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Combined design and control optimization of hybrid vehicles

Nikolce Murgovski (Institutionen för signaler och system, Mekatronik) ; Xiaosong Hu (Institutionen för signaler och system, Reglerteknik) ; Lars Johannesson (Institutionen för signaler och system, Reglerteknik) ; Bo Egardt (Institutionen för signaler och system, Reglerteknik)
Handbook of Clean Energy Systems p. 1-14. (2015)
[Kapitel, populärvetenskapligt]

Hybrid vehicles play an important role in reducing energy consumption and pollutant emissions of ground transportation. The increased mechatronic system complexity, however, results in a heavy challenge for efficient component sizing and power coordination among multiple power sources. This chapter presents a convex programming framework for the combined design and control optimization of hybrid vehicles. An instructive and straightforward case study of design and energy control optimization for a fuel cell/supercapacitor hybrid bus is delineated to demonstrate the effectiveness and the computational advantage of the convex programming methodology. Convex modeling of key components in the fuel cell/supercapactior hybrid powertrain is introduced, while a pseudo code in CVX is also provided to elucidate how to practically implement the convex optimization. The generalization, applicability, and validity of the convex optimization framework are also discussed for various powertrain configurations (i.e., series, parallel, and series-parallel), different energy storage systems (e.g., battery, supercapacitor, and dual buffer), and advanced vehicular design and controller synthesis accounting for the battery thermal and aging conditions. The proposed methodology is an efficient tool that is valuable for researchers and engineers in the area of hybrid vehicles to address realistic optimal control problems.

Nyckelord: hybrid electric vehicle;component sizing;energy management;convex optimization;optimal control;fuel cell;energy storage system



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Denna post skapades 2014-03-21. Senast ändrad 2016-07-28.
CPL Pubid: 195407