Configuration analysis of plug-in hybrid systems using global optimization

This paper proposes a design methodology for a power split type plug-in hybrid electric vehicle (PHEV) by considering drivetrain losses. Selecting the input split type PHEV with a single planetary gear as the reference topology, the locations of the engine, motor and generators (MGs), on the speed lever were determined by using the mechanical point considering the system efficiency. Based on the reference topology, feasible candidates were selected by considering the operation conditions of the engine, MG1, and a redundant element. To evaluate the fuel economy of the selected candidates, the loss models of the power electronic system and drivetrain components were obtained from the mathematical governing equation and the experimental results. Based on the component loss model, a comparative analysis was performed using a dynamic programming approach under the presence or absence of the drivetrain losses. It was found that the selection of the operating mode and the operation time of each mode vary since the drivetrain loss affects the system efficiency. In addition, even if the additional modes provide the flexibility of selecting the operating mode that results in a higher system efficiency for the given driving condition, additional drivetrain elements for realizing the modes can deteriorate the fuel economy due to their various losses.

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“…Gear loss: Gear loss was assumed to be 1% of the transmitted torque [23], which is widely used in the automotive industry.…”

Section: Drivetrain Lossmentioning

confidence: 99%

Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses

et al. 2017

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“…When it comes to HEV, PHEV, and EV, the challenges are the system configuration, energy storage capacity, vehicle mass, battery charging time, charging mode, choice of electric motor, etc. [3][4][5][6][7]. In case of FCEV, hydrogen storage is a significant impediment to their widespread adoption.…”

mentioning

confidence: 99%

Virtual Reality Utilization in Electrical Vehicle Development

Nacu¹,

Fodorean²

2023

Modern Development and Challenges in Virtual Reality

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This book chapter offers a new perspective on vehicle testing methods, by using driving simulators, which can be employed starting from early phases where components are designed, up to the real manufacturing stage. Based on these driving simulators, capable to thorough replicate real driving conditions, it can be described as a complex scenario, and the vehicle components can be dynamized with data coming from it. Therefore, with the use of a Virtual Reality (VR) environment, buildings, traffic signs, road types, and vehicles are imitated, and inspired by the real world or the imagination, but conclusive for the tested components. Moreover, for more realistic tests, a human driver can immerse into VR, controlling the virtual vehicle and leading to more reliable results. Many types of simulators are mentioned, with a focus on a specific type that is capable to test the vehicle’s propulsion system and its driver assistance systems. In the end, a case study is exposed where different configurations, software, and hardware, are tested and several results are presented.

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“…In this study, gear torque loss was assumed to be 1% of the transmitted torque [23], which is a widely used general rule in the automotive industry.…”

Section: Gear Lossmentioning

confidence: 99%

“…In addition, fuel economy improvement strategy was proposed using a neural network-based fuel consumption computational model [19]. However, most of these studies only considered the efficiency of the ICE and MGs in the construction of rule-based controls, dynamic programming (DP), and Pontryagin's minimum principle [20][21][22], except for a few works which include the final reduction gear loss as a constant [23].…”

Section: Introductionmentioning

confidence: 99%

Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses

Son

Kim

2016

Energies

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Abstract:A near-optimal rule-based mode control (RBC) strategy was proposed for a target plug-in hybrid electric vehicle (PHEV) taking into account the drivetrain losses. Individual loss models were developed for drivetrain components including the gears, planetary gear (PG), bearings, and oil pump, based on experimental data and mathematical governing equations. Also, a loss model for the power electronic system was constructed, including loss from the motor-generator while rotating in the unloaded state. To evaluate the effect of the drivetrain losses on the operating mode control strategy, backward simulations were performed using dynamic programming (DP). DP selects the operating mode, which provides the highest efficiency for given driving conditions. It was found that the operating mode selection changes when drivetrain losses are included, depending on driving conditions. An operating mode schedule was developed with respect to the wheel power and vehicle speed, and based on the operating mode schedule, a RBC was obtained, which can be implemented in an on-line application. To evaluate the performance of the RBC, a forward simulator was constructed for the target PHEV. The simulation results show near-optimal performance of the RBC compared with dynamic-programming-based mode control in terms of the mode operation time and fuel economy. The RBC developed with drivetrain losses taken into account showed a 4%-5% improvement of the fuel economy over a similar RBC, which neglected the drivetrain losses.

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Configuration analysis of plug-in hybrid systems using global optimization

Cited by 6 publications

References 11 publications

Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses

Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses

Virtual Reality Utilization in Electrical Vehicle Development

Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses

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