Study On Control Strategy Of Genetic Algorithm For Hybrid Container Loaders

The hybrid container loader is used in the air cargo transport field. The control strategy affects fuel economy and emissions. The reasonable control strategy makes engine and motor work in high efficient area and storage battery keeps balance of the hybrid container loader under arbitrary load, battery SOC and temperature. This has a important academic significance and application value. The main contents are follows:①The hydraulic system model, the engine numerical model engine, the motor/generator model, the storage battery charge and discharge efficiency model,the battery temperature rise model, and the fuel economy simulation of hybrid loader model are established.②The genetic algorithm optimal control strategy of the hybrid container loader is proposed. The control parameters are charging torque limit, discharging torque limit, motor charging torque, motor discharging torque, the high SOC threshold, engine speed under high load, and engine speed under low load. Then the minimum equivalent fuel consumption of the loader cycle and the equilibrium of the battery SOC are used as the optimization objectives. Then the container loader would be simulated respectively in different loads, the different initial temperature(10℃~ 30℃) of the battery and the initial charge state(0.6 ~ 0.8) of the battery. The control parameters can be obtained by the genetic algorithm optimization.When the battery initial SOC is less than the best range, the genetic algorithm optimal control strategy of the hybrid container loader is proposed. Then the minimum equivalent fuel consumption of the loader cycle and the rise of the battery SOC are used as the optimization objectives. The simulation shows that the battery SOC rise in the best the best range, and storage battery charging and discharging current are 1C, meet the control requirements of battery.③The control strategy based on dynamic programming is formulated. The simulation results show that engine fuel consumption based on the genetic algorithm is very close to global optimization control strategy in different load and battery SOC.④When the control strategy parameters for hybrid loader are optimized by offline genetic algorithm in different load, battery initial SOC and battery temperature. The loader mathematical model is established by the response surface. The simulation results show that the lowest of fuel economy, battery SOC keep balance, and the temperature rise in a reasonable range in arbitrary load, temperature and battery SOC.To verify the effectiveness of the control strategy of hybrid container loader based on the response surface model, the simulation results compare with genetic algorithm control strategy, global optimization control strategy and traditional loader.