Naxin Cui

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.

Research on predictive control based energy management strategy for Hybrid Electric Vehicle

Online receding horizon controller based on the principle of predictive control for parallel Hybrid Electric Vehicle is proposed in this paper. First of all, the structure of Hybrid Electric Vehicles is introduced, and the model of batteries and engine, etc is established. Secondly the energy management strategies based on predictive control algorithm is presented. At last the results of simulation experiments are analyzed compared with the rule-based control strategy in different conditions.

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.

Dissipative Hamiltonian realisation and robust H∞ control of induction motor considering iron losses for electric vehicles

The dissipative Hamiltonian realisation and robust H∞ control of induction motor considering iron losses for electric vehicle are investigated in this paper. First, the dissipative Hamiltonian of the electric vehicle drive system is obtained based on the system’s mathematical model in a synchronously rotating frame. Then, a robust co-ordinated tracking controller is designed based on the dissipative Hamiltonian form. One part of the controller is designed by using the method of interconnection and damping assignment to ensure the system’s stability, and another part is designed by using the Hamiltonian system’s robust H∞ technique to attenuate external disturbances. The simulation results show that the controller proposed in the paper works very well in robust tracking of induction motor.

Control strategy for bi-directional DC/DC converter of a stand-alone wind power system

A novel topology with a bi-directional DC/DC for stand-alone wind power system is adopted, which can maximize the battery life by monitoring and controlling its state of charge(soc) and charge/discharge process. According to the atmospheric conditions, the state of charge of the battery and the load conditions, six possible operation modes of the bi-directional DC/DC are also expounded. This paper proposes a hybrid control strategy with the combination of the PI control and the direct output current control to avoid the voltage surge of the DC bus caused by the mode switching. A 5kW wind power system test bench is developed to verify the effectiveness of the control strategy. The experimental results show that the voltage surge can be limited within 5% and the controller performs well in various wind speed and load conditions.

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

The influence of parameters variation on efficiency optimization control for electric vehicle drive motor

The parameters variation of electric vehicle induction motor drive system will directly affect the implementation effect of efficiency optimization control strategy. An efficiency optimization strategy based on loss model is given at first, the relationships between the algorithm parameter-dependent and the motor speed are discussed based on the parameter sensitivity analysis then, and the influence of iron loss equivalent resistance variation and magnetic saturation nonlinearity on efficiency optimization control are analyzed in details. The experiment system is built with DSP TMS320LF2407A, the experimental results verify the validity of the analysis. It provides a sound practical guidance for efficiency optimization control implementation of electric vehicle propulsion system.