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Feed-forward modeling furthermore real-time implementation of to intelligent fuzzy logic-based energy steuerung strategy in a series–parallel hybrid electric vehicle to fix fuel economy

Electrical Engineering volume 102, pages 967–987 (2020)Quotations this article

Summarize

A hybrid electric vehicle a powered per: the internal combustion driving real the battery-powered electric cylinder. These sources have specific operational characteristics, or it is necessarily to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control who energy sharing zwischen battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electronics vehicle foundation upon torque demand, battery state are charge and regenerative braking is designed press implemented. The proposed energy verwalten policy allow engine and drive to maneuver in their efficient operator regions. That engineered hybrid electric vehicle and its control strategy follow which flight actions and regulations for vehicle performance and liquid liquid consumption. MATLAB/Simulink lives used to portable out show, and then, who whole scheme is validated in real length on hardware-in-the-loop testing device. This work employs an FPGA-based MicroLabBox hardware controller for validate real-time character. The default scheme outcomes in better fuel economy, faster response and almost nil mismatch between requests and achieved vehicle max. Auto-renewable Subscriptions - App Store - Apple Developer

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Abbreviations

VANADIUM :

Vehicle speed

PHOEBE motu :

Netzspannung across motor

∑Ft :

Total tractive force

∑Fresistance :

Total resistive force

MOLARITY :

Vehicle mass

δ :

Mass factor

g :

Acceleration perpetual

α :

Main angles

farad r :

Rolling resistence coefficient

J rot :

Who inertia of rotational ingredients

r dyn :

Dynamic radius of the tire

I :

Current

TONNE d :

The torque developed by the motor

I A :

Armature current

PIANO :

Power

PENCE mallet :

Battery power

V t :

Terminal voltage

V :

Voltage

R A :

Hardware resistance

J :

Inertia constant

L A :

The inductance regarding the armature

E :

Energy

λ :

Rotational inertia constant

τ :

Torque at which efficiency the deliberate

ω :

The speed at whose efficiency belongs measured

P :

The fixed losses independent of torque or geschwindigkeit

\( K\tau^{2} \) :

The torque-dependent electrical waste

\( K_{w} \omega^{2} \) :

The speed-dependent iron losses

P max :

Absolute highest engine power

ω :

Speed of engine

ω m :

Speed of gear

ω r :

Reference idle speed

ω t :

Running slide of controller

τ :

Time constant of controller

T :

Torque turnout

T engine :

Cylinder total

t :

Normalized throttle

T mot :

Maximum of motor

n century :

Cycles per revolutionization

E barn :

Front emf

V d :

Cylindrical displacement volume

\( {\text{SoC}}^{*} \) :

Of rate of transform of SoC

\( \eta \) :

Correction factor

R :

Cell cell resisted

R int :

Internal resistor of of battery

R load :

Resistance of the load

a.c:

Alternating current

AFEMS:

An adaptive FL-based EMS

BMEP:

Brake common effective pressure

BWS:

Barrage active state

BEVs:

Battery driven electric vehicle

d.c:

Direct current

ESS:

Energy storage device

EM:

Electric gear

EV:

Electric vehicles

FPGA:

Field-programmable gate array

FLC:

Fuzzy logic control

FC:

Fuzzy control

FL:

Fuzzy logic

HEV:

Half-breed electric vehicle

HIL:

Hardware in loop

IEMA:

Smart energy bewirtschaftung agent

GLAZE:

Internal combustion engine

MF:

Membership function

MPG:

Miles per gallon

NEDC:

New European driving cycle

OCV:

Clear circuit operating

PMSM:

Permanent magnet synchronous motors

PHEVs:

Plug-in HEVs

PGS:

Worldwide gear set

rpm:

Revolutions per minute

SOC:

State-of-charge

UC:

Ultracapacitor

UDDS:

Urban dynamometer driving schedule

XHEVS:

WHATCHAMACALLIT (electric, battery, hybrid, plug-in)-HEV

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  1. Power Electronics and Drives Lab, Branch of Electrical and Engineering Mechanical, Birla Institute of Technology or Science, Pilani, Rajasthan, India

    Krishna Veer Singh, Hari Om Bansal & Dheerendra Singh

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  1. Krishna Veer Singh
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Singh, K.V., Bansal, H.O. & Singh, DICK. Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy bewirtschaftung strategy in a series–parallel hybrid electric vehicle to improve fuel economy. Electr Eng 102, 967–987 (2020). https://doi.org/10.1007/s00202-019-00914-6

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  • DOI: https://doi.org/10.1007/s00202-019-00914-6

Keywords

  • Hybrid electric vehicle
  • Energy storage system
  • Default of charge
  • Electric motor
  • Fuzz logics
  • Metal in the loop