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By computing the velocity trajectory based on the advancing road and traffic information, the predictive cruise control (PCC) system can significantly reduce vehicle energy consumption. As the weight factors of different objective item have an important influence on the control results of PCC system, this paper proposed a novel optimization method with dynamic weight factors according to the classification of road slope for 4 wheel drive intelligent (4WD) electric vehicles. In order to determine the weight coefficient adjustment method, the influence of road slope on the PCC performance was analyzed and a road category identification method was presented. The results of simulation indicated that the proposed PCC based on dynamic weight coefficients can improve the adaptability of PCC on different categories of road. Compared with the traditional PCC with constant weight coefficient, the maximum energy-saving rate can reach 32.3% at a small cost of driving distance 3.86% on rapid downhill road. For other type of road, uphill and continuously undulating road, the energy-saving rate was low, where it was more important to improve the driving efficiency by 1.46%.

In a battery-electric vehicle, a representative electric vehicle, there is a growing demand for performance and one-charge mileage improvement. As an alternative to such improvements, the capacity of the battery has been increased; however, due to the corresponding increase in the weight of the battery and the limited space in the vehicle, increasing the capacity of the battery also has limitations. Therefore, researches are being actively conducted to improve system operation efficiency to overcome such limitations. This paper proposes a distributing method of the driving forces to a battery-powered electric shuttle bus for last-mile mobility equipped with the decentralized driving system while taking into account voltage changes of the input terminals due to changes in the battery charge. The system operation efficiency changes were compared and evaluated by performing energy consumption analysis using ‘Manhattan Bus Driving Cycle’ at low voltage condition (SOC 20%). Various analyzes were performed and compared, such as the uniform distribution method of driving forces of the front and rear wheels (Uniform), the optimization method without considering the input terminal voltage change (Vnorm = 90 V), and the optimization method considering the input terminal voltage change (Vdclink). As a result, it shows that the proposed algorithm can improve 6.0% compared to the conventional uniform driving force distribution method (Uniform). Moreover, it shows that the real-time optimization method without considering the input voltage change (Vnorm = 90 V) can improve 5.3% compared to the uniform distribution method. The proposed method can obtain an additional 0.7% increase in total cost compared to the existing optimization method, which shows that the vehicle system has cost-effectiveness by reducing the battery capacity required to achieve the same mileage.

In this paper, a novel hybrid vehicle chassis is designed without an assisted steering system and its kinematics model, the dynamics model, and the control system. The existing steering mechanism does not provide enough torque or has a shortage of mechanical strength and control when sinking into deeper muddy road. The proposed design can solve these problems smoothly. Initially, the new 4- wheel-drive (4WD) hybrid vehicle chassis vehicle platform and self-steering mechanism are designed, and Its kinematics and dynamics models are built. The linkages are added between the front and rear bodies to optimize the control model. Finally, traditional proportion-integration-differentiation (PID) feedback control loop is employed to track the reference torque of 4 independent wheels. The new hybrid vehicle chassis steering system has been tested in the actual muddy agricultural field. The results are verified using a prototype and real vehicle. The proposed design is proved feasibility not only has a smaller turning radius but also has a higher control precision than an existing vehicle, which ensures the flexibility of the vehicle and can control the direction as well. Besides, the proposed design is best for modern agricultural vehicle and can be implemented in commercial and private vehicles. [ABSTRACT FROM AUTHOR]

In a battery-electric vehicle, a representative electric vehicle, there is a growing demand for performance and one-charge mileage improvement. As an alternative to such improvements, the capacity of the battery has been increased; however, due to the corresponding increase in the weight of the battery and the limited space in the vehicle, increasing the capacity of the battery also has limitations. Therefore, researches are being actively conducted to improve system operation efficiency to overcome such limitations. This paper proposes a distributing method of the driving forces to a battery-powered electric shuttle bus for last-mile mobility equipped with the decentralized driving system while taking into account voltage changes of the input terminals due to changes in the battery charge. The system operation efficiency changes were compared and evaluated by performing energy consumption analysis using 'Manhattan Bus Driving Cycle' at low voltage condition (SOC 20%). Various analyzes were performed and compared, such as the uniform distribution method of driving forces of the front and rear wheels (Uniform), the optimization method without considering the input terminal voltage change (Vnorm = 90 V), and the optimization method considering the input terminal voltage change (Vdclink). As a result, it shows that the proposed algorithm can improve 6.0% compared to the conventional uniform driving force distribution method (Uniform). Moreover, it shows that the real-time optimization method without considering the input voltage change (Vnorm = 90 V) can improve 5.3% compared to the uniform distribution method. The proposed method can obtain an additional 0.7% increase in total cost compared to the existing optimization method, which shows that the vehicle system has cost-effectiveness by reducing the battery capacity required to achieve the same mileage. [ABSTRACT FROM AUTHOR]