A Novel Self-Regulating Hybrid (PV–FC–Diesel–Battery) Electric Vehicle-EV Drive System [20]

35.8.2 A Novel Self-Regulating Hybrid (PV–FC–Diesel–Battery) Electric Vehicle-EV Drive System [20]

This application presents a number of novel self-regulating tri-loop error driven controllers for a hybrid PV–FC–Diesel– Battery powered all-wheel drive electric vehicle using Four Wheels Permanent Magnet DC (PMDC) motors, which are modeled to include existing nonlinearities in motor plus load inertia (J) and viscous friction (B). A Tri-Loop dynamic error driven scheme is proposed to regulate motor speed and current and avoid motor overloading/inrush conditions, in addition

to motor speed dynamic reference tracking. The Proposed tri- loop dynamic error driven self-tuned control schemes utilized to ensure dynamic energy efficiency, control loop decoupling, grid interface stability while maintaining reference speed track- ing capability. The integrated motor drive scheme is fully stabilized using a novel FACTS-based green filter compen- sators that ensures stabilized DC bus voltage, minimal inrush current conditions, and damped load excursions. This applica- tion presents a novel comparison of the MOPSO and Genetic search Algorithms MOGA optimization and search techniques for online dynamic tuning of the different controllers under varying renewable source conditions and load excursions.

The pressing need to utilize all abundant renewable green energy sources (Wind, Solar, PV, Wave, Tidal, Fuel Cell, Biogas, Hybrid...) is currently motivated by economic and environ- mental concerns. The increasing reliance on costly fossil fuels with increasing rate of resource depletion is causing a shift to energy alternatives, clean fuel replacement, and energy dis- placement of conventional sources to new green renewable, environmentally safe, and friendly counterparts [21, 22]. Elec- tric vehicle is one of the solutions for the reduction of the fossil fuel consumption and pollutant emissions of gas respon- sible for the green house effect. However, pure battery electric vehicles have shown their own range limitations, because of their size the vehicle habitability is reduced so has its range. Adding different kinds of power supply in the same vehicle allows taking advantages from their different characteristics [23]. Several types of electric motors may be used for EV propulsion purposes. Earlier traction motors were exclusively

dc motors, either series-excited or separately excited. Recently, more advanced ac drive systems have found application in

35 Novel AI-Based Soft Computing Applications in Motor Drives 1003

PF 0.88 PF 0.84

FIGURE 35.8 Pareto front of ME and MPF problem for different levels of rotor speed ω r = 0.2, 0.4, 0.8, 1 PU and different levels of load torque: (a) T L = 0.2 PU; (b) T L = 1 PU.

1004 A. M. Sharaf and A. A. A. El-Gammal TABLE 35.2 The limits pf the Pareto front of the two conflicting objec-

TABLE 35.3 The limits pf the Pareto front of the two conflicting objec- tive functions using MOGA

tive functions using MOPSO

T L (PU) Efficiency

Power Factor Minimum

Power Factor

T L (PU)

Efficiency

Maximum Minimum

EV propulsion using induction motors, permanent magnet

35.8.2.1 Sample Study AC–DC System

synchronous motors, and permanent magnet brushless dc Figures 35.10 and 35.11 show the proposed the Four-Wheel motors [24, 25]. The EV-DC motor speed or position control electric vehicle drive system scheme with the PV, FC sources, has been realized including conventional PI, PID, fuzzy logic the diesel generator, and the backup battery. The DC com- based, nonlinear, adaptive variable structure, model reference pensator scheme developed by the First Author is used to adaptive control, artificial neural networks (ANN), feed for- ensure stable, efficient, minimal inrush operation of the hybrid ward computed torque control strategies [26–28]. The need for renewable energy scheme. The novel PSO and GA self-tuned an on-line gains adaptation or a “tunable” control mechanism multi-regulators and coordinated controller are used for the is highly stressed in the control of any nonlinear systems with

following purposes:

un-modeled dynamics. The PSO and GA based self-regulating algorithms are utilized to track any reference speed trajectory

1. Diesel AC generator set with control regulator is based under varying parameter and load conditions. The control sys-

on excess generation and load dynamic matching as tem comprises of four different controllers used to track speed

well as stabilization of the common DC collection bus reference trajectory depicting the motor with minimum over

using six pulse controlled rectifier. current, inrush, ripple conditions. The proposed novel con-

2. AC/DC power converter regulator to regulate the DC trol scheme has been validated for effective dynamical speed

voltage at the Diesel engine AC/DC interface and ensure reference trajectory tracking and enhanced power utilization.

limited inrush conditions as well as dynamic power

35 Novel AI-Based Soft Computing Applications in Motor Drives 1005

MOPSO Stator current (PU) 0.8 FOCS CVFRS

Power factor

0.5 Slip speed (PU) 0.01

Load torque (PU)

Load torque (PU)

MOPSO 0.55 Stator current (PU) 0.8 FOCS CVFRS

Power factor

Slip speed (PU) 0.02

Load torque (PU)

Load torque (PU)

(b)

FIGURE 35.9 The comparison between CVFRS, FOCS, and MOPSO for different levels of load torque T L and different levels of rotor speed: (a) ω r = 0.2 PU; (b) ω r = 1 PU.

