Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming

Rakesh Patil, Zoran Filipi, Hosam Kadry Fathy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a computationally efficient Multi-Objective Dynamic Programming (MODP) algorithm. The algorithm is applied to obtain the optimal supervisory control for PHEVs to minimize two objectives -total CO2 emissions and operational dollar costs to an individual PHEV owner. The algorithm integrates the concept of crowding distance from the Multi-Objective Evolutionary Algorithms (MOEA) literature. This distance metric is used to refine the optimal Pareto front at every time step for each state discretization. The refinement of the Pareto front significantly reduces the computational time and memory required for MODP, making it feasible. At the same time, the results show that the refinement retains optimality and produces a Pareto front with a good spread ranging from one extremal point to the other. The results also reveal interesting insights for the tradeoffs that can be achieved in minimizing the CO2 emissions and cost objectives for the underlying grid mix and driving conditions assumed.

Original languageEnglish (US)
Title of host publicationASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
Pages839-845
Number of pages7
DOIs
StatePublished - Dec 1 2012
EventASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012 - Fort Lauderdale, FL, United States
Duration: Oct 17 2012Oct 19 2012

Publication series

NameASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
Volume2

Other

OtherASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012
CountryUnited States
CityFort Lauderdale, FL
Period10/17/1210/19/12

Fingerprint

Plug-in hybrid vehicles
Dynamic programming
Costs
Evolutionary algorithms
Data storage equipment

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Cite this

Patil, R., Filipi, Z., & Fathy, H. K. (2012). Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming. In ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012 (pp. 839-845). (ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012; Vol. 2). https://doi.org/10.1115/DSCC2012-MOVIC2012-8652
Patil, Rakesh ; Filipi, Zoran ; Fathy, Hosam Kadry. / Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming. ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012. 2012. pp. 839-845 (ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012).
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abstract = "This paper presents a computationally efficient Multi-Objective Dynamic Programming (MODP) algorithm. The algorithm is applied to obtain the optimal supervisory control for PHEVs to minimize two objectives -total CO2 emissions and operational dollar costs to an individual PHEV owner. The algorithm integrates the concept of crowding distance from the Multi-Objective Evolutionary Algorithms (MOEA) literature. This distance metric is used to refine the optimal Pareto front at every time step for each state discretization. The refinement of the Pareto front significantly reduces the computational time and memory required for MODP, making it feasible. At the same time, the results show that the refinement retains optimality and produces a Pareto front with a good spread ranging from one extremal point to the other. The results also reveal interesting insights for the tradeoffs that can be achieved in minimizing the CO2 emissions and cost objectives for the underlying grid mix and driving conditions assumed.",
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Patil, R, Filipi, Z & Fathy, HK 2012, Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming. in ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012. ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012, vol. 2, pp. 839-845, ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012, Fort Lauderdale, FL, United States, 10/17/12. https://doi.org/10.1115/DSCC2012-MOVIC2012-8652

Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming. / Patil, Rakesh; Filipi, Zoran; Fathy, Hosam Kadry.

ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012. 2012. p. 839-845 (ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Patil R, Filipi Z, Fathy HK. Minimizing Co2 emissions and dollar costs for plug-in hybrid electric vehicles using multi objective dynamic programming. In ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012. 2012. p. 839-845. (ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, DSCC 2012-MOVIC 2012). https://doi.org/10.1115/DSCC2012-MOVIC2012-8652