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Energy-Efficient Fleet of Electrifie...

Anwar, Hamza.

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Energy-Efficient Fleet of Electrified Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Energy-Efficient Fleet of Electrified Vehicles./
作者:	Anwar, Hamza.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	210 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Transportation. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30782358
ISBN:	9798380589246

Energy-Efficient Fleet of Electrified Vehicles.
Anwar, Hamza.

Energy-Efficient Fleet of Electrified Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 210 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2023.

This item must not be sold to any third party vendors.

This dissertation addresses energy-efficient operations for a fleet of diverse electrified vehicles at two system levels, the single-vehicle powertrain system, and the multi-vehicle transportation system, contributing to both with optimal control- and heuristic-based integrative approaches.At the single vehicle powertrain level, an electrified powertrain exhibits a continuum of complexities: mechanical, thermal, and electrical systems with nonlinear, switched, multi-timescale dynamics; algebraic and combinatorial path constraints relating a mix of integer- and real-valued variables. For optimal energy management of such powertrains, "PS3" is proposed, which is a three-step numerical optimization algorithm based on pseudo-spectral collocation theory. Its feasibility, convergence, and optimality properties are presented. Simulation experiments using PS3 on increasingly complex problems are benchmarked with Dynamic Programming (DP). As problem size increases, PS3's computation time does not scale up exponentially like that of DP. Thereafter, PS3 is applied to a comprehensive 13-state 4-control energy management problem. It saves up to 6% energy demand, 2% fuel consumption, and 18% NOx emissions compared to coarsely-modeled DP baseline. For generalizability, parallel and series electrified powertrain architectures running various urban delivery truck drive cycles are considered with multi-objective cost functions, Pareto-optimal study, energy flow analyses, and warm versus cold aftertreatment-start transients.At the multi-vehicle fleet level, energy-efficient vehicle routing approaches lack in integrating optimal powertrain energy management solutions. Extending singlevehicle PS3 algorithm for a multi-vehicle fleet of plug-in hybrid (PHEV), battery electric (BEV), and conventional engine (ICEV) vehicles, an integrative optimization framework to solve green vehicle routing with pickups and deliveries (PDP) is proposed. It minimizes the fleet energy consumption and total cost of ownership (TCO) by (i) calculating energy-efficient routes (eco-routing), (ii) solving parallel optimal control problems using PS3 for realistic speed profiling (eco-driving), and (iii) running hybrid Simulated Annealing algorithm for sequencing pickup and delivery calls with BEV charging station visits, and cargo, battery capacity, and travel time constraints. Columbus region road network data having traffic lights, stop signs, road grade, speed limits, and locations of available charging stations is utilized in this framework. Presented eco-driving results save up to 12% energy with 4 extra minutes of driving on a 40-minute 16.4-mile city driving route. The TCO objective function for the three vehicle types includes vehicle purchase cost (accounting for its depreciation), energy consumption cost, and maintenance cost over a five-year operation period. Simulation results compare the solutions when minimizing the fleet's (a) TCO, versus its (b) energy consumption. With (a), the solver tends to choose ICEVs and PHEVs over BEVs, as opposed to (b), where it prefers BEVs and PHEVs over ICEVs.

ISBN: 9798380589246Subjects--Topical Terms:

555912
Transportation.
Subjects--Index Terms:

Electrified powertrain

Energy-Efficient Fleet of Electrified Vehicles.
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520 $a This dissertation addresses energy-efficient operations for a fleet of diverse electrified vehicles at two system levels, the single-vehicle powertrain system, and the multi-vehicle transportation system, contributing to both with optimal control- and heuristic-based integrative approaches.At the single vehicle powertrain level, an electrified powertrain exhibits a continuum of complexities: mechanical, thermal, and electrical systems with nonlinear, switched, multi-timescale dynamics; algebraic and combinatorial path constraints relating a mix of integer- and real-valued variables. For optimal energy management of such powertrains, "PS3" is proposed, which is a three-step numerical optimization algorithm based on pseudo-spectral collocation theory. Its feasibility, convergence, and optimality properties are presented. Simulation experiments using PS3 on increasingly complex problems are benchmarked with Dynamic Programming (DP). As problem size increases, PS3's computation time does not scale up exponentially like that of DP. Thereafter, PS3 is applied to a comprehensive 13-state 4-control energy management problem. It saves up to 6% energy demand, 2% fuel consumption, and 18% NOx emissions compared to coarsely-modeled DP baseline. For generalizability, parallel and series electrified powertrain architectures running various urban delivery truck drive cycles are considered with multi-objective cost functions, Pareto-optimal study, energy flow analyses, and warm versus cold aftertreatment-start transients.At the multi-vehicle fleet level, energy-efficient vehicle routing approaches lack in integrating optimal powertrain energy management solutions. Extending singlevehicle PS3 algorithm for a multi-vehicle fleet of plug-in hybrid (PHEV), battery electric (BEV), and conventional engine (ICEV) vehicles, an integrative optimization framework to solve green vehicle routing with pickups and deliveries (PDP) is proposed. It minimizes the fleet energy consumption and total cost of ownership (TCO) by (i) calculating energy-efficient routes (eco-routing), (ii) solving parallel optimal control problems using PS3 for realistic speed profiling (eco-driving), and (iii) running hybrid Simulated Annealing algorithm for sequencing pickup and delivery calls with BEV charging station visits, and cargo, battery capacity, and travel time constraints. Columbus region road network data having traffic lights, stop signs, road grade, speed limits, and locations of available charging stations is utilized in this framework. Presented eco-driving results save up to 12% energy with 4 extra minutes of driving on a 40-minute 16.4-mile city driving route. The TCO objective function for the three vehicle types includes vehicle purchase cost (accounting for its depreciation), energy consumption cost, and maintenance cost over a five-year operation period. Simulation results compare the solutions when minimizing the fleet's (a) TCO, versus its (b) energy consumption. With (a), the solver tends to choose ICEVs and PHEVs over BEVs, as opposed to (b), where it prefers BEVs and PHEVs over ICEVs.
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653 $a Energy management
653 $a Battery electric
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