Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control

Energy Management of the Power-Split Hybrid Electric City Bus Based on the Stochastic Model Predictive Control

“…In the analysis of experimental data in the form of   (32) The algebraic function ( ) Q x in Eq. (31) can be replaced by a polynomial function ( , ) f x y .…”

“…In this regard, the most typical control strategy of the instantaneous optimization method is ECMS, which is based on the instantaneous motor power consumption equivalent to the instantaneous fuel consumption to obtain the current equivalent fuel consumption. In this method, the optimal control sequence can be achieved by calculating the minimum value of equivalent fuel consumption [32]. Since the corresponding optimal equivalent factors are different when the driving cycle and vehicle state change, it is essential to change the equivalent factor dynamically with respect to the variation of the driving cycle and the vehicle state.…”

A Novel Learning-Based Model Predictive Control Strategy for Plug-In Hybrid Electric Vehicle

“…In the comparison study, the selected serial PHEV, parallel PHEV, and 4WD PHEV are simulated and controlled by the designed algorithm. The detailed description on the configurations and component parameters can be found in our previous research [44][45][46]. In Table VIII, average EFC denotes the average equivalent fuel consumption of simulation on NEDC, UDDS, US06, HWFER, and JC 08 driving cycles.…”

“…OWADAYS, plug-in hybrid electric vehicle (PHEVs) have boosted to be top-ranking solutions in auto industry to promote energy consumption economy and mitigate global warming concern [1,2]. The tailored powertrain configurations endow PHEVs with extraordinary capacities to leisurely balance driving mileage, drivability, and energy reservation [3].…”

A hybrid optimization based energy management between electric vehicle and electricity distribution system

“…After a novel plants development in consecutive generations, the plants in more fertile areas are denser. 68 Furthermore, the GBDT is repetitive decision tree algorithm contains multiple decision trees and the ends of the entire trees are gathered for generating the final outcome. It is good classifier than the simple boosting or bagging because it provides the use of an arbitrary cost function.…”