Reza Langari

Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process

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.

Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results

This paper represents the second part of a two-part paper on development of an intelligent energy management agent (IEMA) for parallel hybrid vehicles. In this part, energy management strategies for the torque distribution and charge sustenance tasks are established and implemented. Driving situation awareness-based fuzzy rule bases are developed to make intelligent decisions on the power split function. A charge sustenance strategy is developed in parallel to maintain adequate reserves of energy in the storage device for supporting an extended range of driving. Simulation study is conducted for the proposed IEMA and performance results are analyzed to evaluate its viability as a possible solution to and an extendable framework for energy management for parallel hybrid electric vehicles.

On the stability issues of linear Takagi-Sugeno fuzzy models

Stability issues of linear Takagi-Sugeno (TS) fuzzy models (1985, 1992) are investigated. We first propose a systematic way of searching for a common matrix, which, in turn, is related to stability for N subsystems that are under a pairwise commutative assumption. The robustness issue under uncertainty in each subsystem is then considered. We then show that the pairwise commutative assumption can, in fact, be relaxed by a similar approach as that for uncertainty. The result is applicable to a rather broad class of TS models, which are nonHurwitz and/or nonpairwise commutative.

Robust fuzzy control of a magnetic bearing system subject to harmonic disturbances

Proposes a robust fuzzy logic-based control scheme for a rotating active magnetic bearing (AMB) system. We represent the nonlinear magnetic bearing by means of a Takagi-Sugeno-Kang fuzzy model. Subsequently a systematic synthesis procedure is used to derive a nonlinear fuzzy logic control strategy that overcomes the inherent characteristics of displacement sensitivity and position-dependent nonlinearity of the AMB. Experimentation and simulation results demonstrate that the proposed fuzzy controller yields robustness against harmonic disturbances and parameter uncertainties.

An energy management and charge sustaining strategy for a parallel hybrid vehicle with CVT

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 batterys 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.

A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation

A defuzzification strategy is presented that can handle the possibility of conflicting control commands during the defuzzification process while still producing smooth transitions. The strategy, which is the centroid of the largest area, has parameters that can be chosen to make it behave like both the centroid strategy and the mean of the maximum membership strategy. The strategy addresses the problem of prohibitive information in fuzzy control command formulation. This strategy was applied to a mobile robot path execution system and it was shown that this strategy overcomes some of the deficiencies of other strategies. This strategy not only makes the best choice when several are given, but also avoids abrupt transitions in the control command.<<ETX>>

An LMI-based H ∞ fuzzy control system design with TS framework

Abstract This paper proposes a systematic design methodology for Takagi–Sugeno (TS) model based fuzzy control systems with guaranteed H ∞ performance and additional constraints on the closed-loop pole locations. These objectives are formulated in terms of a convex optimization problem involving linear matrix inequality (LMI). To demonstrate its usefulness, the proposed design methodology is applied to the problem of robust regulation of a nonlinear magnetic bearing system. Experimental and simulation results show that the proposed fuzzy controller yields the optimal disturbance rejection performance and the desired transient response characteristics.

Fuzzy torque distribution control for a parallel hybrid vehicle

A fuzzy torque distribution controller for energy management (and emission control) of a parallel hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy and emissions.

Model-Based Multi-input, Multi-output Supervisory Semi-active Nonlinear Fuzzy Controller

: The authors recently proposed a new multi-input, single-output (MISO) semi-active fuzzy controller for vibration control of seismically excited small-scale buildings. In this article, the previously proposed MISO control system is advanced to a multi-input, multi-output (MIMO) control system through integration of a set of model-based fuzzy controllers that are formulated in terms of linear matrix inequalities (LMIs) such that the global asymptotical stability is guaranteed and the performance on transient responses is also satisfied. The set of model-based fuzzy controllers is divided into two groups: lower level controllers and a higher level coordinator. The lower level fuzzy controllers are designed using acceleration and drift responses; while velocity information is used for the higher level controller. To demonstrate the effectiveness of the proposed approach, an eight-story building structure employing magnetorheological (MR) dampers is studied. It is demonstrated from comparison of the uncontrolled and semi-active controlled responses that the proposed design framework is effective in vibration reduction of a building structure equipped with MR dampers.

A hybrid intelligent system for fault detection and sensor fusion

In this paper, an efficient new hybrid approach for multiple sensor fusion and fault detection is proposed, addressing the problem with multiple faults, which is based on conventional fuzzy soft clustering and artificial immune systems. For this new approach, requires no prior knowledge or information about the sensors, or the system behavior, and no learning processes are required. The proposed hybrid approach consists of two main phases. In the first phase a single fuser for the input sensor signals is generated using the fuzzy clustering c-means algorithm. The fused output is based on the cluster centers that contain the maximum number of the input elements. In the second phase a fault detector was generated base on the artificial immune system AIS.