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In the Plug-in hybrid electric bus, the power train parameter and control strategy significantly affect the economy and dynamic performance. Thus, the simultaneous optimization of power train parameter and control strategy is designed for the trade-off between the dynamic and economic performance. Depending on the parallel electric auxiliary control strategy in a plug-in hybrid electric bus, a vehicle dynamic simulation model is built with the software AVL Cruise. Aiming at the minimization of equivalent gas consumption and acceleration time from 0 to 50kmph, the gear ratio, final drive ratio, gear shifting strategy and control strategy are chosen as optimal variable, which significantly impact power performance and fuel economy. The driving performance and the driving range with full battery are considered as constraints. Based on the software Isight, multi-objective optimization model is built by adopting non-dominated sorting genetic optimization algorithm (NSGA-II).

This paper presents a novel application of hydraulic electromagnetic energy-regenerative shock absorber (HESA) into commercial vehicle suspension system and vehicle road performance are simulated by the evaluating indexes (e.g. root-mean-square values of vertical acceleration of sprung mass, dynamic tire-ground contact force, suspension deflection and harvested power; maximum values of pitch angle and roll angle). Firstly, the configuration and working principle of HESA are introduced. Then, the damping characteristics of HESA and the seven-degrees-of-freedom vehicle dynamics were modeled respectively before deriving the dynamic characteristics of a vehicle equipped with HESA. The control current is fixed at 7A to match the similar damping effect of traditional damper on the basis of energy conversion method of nonlinear shock absorber.

Dual-Clutch-Transmission (DCT) is one kind new automatic transmission which has double clutch structure. The most important unit of DCT is Transmission-Control-Module (TCM).In the development process of TCM, simulation is an important research tools. We have analyzed the DCT principle of work, established its mathematical model, created the charge and discharge oil models of typical wet dual clutch transmission, established the control logic to unify and separate double clutch in turn, and also designed out the shift control using fuzzy control using MATLAB/Simulink software. Utilizing engine model, driver model, the DCT model, the TCM model, the vehicle model, established the vehicle simulation model, and implemented simulation; Result indicated that, the established model can correctly reflect the torque and speed change when shifted gears and can correctly realize the automatic shift gears.

In this paper a new pressure control method of a modified accumulator-type Electro-hydraulic Braking System (EHB) is proposed. The system is composed of a hydraulic motor pump, an accumulator, an integrated master cylinder, a pedal feel simulator, valves and pipelines. Two pressurizing modes are switched between by-motor and by-accumulator to adapt different pressure boost demands. A differentiator filtering raw sensor signal and calculating pedal speed is designed. By using the pedal feel simulator, the relationship between wheel pressures and brake force is decoupled. The relationships among pedal displacement, pedal force and wheel pressure are calibrated by experiments. A model-based PI controller with predictor is designed to lower the influences caused by delay. Moreover, a self-tuning regulator is introduced to deal with the parameter’s time-varying caused by temperature, brake pads wearing and delay variation.

Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed.

Deep Deterministic Policy Gradient (DDPG) is one of the Deep Reinforcement Learning algorithms. Because of the well perform in continuous motion control, DDPG algorithm is applied in the field of self-driving. Regarding the problems of the instability of DDPG algorithm during training and low training efficiency and slow convergence rate. An improved DDPG algorithm based on segmented experience replay is presented. On the basis of the DDPG algorithm, the segmented experience replay select the training experience by the importance according to the training progress to improve the training efficiency and stability of the training model. The algorithm was tested in an open source 3D car racing simulator called TORCS. The simulation results demonstrate the training stability is significantly improved compared with the DDPG algorithm and the DQN algorithm, and the average return is about 46% higher than the DDPG algorithm and about 55% higher than the DQN algorithm.

Road traffic accidents resulting from alcohol-impaired driving are increasing globally despite several measures, currently in place, to curb the trend. For this reason, recent research aims at integrating alcohol early-detection systems and driving simulator experiments to identify intoxicated drivers. However, driving simulator experiments on drunk driving have focused mostly on male participants than female drivers whose characteristics have scarcely been explored. Hence in this paper, vehicle dynamic control inputs on steering, braking, and acceleration performance of 75 licensed female drivers with an upshot of alcohol at four different blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) were investigated. The participants completed simulated driving in a fixed-based simulator experiment coupled with real-time ecological scenarios to extract discrete responses.

On-road testing of driving performance of the urban bus was carried out, and a representative urban bus driving cycle was developed after on-road testing, according to the test results. Then, the vehicle simulation software AVL CRUISE was used to simulate the dynamic behavior of the urban bus. It involves the simulation of complete drive train system and the driver behavior. The model is validated by comparing the results of the simulation to the results of the field test. Then the developed driving cycle is evaluated by fuel consumption resulted from the simulation and engine bench test on fuel economy.