Energy storage systems for EVs with two propulsion machines

Yavaşoğlu, Hüseyin Ayhan; Shi, Chuan; Gokce, Kursad

doi:10.1109/itec.2017.7993354

Cited by 6 publications

(5 citation statements)

References 13 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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“…For achieving high fuel economy and long battery lifetime, the topology given in Ref. 36 is recommended. It composed of two complementary electric propulsion machines and battery/UC/FC HESS as represented in Figure 1.…”

Section: System Topologymentioning

confidence: 99%

“…For achieving high fuel economy and long battery lifetime, the topology given in Ref 36 . is recommended.…”

Section: System Topologymentioning

confidence: 99%

“…Moreover, an NN-based ML model was suggested for the proposed topology study given in Ref. 36 where EV has two propulsion machines and the results of the suggested optimization method are discussed. Therefore, the present paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, fuzzy rules based on expert knowledge are obtained on the basis of observations and does not guarantee global optimum. In a study, 36 a convex optimization problem for power splitting is formulated to extend the battery lifetime for battery/UC/FC HESS. However, due to the excessive number of parameters that has to be taken into consideration, convex optimization of complex systems may not provide online solution.…”

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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A Study of Electric Vehicle EE Power Network Architecture and Considerations to Improve Efficiency

Tamilarasu

Ramasamy

2021

2021 IEEE Transportation Electrification Conference (ITEC-India)

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Energy storage systems for EVs with two propulsion machines

Cited by 6 publications

References 13 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

A Study of Electric Vehicle EE Power Network Architecture and Considerations to Improve Efficiency

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