Power Split Control Strategy for an EV Powertrain With Two Propulsion Machines

Yavaşoğlu, Hüseyin Ayhan; Shen, Junfei; Shi, Chuan; Gökaşan, Metin; Khaligh, Alireza

doi:10.1109/tte.2015.2504406

Cited by 32 publications

(16 citation statements)

References 17 publications

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“…In order to better utilize the high-power density of UCs, a logic threshold control strategy for HESS was proposed in EVs [134][135][136][137]. An adaptive FL-based EMS is used to determine the power split between the battery and the UC pack.…”

Section: Impedance Source Invertermentioning

confidence: 99%

A comprehensive review on hybrid electric vehicles: architectures and components

2019

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The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuelefficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors' role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability. Keywords Hybrid electric vehicle Á Hybrid energy storage system Á Architecture Á Traction motors Á Bidirectional converter Abbreviations and symbols ABS Antilock braking system AC Alternating current ADTR Antidirectional-twin-rotary ADVISOR Advanced vehicle simulator ANN Artificial neural network ASCI Auto-sequential commutated mode singlephase inverter BEV Battery electric vehicle BLDC Brushless DC motor CD Charge depletion CDFIM Cascaded DFIM CF-qZSI Current-fed quasi-ZSI CMPPT Centralized MPPT CS Charge sustaining CSI Current source inverter CS-PMSM Compound-structure PMSM CVT Continuous variable transmission DC Direct current DFIM Doubly fed induction motor DMPPT Distributed MPPT DRM Double-rotor machines DTC Direct torque control e-CVT Electronic continuous variable transmission EM Electric motor EMS Energy management system EREV Extended range electric vehicle ESS Energy storage system EV Electric vehicle FC Fuel cell

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Section: Impedance Source Invertermentioning

confidence: 99%

A comprehensive review on hybrid electric vehicles: architectures and components

2019

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“…According to study given in Ref. 22 FC operates in maximum efficiency around its 20% nominal power and its efficiency is decreasing almost linearly through its maximum power. The minimum FC operation power is limited to 20% of its nominal power due to reduce losses.…”

Section: Power Splitting In Hessmentioning

confidence: 99%

“…In a HESS, FC and/or UC could help to reduce the stress on the battery, resulting in battery lifetime extension with an effective energy management. 22,23 However, the number of input and output of the system rises with each added unit. The use of more than one propulsion machines in the powertrain and utilizing HESS increases the complexity of the system architecture, what needs a reliable energy management.…”

Section: Introductionmentioning

confidence: 99%

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Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

2020

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Due to increased environmental pollution and global warming concerns, the use of energy storage units that can be supported by renewable energy resources in transportation becomes more of an issue and plays a vital role in terms of clean energy solutions. However, utilization of multiple energy storage units together in an electric vehicle makes the powertrain system more complex and difficult to control. For this reason, the present study proposes an advanced energy management strategy (EMS) for range extended battery electric vehicles (BEVs) with complex powertrain structure. Hybrid energy storage system (HESS) consists of battery, ultra-capacitor (UC), fuel cell (FC) and the vehicle is propelled with two complementary propulsion machines. To increase powertrain efficiency, traction power is simultaneously shared at different rates by propulsion machines. Propulsion powers are shared by HESS units according to following objectives: extending battery lifetime, utilizing UC and FC effectively. Primarily, to optimize the power split in HESS, a convex optimization problem is formulated to meet given objectives that results 5 years prolonged battery lifetime. However, convex optimization of complex systems can be arduous due to the excessive number of parameters that has to be taken into consideration and not all systems are suitable for linearization. Therefore, a neural network (NN)-based machine learning (ML) algorithm is proposed to solve multi-objective energy management problem. Proposed NN model is trained with convex optimization outputs and according to the simulation results the trained NN model solves the optimization problem within 92.5% of the convex optimization one.

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“…The electrical components of the EV powertrain include a battery pack, a DC/DC converter, a DC/AC converter, an electrical machine and a control system [4] [5]. The pure battery EVs utilise batteries as the power source to drive the vehicle [6]. They have zero atmospheric emissions and represent a means of eco-friendly personal transportation.…”

Section: Introductionmentioning

confidence: 99%

Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

Cao

et al. 2019

IEEE Trans. Veh. Technol.

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An analytical model for an electric vehicle (EV) powertrain has been developed in this paper to study the vehicular dynamics, based on a Nissan Leaf EV. The electrical components of the powertrain include a battery pack, a battery management system (BMS), a DC/DC converter, a DC/AC inverter, a permanent magnet synchronous motor (PMSM), and a control system while the mechanical system consists of power transmissions, axial shaft and vehicle wheels. The driving performance of the EV is studied through the realworld driving tests and simulation tests in Matlab/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge (SOC) of the battery, and the motor output power. Finally, a number of EVs are introduced in the system to optimize the power dispatch. The greenhouse gas emissions of EVs are analyzed under various driving and charging conditions, and compared with conventional internal combustion engine (ICE) vehicles. For a given driving cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on its duty cycle and the way electricity is supplied.

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Power Split Control Strategy for an EV Powertrain With Two Propulsion Machines

Cited by 32 publications

References 17 publications

A comprehensive review on hybrid electric vehicles: architectures and components

A comprehensive review on hybrid electric vehicles: architectures and components

Neural network‐based energy management of multi‐source (battery/UC/FC) powered electric vehicle

Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

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