Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

Öz

This work proposes a predictive equivalent consumption minimization (P-ECMS) strategy for a power-split hybrid electric vehicle (HEV) using predicted driving cycle speed based on Monte Carlo (MC) algorithm. The proposed P-ECMS fully takes advantage of the predicted speed profiles by the MC algorithm to optimally determine the power split among energy sources. In this study, to validate the workings of the MC-based P-ECMS scheme, a series of simulations under a total of seven replicated driving cycles including New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92), and a combination of all (ALL-CYC) are conducted. The MC-based P-ECMS strategy is compared with a baseline ECMS in terms of fuel-saving, and fuel economy saving up to 6.01% under NEDC, 9.09% under WLTP, 6.33% under UDDS, 5.14% under HWFET, 1.96% under NYCC, 11.47% under LA-92, and 7.92% under ALL-CYC are achieved. The results in this article put forward that the proposed strategy delivers competitive fuel savings compared to the widely used baseline method.

Anahtar Kelimeler

• Hybrid electric vehicles
• equivalent consumption minimization
• predictive control
• Monte Carlo algorithm
• speed prediction

Güç paylaşımlı hibrit elektrikli araçlar için Monte Carlo algoritması kullanarak öngörülü eşdeğer tüketim minimizasyon stratejisi

Öz

Bu çalışma, güç paylaşımlı bir hibrit elektrikli araç (HEA) için, Monte Carlo (MC) algoritmasına dayalı olarak tahmin edilen sürüş çevrimi hızlarını kullanan öngörülü eşdeğer tüketim minimizasyonu stratejisi (Ö-ETMS) önermektedir. Önerilen Ö-ETMS, enerji kaynakları arasındaki güç dağılımını en iyi şekilde belirlemek için MC algoritması tarafından tahmin edilen hız profillerinden tam olarak yararlanmaktadır. Bu çalışmada; MC tabanlı Ö-ETMS metodunu doğrulamak için, New European Driving Cycle (NEDC), Worldwide Harmonised Light Vehicles Test Procedure (WLTP), Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test (HWFET), New York City Cycle (NYCC), California Unified Cycle (LA-92) ve tüm döngülerin kombinasyonu (ALL-CYC) çevrimleri kullanılmış; toplam yedi tekrarlı sürüş döngüsü altında bir dizi simülasyon çalışması gerçekleştirilmiştir. MC tabanlı Ö-ETMS stratejisi, standart ETMS ile karşılaştırılmıştır. NEDC çevriminde %6,01, WLTP çevriminde %9,09, UDDS çevriminde %6,33, HWFET çevriminde %5,14, NYCC çevriminde %1,96, LA-92 çevriminde %11,47 ve ALL-CYC çevriminde %7,92 oranla yakıt tasarrufu elde edilmiştir. Bu makaledeki sonuçlar, önerilen stratejinin yaygın olarak kullanılan temel yönteme kıyasla, rekabetçi bir yakıt tasarrufu sağladığını ortaya koymaktadır.

Anahtar Kelimeler

• Hibrit elektrikli araçlar
• eşdeğer tüketim minimizasyonu stratejisi
• öngörülü kontrol
• Monte Carlo algoritması
• hız tahmini

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