Vehicle Dynamic Control for In-Wheel Electric Vehicles Via Temperature Consideration of Braking Systems

?Vehicle dynamic control (VDC) systems play an important role with regard to vehicle stability and safety when turning. VDC systems prevent vehicles from spinning or slipping when cornering sharply by controlling vehicle yaw moment, which is generated by braking forces. Thus, it is important to control braking forces depending on the driving conditions of the vehicle. The required yaw moment to stabilize a vehicle is calculated through optimal control and a combination of braking forces used to generate the calculated yaw moment. However, braking forces can change due to frictional coefficients being affected by variations in temperature. This can cause vehicles to experience stability problems due an improper yaw moment being applied to the vehicle. In this paper, a brake temperature estimator based on the finite different method (FDM) was proposed with a friction coefficient estimator in order to solve this problem. The developed braking characteristic estimation model was used to develop a VDC cooperative control algorithm using hydraulic braking and the regenerative braking of an in-wheel motor. Performance simulations of the developed cooperative control algorithm were performed through cosimulation with MATLAB/Simulink and CarSim. From the simulation results, it was verified that vehicle stability was ensured despite any changes in the braking characteristics due to brake temperatures.     

Numerical Study for Brake Squeal by Machining Patterns on Frictional Surface

Automotive brake noise has become a stubborn problem as automotive cars achieve higher driving torques, since that the increased torque induces the generation of severe noise dissipation during brake operation. Moreover, the global brake tuning market for achieving higher performance of the vehicle has expanded recently. The need to control the noise grows more in this connection. The tuning brake kits have employed cross-drilled and slotted machining pattern on the surface of the rotor. These designs have advantages to improve air ventilation, temperature control, and surface cleaning of brake pad. However, the effects of modal frequency by patterned rotor surfaces are rarely discussed, even if it is highly related with brake squeal phenomenon. Therefore, this study deals with the relationship between patterned surfaces and brake squeal through the numerical methods. The commercial software of a finite element analysis is employed for calculation by varying geometric design factors of each rotor pattern. As a result, the cross-drilled machining patterns are concluded to be an influential factor for in-plane mode frequency while the slotted patterns have more leverage for out-of-plane mode frequency.     

Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions.