A Method for Battery Sizing in Parallel P4 Mild Hybrid Electric Vehicles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luca Castellazzi ◽  
Sanjarbek Ruzimov ◽  
Angelo Bonfitto ◽  
Andrea Tonoli ◽  
Nicola Amati
2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110360
Author(s):  
Yiqun Liu ◽  
Y Gene Liao ◽  
Ming-Chia Lai

This paper intends to provide design selections of hybrid powertrain architectures in 48 V mild hybrid electric vehicles. Based on the location of the electric machine in the driveline, the hybrid powertrain architectures can be categorized into five groups, P0, P1, P2, P3, and P4. This paper uses simulation software to investigate the fuel economy improvements and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures. A baseline conventional and a 12 V start/stop vehicle models based on the production vehicle are built for comparison. The 48 V battery pack model is based on experimental data including open-circuit voltage and internal resistance of a 20 Ah lithium polymer battery cell. Four standard driving cycles are used to assess the fuel economy and emissions of the vehicle models. With features of engine idle elimination, electric power assist, and regenerative braking, the 48 V P0 and P1 respectively gains average 13.5% and 15.5% simulated fuel economy compared to baseline vehicle. The 48 V P2 enables feature of electric launch/driving and improves the fuel economy by average 18.5% better than baseline vehicle. The 48 V mild hybrid system seems to be one of the promising techniques to meet future fuel economy standards and emission regulations.


2013 ◽  
Vol 333-335 ◽  
pp. 2072-2075
Author(s):  
Jian Fei Shi ◽  
Bo Jun Zhang ◽  
Yu Wang

Analysis the super-mild hybrid electric vehicle and its transmission system, the transmission system model of low-gear is established through bond graph. Establish vehicle control simulation model, development of low-gear control strategy to simulation. The simulation results show that the fuel economy and emission performance are improved.


2021 ◽  
Author(s):  
Shailesh Hegde ◽  
Angelo Bonfitto ◽  
Hadi Rahmeh ◽  
Nicola Amati ◽  
Andrea Tonoli

Abstract The increasing stringent emissions regulation over the years have shifted the focus of automotive industry towards more efficient fuel economy solutions. One such solution is Hybrid electric architecture, which is able to improve the fuel economy and consequently cutting down emissions. A well known control strategy to solve optimization problem for energy management of Hybrid electric vehicles is ECMS (Equivalent Consumption Minimization Strategy). Finding the best control parameters (equivalence factors) of this strategy may become quite involved. This paper proposes a method for the selection of the optimal equivalence factors, for charging and discharging, by applying genetic algorithm in the case of a P0 mild hybrid electric vehicle. This method is a systematic and deterministic way to guarantee an optimal solution with respect to the trial and error method. The proposed ECMS is compared to a technique available in literature, known as the shooting method, which relies only on one equivalence factor for discharging. It is demonstrated that the performance in terms of pollutant emissions are comparable. However, ECMS with GA always guarantees an optimal solution even in the case of heavy accessory load, when shooting method is not valid anymore, as it does not guarantee a charge sustaining condition.


2021 ◽  
Vol 244 ◽  
pp. 114515
Author(s):  
Heechang Oh ◽  
Jonghyeok Lee ◽  
Soohyung Woo ◽  
Hanyong Park

2019 ◽  
Vol 2019 (17) ◽  
pp. 4590-4594 ◽  
Author(s):  
Dave Winterborne ◽  
Muez Shiref ◽  
Stuart Snow ◽  
Volker Pickert

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