Chenghui Zhang

PSO algorithm-based optimization of plug-in hybrid electric vehicle energy management strategy

The energy management strategy renders significant impacts on the operating performance of plug-in hybrid electric vehicles (PHEV), which are similar to the traditional hybrid electric vehicle. A comprehensive methodology based on Particle Swarm Optimization (PSO) is presented in the paper to achieve parameter optimization for the energy management strategy, with a view to reducing the fuel consumption of PHEV. The parameters of energy management strategy are set as the optimized variables by PSO, with dynamic performance index of PHEV being defined as the constraint condition. Computer simulations are hence carried out, which show the PSO scheme gives much preferable results to original energy management strategy, and thereby the fuel consumption of PHEV can be effectively reduced without sacrificing PHEVs dynamic performance.

An improved energy optimization control strategy for electric vehicle drive system

Electric vehicles must extremely improve its electric drive system efficiency and effectively using its limited energy. In this paper, an improved energy optimization control strategy for electric vehicle drive system is proposed based on the typical drive cycle operation characteristics analysis. An efficiency optimization strategy based on loss model is used to reduce the operation losses of electric drive system at light load, and a fast torque response control algorithm is given to reduce the influence of electric vehicle dynamic response speed caused by efficiency optimization operation. An efficient braking force distribution strategy is proposed then, which assigns the braking force to the regenerative braking as much as possible under the condition that the vehicle possesses good stability during braking process. The comparative simulation results in advanced vehicle simulator (ADVISOR) software show that there is obvious improvement in the energy usage and the electric vehicles travel distance is lengthened, which confirms the validity of the improved energy optimization control strategy proposed in this paper.

Comparative study of induction motor efficiency optimization control strategy for electric vehicle

Electric vehicles must extremely improve its electric drive system efficiency and effectively use its limited energy. In this paper, a structure diagram of the induction motor in synchronously rotating frame of reference including iron loss is given at first, the losses of induction motor in operation is discussed and an efficiency optimization control strategy of induction motor driving system based on loss model is proposed. then a comprehensive analysis of the efficiency optimization control strategy based on search, minimum stator current and loss model respectively is presented to discuss the distinction and links among them. The experiment system is built with DSP TMS320LF2407A, the experimental results show that the loss model based efficiency optimization control strategy can reach global optimum without additional hardware. It has a fast optimization speed and a fairly stable torque and speed in the optimization process.

Guaranteed Cost Control of Stochastic Uncertain Systems with Time-varying Delays

The problem of guaranteed cost robust stable control is considered via state feedback for a class of uncertain stochastic systems with time-varying delay, where the uncertainties are norm bounded. Sufficient conditions for the existence of guaranteed cost controller are given in terms of linear matrix inequalities (LMI). Furthermore, two convex optimization problems with LMI constraints are formulated to design the optimal guaranteed cost controller which minimizes the guaranteed cost of the closed-loop uncertain system with known or unknown initial function. The unknown initial function is assumed to be a white noise process with zero expectation function and unit covariance function. A numerical example is given to illustrate the design method

An Improved Energy Management Strategy for Parallel Hybrid Electric Vehicle

Effective control strategy (EMS) is very important for high fuel economy, low emission and good driving performance of hybrid electric vehicles. First, the operation mode of parallel hybrid electric vehicles was analyzed and the rule-based EMS was introduced. Then the equivalent fuel consumption minimization strategy (ECMS) was modified by the variation of battery state of charge (SOC) and introduced to optimize the EMS. Finally, this EMS was implemented in JA1015 drive cycle and analyzed by advanced vehicle simulation software CRUISE. The simulation results indicate that the improved EMS reduces fuel consumption effectively by distributing the torque between engine and motor properly, and it also sustains the battery SOC in a proper range

Decentralised regulation of nonlinear multi-agent systems with directed network topologies

ABSTRACT This paper aims to address the leader–follower regulation problem of multi-agent systems with directed network topologies, where the agents are described by feedforward nonlinearities with the growth rate being unknown a priori. Both the state feedback regulation protocol and the output feedback regulation protocol are delicately constructed such that all the states of followers can converge to the leader state globally. In this paper, a model transformation is firstly performed and the leader–follower regulation problem can be transformed into a general regulation problem. Then, by introducing an appropriate state transformation, the regulation problem can be changed into a parameter determined problem. It is proved that the parameter can be determined by both the properties of M-matrices and the estimates of nonlinear terms. Finally, a numerical example is presented to show the feasibility of designed protocols.

Feedback stabilization for cross triangular nonlinear systems

This paper investigates the global asymptotic stabilization for a class of cross triangular nonlinear systems in which the nonlinearities are assumed to satisfy a linear growth condition on cross systems. The system includes both upper and lower triangular system and hence it can be seen as a generalization of triangle systems. With the help of Lyapunonv stability theorem, and the state transformation of nonlinear systems, we construct a control strategy, in which two static parameters are introduced to achieve our control purposes. A numerical example is given to illustrate the effectiveness of the proposed strategy.

Adaptive state estimation for a class of system with nonlinear parametrization

This work presents the problem of state estimation with the adaptive time-varying observer, for a class of nonlinear parameter systems in a linearly growing triangular form. The parameter appears in functions involving the unmeasured state and the bound of it is not known a priori. By extending the technology of constructing the high-gain observer, we explicitly construct an adaptive time-varying observer which converges to the system. We will present a pendulum system to show the feasibility of this technology.

Full speed range control of IPMSM for electric vehicles based on Hamiltonian theory

The full speed range stabilizing control of interior permanent magnet synchronous motor(IPMSM)for electric vehicles based on Port-Controlled Hamiltonian(PCH)theory is proposed in this paper. First, the Hamiltonian stabilizing control of IPMSM is realized using energy-shaping method of interconnection and damping assignment. Then the desired equilibrium is acquired by maximum torque per current(MTPA) and field-weakening control. Finally, the load torque observer is designed and the controller design is developed when the load torque is unknown. The simulation results show that the proposed method has good speed tracking performance and anti-disturbance ability in the full speed range, and the theory contributes to the improvement of control performance in the electric vehicle.

Robust H ∞ control for uncertain stochastic system with spectral restraint

A design procedure for state feedback controllers is derived for uncertain stochastic systems. The controller ensures that the closed loop system is asymptotical mean square stable with stabilizing degree α, and H_∞ bound is less than some given scalars. Based on linear matrix inequalities (LMI) method and convex optimization algorithm, this paper gets one sufficient condition, which guarantees the closed-loop is with stabilizing degree α and the H_∞ bound is least. Furthermore, the robust H_∞ optimization controller design and the least H_∞ bound are presented via solving one convex optimization problem with LMI constraints.