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In this paper, the performance simulation model of a domestic self-dumping truck was established using AVL-Cruise software. Then its accuracy was checked by the power performance and fuel economy tests which were conducted on the proving ground. The power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, overtaking acceleration time from 60 to 70km/h, maximum speed and maximum gradeability, while the composite fuel consumption per hundred kilometers was taken as an evaluation index of fuel economy. A L9 orthogonal array was applied to investigate the effect of three matching factors including engine, transmission and final drive, which were considered at three levels, on the power performance and fuel economy of the self-dumping truck. Furthermore, the grey relational grade was proposed to assess the multiple performance responses according to the grey relational analysis.

In this study, with the aim of reducing fuel consumption and improving power performance, the optimization for the driveline parameters of a self-dumping truck was performed by using a vehicle performance simulation model. The accuracy of this model was checked by the power performance and fuel economy tests. Then the transmission ratios and final drive ratio were taken as design variables. Meanwhile, the power performance of the self-dumping truck was evaluated through standing start acceleration time from 0 to 70km/h, maximum speed and maximum gradeability, while the combined fuel consumption of C-WTVC drive cycle was taken as an evaluation index of fuel economy. The multi-objective optimization for the power performance and fuel economy was then performed based on particle swarm optimization algorithm, and the Pareto optimal set was obtained. Furthermore, the entropy method was proposed to determine the weight of fuel consumption and acceleration time.

Utilizing the developed off-line simulation model of series hybrid power train the study on the influence of components' parameters on acceleration performance and fuel economy of transit bus is completed. Based on these the guideline strategies of parametric design of series hybrid power train for transit bus are brought forward in this paper. Given the condition of propulsion requirement the parametric design for this transit bus are performed targeting minimizing fuel consumption. It is conclusion that the appropriate components' parameters determined by means of parametric design can make series hybrid transit bus achieve much better acceleration performance and much lower fuel equivalent consumption than that of baseline transit bus.

A novel torque-coupling architecture for hybrid electric vehicles is proposed. The torque-coupling device is based on automated manual transmission (AMT), which is highly efficient and provides six gears for the engine and three gears for each motor to enable the engine and the motors to work at high-efficiency levels in most cases. The proposed power-shift AMT (P-AMT) does not have a hydraulic torque converter and wet clutches, which dampen the driveline shock. Thus, the drivability control of the P-AMT becomes a challenging issue. Accurate engine, motor model and transmission model have been built and the dynamic control of the gear shift process of PAMT in hybrid mode is simulated. The electric motors compensate for the traction loss during the gear shift of the engine.

To reach the goal of optimal performance match between engine and transmission, the dynamic characteristics of engine should be taken into consideration. In the paper, the dynamic torque and fuel consumption models of engine, described by a multi-layers feed forward neural network, were established. Based on that, the methods used to calculate the optimal dynamic and economical shift schedules with dynamic 3-parameters were put forward. The shift schedule with dynamic 3-parameters based on neural network model is proven to be superior to the shift schedule with only 2-parameters in both dynamic performance and fuel economy by the test.

With rapid development of the automobile industry and the growing maturity of the automotive electronic technologies, the distributed-traction electric vehicle with four-wheel-distributed steering/braking/traction systems is regarded as an important development direction. With its unique chassis structure, it is the ideal benchmark platform used to evaluate active safety systems. The distributed-traction electric vehicle with four-wheel-distributed steering system is essentially full drive-by-wire vehicle. With its flexible chassis layout and high control degrees-of-freedom, the full drive-by-wire electric vehicle acted as a kind of redundant system is an ideal platform for the research of integrated control. In this treatise, the longitudinal dynamics of the electric vehicle as well as its lateral and yaw motions are controlled simultaneously.

Under complex and extreme operating conditions, the road adhesion coefficient emerges as a critical state parameter for tire force analysis and vehicle dynamics control. In contrast to model-based estimation methods, intelligent tire technology enables the real-time feedback of tire-road interaction information to the vehicle control system. This paper proposes an approach that integrates intelligent tire systems with machine learning to acquire precise road adhesion coefficients for vehicles. Firstly, taking into account the driving conditions, sensor selection is conducted to develop an intelligent tire hardware acquisition system based on MEMS (Micro-Electro-Mechanical Systems) three-axis acceleration sensors, utilizing a simplified hardware structure and wireless transmission mode. Secondly, through the collection of real vehicle experiment data on different road surfaces, a dataset is gathered for machine learning training.

