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A new concept electric vehicle (EV) with four-in-wheel motor is developed to make EVs spread widely. Each motor is mounted inside a wheel and can be controlled individually. The batteries and electrical components are placed in the hollow space of frame structure under the seats. Such a compact configuration permits packaging flexibility by eliminating the central drive motor and driveline components, including the transmission, the mechanical differential, the universal joints and the drive shaft. Apart from many mechanical advantages of such a structure, high performances of chassis control systems such as traction control, electronic differential control, direct yaw moment control and regenerative braking control systems are employed due to the rapid response and precise torque control of the motor. Simulation is conducted in ADVISOR and Matlab/Simulink environment.

A flat neural network is designed for the on-line state prediction of engine. To reduce the computational cost of weight matrix, a fast recursive algorithm is derived according to the pseudoinverse formula of a partition matrix. Furthermore, the forgetting factor approach is introduced to improve predictive accuracy and robustness of the model. The experiment results indicate that the improved neural network is of good accuracy and strong robustness in prediction, and can apply for the on-line prediction of nonlinear multi input multi output systems like vehicle engines.

Automatic identification of road profiles ensures primarily right working condition of an anti-lock braking system (ABS). This paper presents a practical strategy to identify road conditions according to braking pressure, wheel slip ratio and angular deceleration. A fuzzy controller is designed to follow the target wheel slip ratio determined by road conditions. Simulation tests have been undertaken respectively on single and variable adhesion road surfaces by using a 7-DOF nonlinear vehicle model, accounting for nonlinearity of suspension and tire. It is shown that the ABS fuzzy controller can identify changes of road conditions precisely and therefore modulate the control scheme, so as to obtain the maximum road friction and good lateral stability. In comparison with a conventional PID controller, the present fuzzy controller possesses of more robustness and better performance.

Since the nonlinearity which inherently exists in vehicle system need to be considered in active suspension control law design, a new control strategy is proposed for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a PID controller and a fuzzy logic controller in two loops. In this paper, the investigation is mainly focused on vehicle ride comfort performance and simulations in straight running operating condition are presented. The control goal is to minimize vehicle body vertical and pitch accelerations for passenger comfort. The control system consists of two parallel control loops. One loop, using PID control, is to minimize vehicle body vertical acceleration; and the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors.

The paper describes the idea of constructing the rapid development system of vehicle electric control subsystem in details by using hardware-in-the-loop (HiL) simulation technology for cost reduction in today's competitive business environment. This rapid development system is probably most effectively used in parallel with a new vehicle electronic control product. Once a desired simulation results are achieved, it can be verified by setting up the same configurations in the new product and comparing results. The feasibility of using such rapid development system is demonstrated through vehicle ABS algorithm development. Significant reduction of developing cycle time and cost has been achieved with the aid of this powerful tool and promising effectiveness of ABS algorithm has been validated in vehicle field test.

A nonlinear controller for electronically controlled clutches of Automatic Mechanical Transmission (AMT) vehicle is designed based on the variable structure control strategy. During the automatic clutch engagement, influence of dynamic characteristics of clutch on the longitudinal driving comfort and fuel economy of the vehicle is examined and discussed. It is difficult to control the engaging process of the automatic clutch because there exist nonlinearities and uncertainties due to variation of vehicle characteristics, driving condition and the driver's intention. Good performance of the system requires high standards in design of the control system, which are dependent on an accurate model of the dynamic system, a robust controller and so on. In this paper, a nonlinear controller is proposed based on variable structure control (VSC) for the purpose of improving dynamic responses for engagements of the automated clutches at vehicle start-up and shift.

In this paper, an investigation is made to the friction clutch engagement control of automotive AMT systems based on a nonlinear dynamic model with double inputs. According to friction torque transmission characteristics during clutch engagement, an equivalent, fully controllable and linearized model and the feedback linearization control are derived from the original system with nonlinearities via homomorphic transforms. By the resulting mathematical modeling, computer simulations are made both for the original nonlinear and feedback linearized systems with incorporation of ordinary PID controllers to follow ideal vehicle dynamic responses. It has been shown by comparison between the two sets of numerical results that the feedback linearization control designed for the nonlinear system is of fine accuracy and robustness in model tracking behaviors of clutch engagements.

For a semi-active suspension design, an important subject is to determine the control law which can achieve good performance both in ride and handling performance. Because of its superiority in non-linear control systems and capability of learning on-line, the fuzzy neural networks (FNNs) control scheme is proposed in this paper for a semi-active suspension system with dynamic absorber. The quarter vehicle model is described by a nonlinear system with three DOF subject to irregular excitation from a road surface and FNNs control scheme is employed. The on-line learning of FNNs to optimize fuzzy inference system is presented. Four kinds of methods, including passive suspension respectively with and without dynamic absorber, semi-active suspension respectively using fuzzy control and FNNs control, are investigated by computer simulation and comparison is made.