Masakazu Mukai

Ecological Vehicle Control on Roads With Up-Down Slopes

This paper presents a novel development of an ecological (eco) driving system for running a vehicle on roads with up-down slopes. Fuel consumed in a vehicle is greatly influenced by road gradients, aside from its velocity and acceleration characteristics. Therefore, optimum control inputs can only be computed through anticipated rigorous reasoning using information concerning road terrain, model of the vehicle dynamics, and fuel consumption characteristics. In this development, a nonlinear model predictive control method with a fast optimization algorithm is implemented to derive the vehicle control inputs based on road gradient conditions obtained from digital road maps. The fuel consumption model of a typical vehicle is formulated using engine efficiency characteristics and used in the objective function to ensure fuel economy driving. The proposed eco-driving system is simulated on a typical road with various shapes of up-down slopes. Simulation results reveal the ability of the eco-driving system in significantly reducing fuel consumption of a vehicle. The fuel saving behavior is graphically illustrated, compared, and analyzed to focus on the significance of this development.

On board eco-driving system for varying road-traffic environments using model predictive control

This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.

A battery management system using nonlinear model predictive control for a hybrid electric vehicle

Abstract This present paper introduces a battery management system using nonlinear model predictive control for a hybrid electric vehicle. This paper adds two new contributions to this field. First, the apparent relationship between the battery power and the future road load is addressed in the cost function of the fuel economy optimal control problem with a simplified hybrid electric vehicle energy management system model. Second, it examines quantitatively the effects of operating the engine along the best efficiency line of the engine with a continuously variable transmission using a commercially available hybrid electric vehicle energy management electronic control unit simulator. Effectiveness of the proposed algorithm is validated by the JSAE-SICE benchmark problem II simulator.

Model predictive control of a power-split hybrid electric vehicle system

This paper presents a model predictive control approach for the energy management problem of a power-split hybrid electric vehicle system. The model predictive control is suggested to optimally share the road load between the engine and the battery. By analyzing the configuration of the power-split hybrid electric vehicle system, we developed a simplified model for better implementation of model predictive control. The model predictive control problem is solved using numerical computation method: continuation and generalized minimum residual method. Computer simulation results showed that the fuel economy was better using the model predictive control approach than the ADVISOR rule-based approach in three cases. We conclude that the model predictive control approach is effective for the application of power-split hybrid electric vehicle systems energy management and has the potential for real-time implementation. The simplified modeling method of the power-split hybrid electric vehicle system configuration can be applied to other configurations of hybrid electric vehicle.

Ecological driving based on preceding vehicle prediction using MPC

Abstract This paper presents an ecological (eco) driving system based on prediction of the preceding vehicle using model predictive control. At any measured road-traffic states it computes the optimal vehicle control input using the models of the vehicle dynamics and fuel consumption. The prediction model the preceding vehicle is formulated based on experimentally obtained driving data. The proposed system is evaluated for driving on urban roads containing traffic control signals at the intersections using the microscopic transport simulator AIMSUN. Significant improvement in fuel efficiency by introducing the model of the preceding vehicle has been confirmed from the simulation results.

Mild merging path generation method with optimal merging point based on MPC

Abstract In this paper, a merging path generation method based on model predictive control (MPC) method is proposed to optimize the merging point, and the merging path of the merging vehicle, while the motion of the main lane vehicle is optimized at the same time. To simplify the optimization problem which is used to generate the merging trajectory, the longitudinal movement of the merging vehicle is related to the lateral movement of it. To reproduce and make full use of the cooperative driving behavior in merging, the motions of the two relevant vehicles are optimized at the same time. A variable which enables the translation of the merging trajectory of the merging vehicle is introduced into the state of the system. So that when it is necessary to translate the merging trajectory of the merging vehicle for some reason, for example, keeping safe distance, the merging trajectory would be translated and thus the merging point can be optimized. In consideration of the upper bounds of the accelerations and lower bounds of the decelerations that actual vehicles can produce, during merging the accelerations and decelerations of both the relevant vehicles are restricted to appropriate ranges. A computer simulation of three typical merging cases, whose initial conditions are set according to the data drawn from actual merging scene, was conducted on a personal computer to verify the effectiveness of the proposed method. It is shown that the computer simulation results for all the three cases are reasonable. The computational time for all of the three cases is much shorter compare to the time step; therefore the proposed method is quite probable to be implemented on actual vehicles.

Driver Assistance Algorithm for Automotive Collision Avoidance Using Optimization Feasibility

Abstract This paper considers a collision avoidance problem of the vehicle and the moving obstacle. The prohibited region is defined for the vehicle and the obstacle considering a specified size of them. The problem is formulated as a mixed integer programming problem. In the problem the obstacles and environments around the automobile can be represent as inequality conditions. Then the driver assistance algorithm for the collision avoidance using the feasibility of the optimization is proposed. Computer simulation shows that the proposed algorithm can provide appropriate control input for collision avoidance.

A centralized control system for ecological vehicle platooning using linear quadratic regulator theory

This paper presents an ecological vehicle platooning control system that aims in reducing overall fuel consumption of the vehicles in a platoon. A centralized linear quadratic regulator system for controlling the vehicles in the platoon has been developed considering the aerodynamic characteristics of the vehicle and the resistance due to the road slope. The proposed control system is simulated on a highway with up–down slopes for high speed driving. Its fuel saving performance is compared with a conventional decentralized vehicle platooning control system. Computer simulation results reveal the significant improvement in fuel economy by the proposed control system.