In the face of energy and environmental problems, how to improve the economy of fuel cell vehicles (FCV) effectively and develop intelligent algorithms with higher hydrogen-saving potential are the focus and difficulties of current research. Based on the Toyota Mirai FCV, this paper focuses on the short-term speed prediction algorithm based on the back propagation neural network (BP-NN) and carries out the research on the short-term speed prediction algorithm based on BP-NN. The definition of NN and the basic structure of the neural model are introduced briefly, and the training process of BP-NN is expounded in detail through formula derivation. On this basis, the speed prediction model based on BP-NN is proposed. After that, the parameters of the vehicle speed prediction model, the characteristic parameters of the working condition, and the input and output neurons are selected to determine the topology of the vehicle speed prediction model.

The steer-by-wire system has been widely studied due to many advantages such as good controllability. In the system, the steering column is cancelled and the driver can't feel the feedback torque (also called steering feeling) coming from the ground. Therefore a steering feeling feedback system is needed. In this paper, we propose a simple method to calculate desired feedback torque based on a nonlinear 2DOF vehicle model. The vehicle model contains the nonlinearity of tire. So that the proposed method is also appropriate for big acceleration conditions. Besides that, the properties of steering system such as friction and stiffness are also taken into consideration. As for conventional steering system, driver can only feel part of the feedback torque due to the power assist system. In order to provide steering feeling similar to conventional steering system, a weighting function is proposed to compensate the influence of power assist system.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

The traditional energy management algorithm is mainly based on a single driving cycle, it is obvious that many factors might be often neglected by designer, such as different driving cycles would suit for different control strategies. But they tend to make decisions on the balance of torque distribution and battery power that based on a single driving cycle. Therefore, it is very difficult to achieve the optimal control in each case. In this paper we introduce a new design concept of Multi-operating mode energy management, a mathematical model of the energy management applied to a hybrid vehicle system is presented. Results of simulations using the model with the Multi-operating mode energy management were compared with results of simulations using a model with the single mode energy management, allowing the energy efficiency evaluation of the proposed energy management system.

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

Supercapacitor has the merits of low resistance and long lifecycle ability. When combined with battery, they can alleviate the burthen of battery, increase the battery's working efficiency and prolong its lifecycle. This paper introduces a control architecture based on balancing of SOC and algorithm based on fuzzy logic, Aiming at the two different cycles that have sufficient and insufficient energy that can be recovered from braking unlikely, this paper puts forward the methods of on-line adjusting fuzzy control parameters. Consequently, simulation was performed,and the results validate the effective adapting capacity of the control logic under different driving cycles.

In this paper, an intelligent tire system is designed to estimate tire force by detecting the tire carcass deformation. The intelligent tire system includes a set of marker points on the inner liner of the tire to locate the position of tire carcass and a camera mounted on the rim to capture the position of these points under different driving conditions. An image recognition program is used to identify the coordinates of the marker points in order to determine the deformation of the tire carcass. According to the tire carcass stiffness test and the general tire carcass deformation theory, an approximate linear relationship between tire force and carcass deformation in all directions was obtained. The vertical force of the tire is determined by the distance between adjacent marker points. The longitudinal force and lateral force of the tire are estimated by measuring the longitudinal and lateral displacements of the marker points.

This paper proposes a dynamic obstacle avoidance system to help autonomous vehicles drive on high-speed structured roads. The system is mainly composed of trajectory planning and tracking controllers. The potential field (PF) model is introduced to establish a three-dimensional potential field for structured roads and obstacle vehicles. The trajectory planning problem that considers the vehicle’s and tires’ dynamics constraints is transformed into an optimization problem with muti-constraints by combining the model predictive control (MPC) algorithms. The trajectory tracking controller used in this paper is based on the 7 degrees of freedom (DOF) vehicle model and the UniTire tire model, which was discussed in detail in previous work [25, 26]. The controller maintains good trajectory tracking performance even under extreme driving conditions, such as roads with poor adhesion conditions, where the car’s tires enter the nonlinear region easily.