On Dynamic Coordinated Control Of Powertrains Of Parallel Hybrid Electric Vehicles

Dynamic coordinated control of the powertrain system of parallel hybrid electric vehicles is one of the key technologies to improve the energy conversion efficiency and ride comfort.The parallel hybrid power system is a multivariable,nonlinear and structurally complex electromechanical coupling system.The dynamic coordinated control quality of multiple power sources seriously affects the driving performance and ride comfort during the powertrain system operation process.Therefore,it is of great significance to study the dynamic coordination control issue of hybrid power systems with driving performance and ride comfort selected as control objectives under complex driving conditions.In view of this,the dynamic coordinated control issue of single-shaft parallel hybrid powertrain based on mechanical automatic transmission(AMT)considering the influence of system uncertainties is thoroughly studied in this paper.The main research content includes the following four aspects:For the dynamic mode transition process of the hybrid system from the pure electric driving mode to engine driving mode,a dynamic mode transition adaptive control strategy is designed to guarantee fast and smooth mode transition process considering the system uncertainties,such as parameter perturbation and external disturbances.The robust adaptive sliding mode controller and particle swarm optimization(PSO)algorithm are utilized to obtain the optimal clutch target torque.Moreover,a fuzzy controller is designed to calculate the clutch target engagement speed,and the actual clutch transfer torque is estimated by a PI observer.The simulation and experiment results show that the proposed control strategy can reduce the vehicle jerk and clutch slipping energy loss,and improve the vehicle ride comfort.In response to the limitations of the previous research on mode transition control,which mainly focused on designing controller based on the operating state of the ego vehicle to improve the transition control quality,the developed vehicle-to-infrastructure(V2I)communication technology is utilized to design mode transition control strategy considering the operating state of the ego vehicle and the current traffic information.The target vehicle speed can be obtained via a model predictive control(MPC)algorithm,the clutch engagement speed intention can be predicted via a type-2 fuzzy neural network(T2FNN),and a fuzzy controller is designed to obtain the clutch target engagement speed.Co-simulation results show that the designed control strategy can realize fast and smooth mode transition process.In the light of a robust control issue of the optimized engagement position curve for clutch tracking during the mode transition process,an adaptive dynamic sliding mode position tracking control strategy based on the extended state observer is proposed to realize the clutch position tracking control.In order to improve further the dynamic and steady state performance of the position tracking control and avoid the chattering issue of the sliding mode control,an adaptive finite-time position tracking control strategy is designed.Simulations and HIL experiments demonstrate that the proposed adaptive finite-time control strategy improves further the clutch position tracking control performance.Focusing on the differences in dynamic tracking response characteristics of the allocated driving torque due to the physical characteristics of the engine and motor,the dynamic coordinated adaptive control strategy of powertrain system for hybrid driving is designed.The fast response characteristics of the motor is utilized to compensate for the deviation of the engine torque response.The adaptive terminal sliding mode speed tracking control strategy and the adaptive dynamic sliding mode torque tracking control strategy are designed to control the engine speed and motor torque,respectively.Simulation results and real vehicle test results show that the designed control strategy can realize efficient electromechanical hybrid driving,and improve the tracking control performance under the system uncertainties.