This paper deals with the control system development for a hybrid energy storage system, consisting of a battery and a supercapacitor, for a through-the-road-parallel hybrid electric vehicle. One of the main advantages deriving from the coupling of a battery and a supercapacitor is the possibility of reducing battery ageing, in addition to energy efficiency improvements when the system operates in critical climate conditions. At the moment, no specific controller has been proposed with the aim of directly reducing battery wear. This paper presents a novel model predictive controller and a dynamic programming algorithm including a simplified battery ageing model in their formulations. The simulation results of the model predictive controller and dynamic programming algorithm are compared with the results deriving from a rule-based strategy. The rule-based controller achieves a 67% reduction of the root mean square values of battery current along a selection of driving cycles in comparison with the same vehicle equipped with battery only. In the same conditions the battery peak current is reduced by 38%. The model predictive controller and the dynamic programming algorithm further reduce the root mean square value by 6% and 10% respectively, whilst the peak values are additionally decreased by 17% and 45%.
Power split strategies for hybrid energy storage systems for vehicular applications / Santucci, A.; Sorniotti, A.; Lekakou, C.. - In: JOURNAL OF POWER SOURCES. - ISSN 0378-7753. - 258:(2014), pp. 395-407. [10.1016/j.jpowsour.2014.01.118]
Power split strategies for hybrid energy storage systems for vehicular applications
Sorniotti A.;
2014
Abstract
This paper deals with the control system development for a hybrid energy storage system, consisting of a battery and a supercapacitor, for a through-the-road-parallel hybrid electric vehicle. One of the main advantages deriving from the coupling of a battery and a supercapacitor is the possibility of reducing battery ageing, in addition to energy efficiency improvements when the system operates in critical climate conditions. At the moment, no specific controller has been proposed with the aim of directly reducing battery wear. This paper presents a novel model predictive controller and a dynamic programming algorithm including a simplified battery ageing model in their formulations. The simulation results of the model predictive controller and dynamic programming algorithm are compared with the results deriving from a rule-based strategy. The rule-based controller achieves a 67% reduction of the root mean square values of battery current along a selection of driving cycles in comparison with the same vehicle equipped with battery only. In the same conditions the battery peak current is reduced by 38%. The model predictive controller and the dynamic programming algorithm further reduce the root mean square value by 6% and 10% respectively, whilst the peak values are additionally decreased by 17% and 45%.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2990784