously reducing the strength of the pressure fluctuations near the trailing edge; and the trailing edge serrations decrease the distribution of the sound source near the trailing edge and reduce the local peak value of sound pressure level in a specific frequency range as well as the overall sound pressure level. Moreover, we observed that, in the flow around the NACA0012 airfoil, the location where the strong sound source distribution begins to appear is in good agreement with the location where the separated boundary layer reattaches. It is therefore effective to reduce trailing edge noise by applying serrations on the upstream of the reattachment point.

Power system parameter matching is one of the key technologies in the development of hybrid electric vehicles. The power source is the key component of the power system which composed of engine, motor, and battery. Reasonable power source parameters are conducive to improve the power, fuel economy, and emission performance of vehicles. In this paper, regarding the problem that the plug-in hybrid electric vehicle (PHEV) parameter matching needs to weigh different design objectives, a multi-objective optimization and matching method based on a genetic algorithm is proposed. The vehicle dynamic model is established based on MATLAB/Simulink (Mathworks in Natick, Massachusetts, USA), and the feasibility of the model is verified by simulation. The main performance parameters of the power source are matched by theoretical analysis, and the PHEV integrated optimization simulation platform is established based on Isight(Dassault Systemes in Paris, France) and MALTAB/Simulink. Power source components are optimized considering fuel economy and lightweight objectives under the performance constraints. Firstly, the optimal matching results under different weights are obtained by transforming different objectives into single objective, and the multi-island genetic algorithm is used to obtain the optimal matching results in which the equivalent fuel consumption of 100km is reduced by 1%. Then the Pareto solution is obtained using the NSGA-II algorithm. The optimal matching results can be found after determining the weights of different design objectives, which proves the effectiveness and superiority of the multi-objective optimization matching method. The optimization results show that compared with the original vehicle, the fuel economy effect is increased by 2.26% and the lightweight effect is increased by 8.26%.