Singular Solutions in a p(x)-Laplace Equation with Multi-sources

Li, Fengjie; Sun, Anqi

doi:10.1007/s10883-022-09602-4

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 1 ] Li et al evaluated the driving style of SV from the data set by logistic regression classifier, and in their work the hidden variables in decision‐making problems are processed based on the principle of maximum likelihood estimation and supervised learning. [ 17 ] However, this kind of method needs deep learning training, which is limited by the data set. Its generalization ability also needs to be improved; otherwise, the actual collected data set can't be learnt to verify the interaction between vehicles.…”

Section: Introductionmentioning

confidence: 99%

TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic

Chen

Cao

Guo

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Planning for autonomous vehicles to merge into high‐density traffic flows within limited mileage is quite challenging. Specifically, the driving trajectory will inevitably have intersections with other vehicles whose driving intentions can't be directly observed. Herein, a two‐stage algorithm framework that is decomposed into the longitudinal and lateral planning processes for online merging planning is proposed. An improved particle filter is used to estimate the driving models of surrounding vehicles for predicting their future driving intentions. Based on Monte Carlo tree search (MCTS), different action spaces are evaluated for longitudinal merging gap selection and lateral interactive merging operation, while heuristic pruning is used to reduce the computation cost. Moreover, the coefficients related to the driving styles are introduced, and their influences on merging performance are analyzed. Finally, the proposed algorithm is implemented in a two‐lane simulation environment. The results show that the proposal has outperformed other baseline methods.

show abstract

Section: Introductionmentioning

confidence: 99%

TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic

Chen

Cao

Guo

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…In any case, multiobjective optimization will be very conducive to the exploration and development of electric vehicles, and has achieved encouraging optimization results. [27,28] This study presents the trade-off optimization problem of economy and ride comfort of MMHEV; the Pareto method was used to design bi-objective trade-off control strategy based on ECMS. To solve the vehicle's driving demand, a numerical model of the HEV was developed.…”

Section: Introductionmentioning

confidence: 99%

Bi‐objective Trade‐Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy–Pareto Algorithm for a Multimode Hybrid Electric Vehicle

Yang,

Lin,

Huang

et al. 2023

Energy Tech

View full text Add to dashboard Cite

Considering the trade‐off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi‐objective trade‐off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade‐off optimization is carried out using the NSGA‐II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware‐in‐the‐loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade‐off between economy and ride comfort for the MMHEV.

show abstract

Singular Solutions in a p(x)-Laplace Equation with Multi-sources

Cited by 2 publications

References 24 publications

TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic

TMPF: A Two‐Stage Merging Planning Framework for Dense Traffic

Bi‐objective Trade‐Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy–Pareto Algorithm for a Multimode Hybrid Electric Vehicle

Contact Info

Product

Resources

About