2019j - Revision history

Scipediacontent at 13:59, 5 March 2021

2021-03-05T13:59:17Z

Scipediacontent: Scipediacontent moved page Draft Content 482559284 to 2019j

2021-01-28T20:08:02Z

Scipediacontent moved page Draft Content 482559284 to 2019j

Scipediacontent: Created page with "== Abstract == This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajecto..."

2021-01-28T20:07:56Z

Created page with "== Abstract == This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajecto..."

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== Abstract ==

This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4%<inline-formula></inline-formula> energy compared to MLC at same average velocity, and achieves a 2.6%<inline-formula><math display="inline"><semantics><mrow><mo></mo> </mrow> </semantics> </math> </inline-formula> higher average velocity than MLC at the same energy consumption.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_482559284-beopen1140-6435-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.3390/wevj10030052 http://dx.doi.org/10.3390/wevj10030052] under the license https://creativecommons.org/licenses/by

* [http://dx.doi.org/10.3390/wevj10030052 http://dx.doi.org/10.3390/wevj10030052] under the license http://creativecommons.org/licenses/by/3.0/

* [http://dx.doi.org/10.5445/ir/1000098758 http://dx.doi.org/10.5445/ir/1000098758] under the license https://creativecommons.org/licenses/by/4.0

* [https://www.mdpi.com/2032-6653/10/3/52/pdf https://www.mdpi.com/2032-6653/10/3/52/pdf] under the license https://creativecommons.org/licenses/by/4.0/deed.de

* [https://publikationen.bibliothek.kit.edu/1000098758 https://publikationen.bibliothek.kit.edu/1000098758],
: [https://publikationen.bibliothek.kit.edu/1000098758/43042956 https://publikationen.bibliothek.kit.edu/1000098758/43042956],
: [https://doi.org/10.5445/IR/1000098758 https://doi.org/10.5445/IR/1000098758] under the license cc-by

* [https://www.mdpi.com/2032-6653/10/3/52 https://www.mdpi.com/2032-6653/10/3/52],
: [https://doaj.org/toc/2032-6653 https://doaj.org/toc/2032-6653]

* [https://www.mdpi.com/2032-6653/10/3/52/pdf https://www.mdpi.com/2032-6653/10/3/52/pdf],
: [http://dx.doi.org/10.3390/wevj10030052 http://dx.doi.org/10.3390/wevj10030052]

* [https://www.mdpi.com/2032-6653/10/3/52/pdf https://www.mdpi.com/2032-6653/10/3/52/pdf],
: [https://www.mdpi.com/2032-6653/10/3/52 https://www.mdpi.com/2032-6653/10/3/52],
: [https://academic.microsoft.com/#/detail/2972289227 https://academic.microsoft.com/#/detail/2972289227] under the license https://creativecommons.org/licenses/by/4.0/

* [ ]

DOIS: 10.3390/wevj10030052 10.5445/ir/1000098758

@@ Line 1: / Line 1: @@
 == Abstract ==
-This works presents a driver assistance system for energy-efficient ALC of a BEV. The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to MLC. On the chosen reference route the ALC saves up to 3.4%<inline-formula></inline-formula> energy compared to MLC at same average velocity, and achieves a 2.6%<inline-formula><math display="inline"><semantics><mrow><mo></mo> </mrow> </semantics> </math> </inline-formula> higher average velocity than MLC at the same energy consumption.
+This works presents a driver assistance system for energy-efficient automated longitudinal control (ALC) of a battery electric vehicle (BEV). The ALC calculates a temporal velocity trajectory from map data. The trajectory is represented by a cubic B-spline function and results from an optimization problem with respect to travel time, driving comfort and energy consumption. For the energetic optimization we propose an adaptive model of the required electrical traction power. The simple power train of a BEV allows the formulation of constraints as soft constraints. This leads to an unconstrained optimization problem that can be solved with iterative filter-based data approximation algorithms. The result is a direct trajectory optimization method of which the effort grows linearly with the trajectory length, as opposed to exponentially as with most other direct methods. We evaluate ALC in real test drives with a BEV. We also investigate the energy-saving potential in driving simulations with ALC compared to manual longitudinal control (MLC). On the chosen reference route the ALC saves up to 3.4% energy compared to MLC at same average velocity, and achieves a 2.6% higher average velocity than MLC at the same energy consumption.
 Document type: Article

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