Engine Torque Observer Based Real-time Optimization Control Strategy of Parallel Hybrid Electric Bus

2007 ◽  
Author(s):  
Huang Kaisheng ◽  
Wang Shuaiyu ◽  
Jin Zhenhua ◽  
Jiang Dinan
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Engine Torque ◽  
Hybrid Electric Bus ◽  
Time Optimization ◽  
Electric Bus ◽  
Optimization Control ◽  
Parallel Hybrid ◽  
Torque Observer ◽  
Real Time Optimization

scholarly journals Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7919
Author(s):  
Penghui Qiang ◽  
Peng Wu ◽  
Tao Pan ◽  
Huaiquan Zang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Hybrid Powertrain ◽  
Computational Load ◽  
Time Optimization ◽  
Equivalence Factor ◽  
Optimization Control ◽  
Parallel Hybrid ◽  
Equivalent Consumption Minimization Strategy ◽  
Real Time Optimization

Real-time energy management strategy (EMS) plays an important role in reducing fuel consumption and maintaining power for the hybrid electric vehicle. However, real-time optimization control is difficult to implement due to the computational load in an instantaneous moment. In this paper, an Approximate equivalent consumption minimization strategy (Approximate-ECMS) is presented for real-time optimization control based on single-shaft parallel hybrid powertrain. The quadratic fitting of the engine fuel consumption rate and the single-axle structure characteristics of the vehicle make the fitness function transformed into a cubic function based on ECMS for solving. The candidate solutions are thus obtained to distribute torque and the optimal distribution is got from the candidate solutions. The results show that the equivalent fuel consumption of Approximate-ECMS was 7.135 L/km by 17.55% improvement compared with Rule-ECMS in the New European Driving Cycle (NEDC). To compensate for the effect of the equivalence factor on fuel consumption, a hybrid dynamic particle swarm optimization-genetic algorithm (DPSO-GA) is used for the optimization of the equivalence factor by 9.9% improvement. The major contribution lies in that the Approximate-ECMS can reduce the computational load for real-time control and prove its effectiveness by comparing different strategies.

scholarly journals Research on the Application of Artificial Intelligence Technology in Power System Intelligent Dispatching Automation

2021 ◽  
Vol 2083 (4) ◽  
pp. 042047
Author(s):  
Hongying Liu
Keyword(s):  
Real Time ◽  
Reactive Power ◽  
Power Grid ◽  
Control Mode ◽  
Particle Swarm Algorithm ◽  
Automation System ◽  
Regional Power ◽  
Time Optimization ◽  
Optimization Control ◽  
Real Time Optimization

Abstract From the perspective of meeting the power quality requirements of users, the article analyses the characteristics of traditional voltage and reactive power control mode and the regional power grid reactive voltage optimization centralized closed-loop control mode (AVC system) based on the dispatch automation system (SCADA/EMS) from the perspective of technical management. Combining the reactive power/voltage real-time optimization control model, a real-time optimization control method of the regional power grid based on the improved differential evolution algorithm is proposed. The particle swarm algorithm is combined with the characteristics of reactive power/voltage control to improve the initial particle quality, reduce the optimization space, and introduce a crossover operator to improve the calculation speed and efficiency of the algorithm. Taking an actual regional power grid as an example, the simulation calculation of reactive power/voltage real-time optimization is carried out. The results show that the proposed algorithm and control strategy are feasible and effective.

Experimental Evaluation of a Control Strategy for Real-Time Optimization of Low Temperature PEM Fuel Cell Stack

2009 ◽  
Author(s):  
Panini Kolavennu ◽  
Susanta K. Das ◽  
K. Joel Berry
Keyword(s):  
Fuel Cell ◽  
Real Time ◽  
Control Strategy ◽  
Pem Fuel Cell ◽  
Effective Control ◽  
Feed Forward ◽  
Fuel Cell Stack ◽  
Feed Forward Control ◽  
Time Optimization ◽  
Real Time Optimization

A robust control strategy which ensures optimum performance is crucial to proton exchange membrane (PEM) fuel cell development. In a PEM fuel cell stack, the primary control variables are the reactant’s stochiometric ratio, membrane’s relative humidity and operating pressure of the anode and cathode. In this study, a 5 kW (25-cell) PEM fuel cell stack is experimentally evaluated under various operating conditions. Using the extensive experimental data of voltage-current characteristics, a feed forward control strategy based on a 3D surface map of cathode pressure, current density and membrane humidity at different operating voltages is developed. The effectiveness of the feed forward control strategy is tested on the Green-light testing facility. To reduce the dependence on predetermined system parameters, real-time optimization based on extremum seeking algorithm is proposed to control the air flow rate into the cathode of the PEM fuel cell stack. The quantitative results obtained from the experiments show good potential towards achieving effective control of PEM fuel cell stack.

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