scholarly journals A Study on the Fuel Economy Potential of Parallel and Power Split Type Hybrid Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2103 ◽  
Author(s):  
Hyunhwa Kim ◽  
Junbeom Wi ◽  
Jiho Yoo ◽  
Hanho Son ◽  
Chiman Park ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicles ◽  
Improvement Rate ◽  
Power Characteristics ◽  
Power Split ◽  
Variable Transmission ◽  
Hybrid Electric ◽  
Load Leveling ◽  
Parallel Type

What is the best number of gear steps for parallel type hybrid electric vehicles (HEVs) and what are the pros and cons of the power split type HEV compared to the parallel type have been interesting issues in the development of HEVs. In this study, a comparative analysis was performed to evaluate the fuel economy potential of a parallel HEV and a power split type HEV. First, the fuel economy potential of the parallel HEV was investigated for the number of gear steps. Four-speed, six-speed, and eight-speed automatic transmissions (ATs) and a continuously variable transmission (CVT) were selected, and their drivetrain losses were considered in the dynamic programming (DP). It was found from DP results that the power electronics system (PE) loss decreased because the magnitude of the motor load leveling power decreased as the number of gear steps increased. On the other hand, the drivetrain losses including the electric oil pump (EOP) loss increased with increasing gear step. The improvement rate from the 4-speed to the 6-speed was the greatest, while it decreased for the higher gear step. The fuel economy of the CVT HEV was rather low due to the large EOP loss in spite of the reduced PE loss. In addition, the powertrain characteristics of the parallel HEV were compared with the power split type HEV. In the power split type HEV, the PE loss was almost double compared to that of the parallel HEV because two large capacity motor-generators were used. However, the drivetrain loss and EOP loss of the power split type HEV were found to be much smaller due to its relatively simple architecture. It is expected that the power characteristics of the parallel and power split type HEVs obtained from the DP results can be used in the development of HEV systems.

Systematic Configuration Selection Methodology of Power-Split Hybrid Electric Vehicles With a Single Planetary Gear

2014 ◽  
Author(s):  
Minkuk Kang ◽  
Hyunjun Kim ◽  
Dongsuk Kum
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicles ◽  
Planetary Gear ◽  
Full Load ◽  
Light Load ◽  
Design Variables ◽  
Power Split ◽  
Load Analysis ◽  
Hybrid Electric

Nowadays, power-split hybrid electric vehicles (PS-HEV) are very popular mainly thanks to the success of Toyota Prius. Despite their superior performance, the design and control of PS-HEVs are non-trivial due to the large number of design candidates and the complex control problems. For instance, there exist twelve ways to connect the four components (two motor/generators, an engine, and a driving wheel) with a single planetary gear-set (PG), and the number increases to 1152 possible configurations when using two PGs. Moreover, if we consider the final drive (FD) and PG ratios as design variables, finding the best design becomes intractable. In this study, we introduce a simple yet powerful way to find the optimal designs of single PG PS-HEVs. The suggested method consists of two parts — full-load analysis and light-load analysis. The full-load analysis computes 0–100kph times to evaluate acceleration performance of all designs using instantaneous optimization approach. The light-load analysis evaluates the fuel economy of selected designs (designs with acceptable acceleration performance) using equivalent consumption minimization strategy (ECMS). Note that the sun-to-ring (SR) gear ratio and the FD ratio are considered design variables, and thus one can see how fuel economy and acceleration performance of each configuration vary with SR and FD ratios. Based on these analyses, the optimal design that balances full-load and light-load performances can be selected.

scholarly journals A Review of Optimal Energy Management Strategies for Hybrid Electric Vehicle

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Aishwarya Panday ◽  
Hari Om Bansal
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Management Strategies ◽  
Energy Optimization ◽  
Hybrid Vehicles ◽  
Power Split ◽  
Hybrid Electric

Presence of an alternative energy source along with the Internal Combustion Engine (ICE) in Hybrid Electric Vehicles (HEVs) appeals for optimal power split between them for minimum fuel consumption and maximum power utilization. Hence HEVs provide better fuel economy compared to ICE based vehicles/conventional vehicle. Energy management strategies are the algorithms that decide the power split between engine and motor in order to improve the fuel economy and optimize the performance of HEVs. This paper describes various energy management strategies available in the literature. A lot of research work has been conducted for energy optimization and the same is extended for Plug-in Hybrid Electric Vehicles (PHEVs). This paper concentrates on the battery powered hybrid vehicles. Numerous methods are introduced in the literature and based on these, several control strategies are proposed. These control strategies are summarized here in a coherent framework. This paper will serve as a ready reference for the researchers working in the area of energy optimization of hybrid vehicles.

scholarly journals Effective optimal control strategy for hybrid electric vehicle with continuously variable transmission

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401882481 ◽  
Author(s):  
Hangyang Li ◽  
Xiaolan Hu ◽  
Bing Fu ◽  
Jiande Wang ◽  
Feitie Zhang ◽  
...  
Keyword(s):  
Cost Function ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Optimal Control Strategy ◽  
Variable Transmission ◽  
Hybrid Electric

Hybrid electric vehicles equipped with continuously variable transmission show dramatic improvements in fuel economy and driving performance because they can continuously adjust the operating points of the power source. This article proposes an optimal control strategy for continuously variable transmission–based hybrid electric vehicles with a pre-transmission parallel configuration. To explore the fuel-saving potential of the given configuration, a ‘control-oriented’ quasi-static vehicle model is built, and dynamic programming is adopted to determine the optimal torque split factor and continuously variable transmission speed ratio. However, a single-criterion cost function will lead to undesirable drivability problems. To tackle this problem, the main factors affecting the driving performance of a continuously variable transmission–based hybrid electric vehicle are studied. On that basis, a multicriterion cost function is proposed by introducing drivability constraints. By varying the weighting factors, the trade-off between fuel economy and drivability can be evaluated under a predetermined driving cycle. To validate the effectiveness of the proposed method, simulation experiments are performed under four different driving cycles, and the results indicate that the proposed method greatly enhanced the drivability without significantly increasing fuel consumption. Compared to a single-criterion cost function, the use of multiple criteria is more representative of real-world driving behaviour and thus provides better reference solutions to evaluate suboptimal online controllers.

scholarly journals Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1444
Author(s):  
Kyung-Hun Hwang ◽  
Joong-Hoo Park ◽  
Hee-Jung Kim ◽  
Tea-Yong Kuc ◽  
Se-Joon Lim
Keyword(s):  
Fuzzy Control ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Hybrid Electric Vehicles ◽  
Fuel Efficiency ◽  
Pi Controller ◽  
Improvement Rate ◽  
Optimal Value ◽  
Fuel Efficiency Improvement ◽  
Hybrid Electric

Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics.

A unified quantitative analysis of fuel economy for hybrid electric vehicles based on energy flow

2021 ◽  
Vol 292 ◽  
pp. 126040
Author(s):  
Xiaohua Zeng ◽  
Qifeng Qian ◽  
Hongxu Chen ◽  
Dafeng Song ◽  
Guanghan Li
Keyword(s):  
Quantitative Analysis ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Energy Flow ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric
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