國立虎尾科技大學 |

Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle./
作者:	Sockeel, Nicolas.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
標題:	Automotive engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355921731

Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle.
Sockeel, Nicolas.

Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle. - 1 online resource (111 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--Mississippi State University, 2018.

Includes bibliographical references

Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flow inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside an MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC search algorithms. In fact, the literature does not deal with the impact of the battery model on MPC. This is why this Ph.D. dissertation evaluates the impact of different battery models of a plug-in hybrid electric vehicle (PHEV) through a sensitivity analysis to reach optimal performance for an MPC. The required fidelity of the battery might depend on different factors:

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355921731Subjects--Topical Terms:

1104081
Automotive engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle.
LDR:03827ntm a2200409Ki 4500 001 917877
005 20181022132249.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355921731
035 $a (MiAaPQ)AAI10788155
035 $a (MiAaPQ)msstate:13273
035 $a AAI10788155
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Sockeel, Nicolas. $3 1192065
245 1 0 $a Sensitivity Analysis of the Battery Model for Model Predictive Control Implementable Into a Plug-in Hybrid Electric Vehicle.
264 0 $c 2018
300 $a 1 online resource (111 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Advisers: James E. Fowler; Masood Shahverdi.
502 $a Thesis (Ph.D.)--Mississippi State University, 2018.
504 $a Includes bibliographical references
520 $a Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flow inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside an MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC search algorithms. In fact, the literature does not deal with the impact of the battery model on MPC. This is why this Ph.D. dissertation evaluates the impact of different battery models of a plug-in hybrid electric vehicle (PHEV) through a sensitivity analysis to reach optimal performance for an MPC. The required fidelity of the battery might depend on different factors:
520 $a • the prediction horizon also called look-ahead step time.
520 $a • the vehicle states update time.
520 $a • the vehicle model step time.
520 $a • the objective function.
520 $a The results of simulations show that higher fidelity model improves the capability to predict accurately the battery aging. As the battery pack is currently one of the most expensive components of an electric vehicle and lithium is a limited natural resource, being able to manage precisely the battery aging is a crucial point for both the automotive company and the battery manufacturer. Another important aspect highlighted by this PhD dissertation is that higher battery fidelity model reduces the possibility to violate the SoC constraint, which is greatly desirable. In fact, this constraint is usually defined by battery manufacturers for safety and battery aging management reasons. Last but not least, it has been proven that the impact of the battery modeling for the MPC controller depends on what the objective function aims to optimize. For instance, battery modeling have limited impact if the objective function takes into account the fuel consumption but far more for if it considers the battery degradation.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Automotive engineering. $3 1104081
650 4 $a Electrical engineering. $3 596380
650 4 $a Energy. $3 784773
655 7 $a Electronic books. $2 local $3 554714
690 $a 0540
690 $a 0544
690 $a 0791
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Mississippi State University. $b Electrical and Computer Engineering. $3 1148511
773 0 $t Dissertation Abstracts International $g 79-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10788155 $z click for full text (PQDT)