Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombinati...Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock.Therefore,a smooth transient process,among other concerns,typically associated with this category of vehicles,is of great importance.The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore im-prove ride comfort.To this end,a dynamic model of the objective power-split HEV is first built.To resolve the contention between vehicle jerk and clutch friction loss,a model predictive control(MPC)combined with control allocation(CA)is then designed for the clutch-engaged phase.To reduce the torque fluctuation caused by the inertia torques of multiple power sources,a dynamic compensation control strategy(DCcs)that coordinates motorgenerator torque to compensate for the transition torque is proposed for the brake-disengaged phase.Finally,the proposed control strategy is validated by simulation and bench test,and results show great potential in reducing shift duration,torque variation,vehicle jerk and friction loss(the simulation results show decreases of 22%,39%,83%and 53%,and the experimental results show decreases of 21%,74%,77%,and 59%,re-spectively),thereby improving shift quality.展开更多
Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviat...Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.展开更多
The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open ...The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.52005039,51575043,51975048,U1764257).
文摘Mode shift is a special mechanism for a power-split hybrid electric vehicle(HEV)to realise electrically variable transmission,but the sudden change of equivalent inertia caused by topological configuration recombination during mode shift induces a significant torque shock.Therefore,a smooth transient process,among other concerns,typically associated with this category of vehicles,is of great importance.The present research aims to introduce a novel control strategy to manage the dynamic torque of multiple power sources and therefore im-prove ride comfort.To this end,a dynamic model of the objective power-split HEV is first built.To resolve the contention between vehicle jerk and clutch friction loss,a model predictive control(MPC)combined with control allocation(CA)is then designed for the clutch-engaged phase.To reduce the torque fluctuation caused by the inertia torques of multiple power sources,a dynamic compensation control strategy(DCcs)that coordinates motorgenerator torque to compensate for the transition torque is proposed for the brake-disengaged phase.Finally,the proposed control strategy is validated by simulation and bench test,and results show great potential in reducing shift duration,torque variation,vehicle jerk and friction loss(the simulation results show decreases of 22%,39%,83%and 53%,and the experimental results show decreases of 21%,74%,77%,and 59%,re-spectively),thereby improving shift quality.
基金supported by National Natural Science Foundation of China(Grant No.51005017)
文摘Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.
基金supported by the National Natural Science Foundation of China (62173303)the Fundamental Research for the Zhejiang P rovincial Universities (RF-C2020003)。
文摘The utilization of traffic information received from intelligent vehicle highway systems(IVHS) to plan velocity and split output power for multi-source vehicles is currently a research hotspot. However, it is an open issue to plan vehicle velocity and distribute output power between different supply units simultaneously due to the strongly coupling characteristic of the velocity planning and the power distribution. To address this issue, a flexible predictive power-split control strategy based on IVHS is proposed for electric vehicles(EVs) equipped with battery-supercapacitor system(BSS). Unlike hierarchical strategies to plan vehicle velocity and distribute output power separately, a monolayer model predictive control(MPC) method is employed to optimize them online at the same time. Firstly, a flexible velocity planning strategy is designed based on the signal phase and time(SPAT) information received from IVHS and then the Pontryagin’s minimum principle(PMP) is adopted to formulate the optimal control problem of the BSS. Then, the flexible velocity planning strategy and the optimal control problem of BSS are embedded into an MPC framework, which is online solved using the shooting method in a fashion of receding horizon. Simulation results verify that the proposed strategy achieves a superior performance compared with the hierarchical strategy in terms of transportation efficiency, battery capacity loss, energy consumption and computation time.
