2024
DOI: 10.1109/tvt.2023.3333250
|View full text |Cite
|
Sign up to set email alerts
|

Learning-Based Model Predictive Control for the Energy Management of Hybrid Electric Vehicles Including Driving Mode Decisions

David Theodor Machacek,
Stijn van Dooren,
Thomas Huber
et al.
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1

Relationship

1
0

Authors

Journals

citations
Cited by 1 publication
(4 citation statements)
references
References 34 publications
0
4
0
Order By: Relevance
“…It is used to ensure that the optimization problem remains feasible, even if an unrealistically high power request is predicted. A detailed explanation is provided in [20]. Third, there is the scalar term κ NO x • ϵ NO x , which, together with Equation (27g,j), represents a soft constraint on accumulated NO eo x .…”
Section: Cost Function and Dynamicsmentioning
confidence: 99%
See 3 more Smart Citations
“…It is used to ensure that the optimization problem remains feasible, even if an unrealistically high power request is predicted. A detailed explanation is provided in [20]. Third, there is the scalar term κ NO x • ϵ NO x , which, together with Equation (27g,j), represents a soft constraint on accumulated NO eo x .…”
Section: Cost Function and Dynamicsmentioning
confidence: 99%
“…In this section, the proposed control architecture to calculate the vehicle control inputs is presented. The overall controller architecture is presented in Figure 11 and is based on the work presented in [20]. The controller consists of three controller levels: the lower-level controller; the so-called MPC; and the reference trajectory generator, which is from here on referred to as the RTG.…”
Section: Controller Structurementioning
confidence: 99%
See 2 more Smart Citations