This two part paper proposes an intelligent energy management agent (IEMA) for parallel hybrid vehicles. IEMA incorporates a driving situation identification component whose role is to assess the driving environment, the driving style of the driver and the operating mode of the vehicle using long and short term statistical features of the drive cycle. This information is subsequently used by the torque distribution and charge sustenance components of IEMA to determine the power split strategy, which is shown to lead to enhanced fuel economy and reduced emissions. In Part I, the overall architecture of IEMA is presented and the driving situation identification process is described. It is specifically shown that a learning vector quantization (LVQ) network can effectively determine the driving condition using a limited duration of driving data. The overall performance of the system under a range of drive cycles is discussed in the second part of this paper.

An energy management control system for a parallel hybrid electric vehicle is presented. The proposed system incorporates an optimization scheme to assess the amount of engine torque for generating propulsive power (torque distribution task) as well as a charge sustaining scheme to ensure that battery's state of charge is maintained at sufficiently high level. In order to accomplish the torque distribution task, torque distribution control problem is formulated as a multi-objective nonlinear optimization problem, and recast and solved as a single objective linear optimization problem. Furthermore, a new vehicle-mode-based state-of-charge compensator is developed to accomplish the charge sustaining task. Computer simulation work is carried out to evaluate the proposed energy management system. Finally, through sensitivity analysis, robustness of the solution to changes in the parameters of the objective functions is investigated.

In diesel engines, variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems are used to increase engine specific power and reduce NO_x emissions, respectively. Because the dynamics of both the VGT and EGR are highly nonlinear and coupled to each other, better performance may be attained by substituting nonlinear multiple input, multiple output (MIMO) controllers for the existing conventional lookup table-based linear controllers. This paper presents a coordinated VGT/EGR control system for common-rail direct injection diesel engines. The objective of the control system is to track target mass air flow and target intake manifold pressure by adjusting the EGR and VGT actuator positions. We designed a nonlinear MIMO control system using a neural control scheme that adopts an indirect adaptive control approach. The neural control system is comprised of a neural network identifier, which mimics the target air system, and a neural network controller, which calculates the actuator positions. The proposed control system has been validated with engine experiments under transient operating conditions. It was demonstrated from experimental results that the proposed control system shows improved target value tracking performance over conventional VGT/EGR control system.

This study presents a multi-robot navigation strategy based on a multi-objective decision-making algorithm, the Fuzzy Analytic Hierarchy Process (FAHP). FAHP analytically selects an optimal position as a sub-goal among points on the sensing boundary of a mobile robot considering the following three objectives: the travel distance to the target, collision safety with obstacles, and the rotation of the robot to face the target. Alternative solutions are evaluated by quantifying the relative importance of the objectives. As the FAHP algorithm is insufficient for multi-robot navigation, cooperative game theory is added to improve it. The performance of the proposed multi-robot navigation algorithm is tested with up to 12 mobile robots in several simulation conditions, altering factors such as the number of operating robots and the warehouse layout.