R f3 L f3

C f3 GPFC R fd

All-wheel drive electric vehicle Four PMDC motors

C d GPFC

500 KVA SG i g

Gear box

600 KW @ 3600 rpm DIESEL

V g 6 - pulse controlled

dynamic controller

Multiloop

Fuel g v

C self = 300

M. Sharaf

controller

controller

I d V d Tri-Loop

FIGURE 35.10 The proposed all-wheel electric vehicle drive system with PV–FC–Diesel–Battery AC/DC Generation.

A. A. A. El-Gammal

35 Novel AI-Based Soft Computing Applications in Motor Drives 1007 mover and generator. Ideally, the prime mover has the capabil-

ity to supply any power demand up to rated power at constant PMDC

PMDC

synchronous frequency. The synchronous generator connected to it must be able to keep the voltage constant at any load condi- tion. The diesel engine kept the operating speed and frequency constant. When power demand fluctuates the diesel genera-

Intillegent self tor could vary its power output via fuel valve regulation and regulating controller

governor control. The synchronous generator must control its output voltage by controlling its excitation current. Thus, the diesel generating system, as an auxiliary source, must be able to

4 Quadrants DC–DC control its frequency and its output voltage. The ability of the

converter

diesel generator to respond to any frequency changes is affected by the inertia of the diesel gen-set, the sensitivity of the gover- nor, and the power capability of the diesel engine. The ability

GPFC

of the AC synchronous generator to control its terminal voltage can be affected by the field-winding time constant, the avail-

ability of DC excitation power to supply the field winding, and PV-FC-diesel-battery

Hybrid

the time constant of the voltage control loop.

B. Photo Voltaic PV The equivalent circuit shown in Fig.

35.12 is used to model the PV cells used in the proposed PV array [29]. This model consists of a current source, a resis-

tor, and a reverse parallel connected diode. The PVA model developed and used in Matlab/Simulink environment is based upon the circuit given in Fig. 35.12, in which the current pro- duced by the solar cell is equal to that produced by the current

PMDC

PMDC

FIGURE 35.11 Schematic diagram of a prototype all-wheel drive elec- source, minus that which flows through the diode, minus that tric vehicle using four PMDC motors.

which flows through the shunt resistor:

matching to reduce current transients and improve

=I L −I D −I SH

utilization at the diesel engine interface AC–DC bus.

3. The DC side Green plug filter compensator GPFC– where I = output current, I L = photo generated current, I D = diode SPWM regulator for pulse width switching scheme to current, I SH = shunt current. The current through these ele- regulate the DC bus voltage and minimize inrush cur- ments is governed by the voltage across them: rent transients and load excursions and/or PV and FC non linear Volt-Ampere characteristics. The GPFC

(35.9) device acts as a matching DC–DC interface device between the DC load dynamic characteristics and that of where V j = voltage across both diode and resistor R SH (volts),

V j = V + IR S

the hybrid main PV, FC and backup diesel generator set. V = voltage across the output terminals (volts), I = output

4. The PMDC motor drive with the speed regulator that current (amperes), R S ensure speed reference tracking with minimum inrush

The current diverted through the diode is conditions and ensure reduced voltage transients and

qV & j improved energy utilization.

The unified DC–AC utilization scheme is fully validated using the Matlab/Simulink software environment under nor-

R S mal conditions, DC load excursion, PMDC motor torque

I changes and the PV, and FC source output variations due to the

+ inherent Volt–Ampere nonlinear relationship. Other excursion

I I SH

conditions in the diesel engine generator set are also intro- duced to assess the control system robustness, effective energy

v utilization, and speed reference tracking.

R SH

A. Diesel Generator Set − From an electrical system point of view, a diesel driven AC generator can be represented as a prime

FIGURE 35.12 The equivalent circuit of a solar cell.

1008 A. M. Sharaf and A. A. A. El-Gammal Basic solar cell current and power output

Current at 1 sun (A)

EOC Activation

1.4 Power at 1 sun (W)

Mass transport

1 region 0.8

Fuel cell voltage (V)

0.4 Current (A) or power (W)

0 0.1 0.2 0.3 0.4 0.5 0.6 Fuel cell current (A)

FIGURE 35.15 (V − I) polarization curve of an SOFC. FIGURE 35.13 The behavior of a solar cell at particular intensities of

Cell voltage (V)

solar radiation. where I 0 = reverse saturation current (amperes), n = diode

C. Fuel Cell Energy System Model Fuel cell stacks were con- ideality factor (1 for an ideal diode), q = elementary charge, nected in series/parallel combination to achieve the rating k = Boltzmann’s constant, T = absolute temperature.

desired. Figure 35.14 shows a simplified diagram of the PEMFC The characteristic equation of a solar cell, which relates solar system [31, 32]. The FC model here is for a type of PEM, which cell parameters to the output current and voltage [30]:

uses the following electrochemical reaction:

H 2 + O 2 →H 2 O + Heat + Electrical Energy (35.12)

Figure 35.15 shows a simulated V – I (voltage versus current) polarization curve of a fuel cell [31, 32]. As the cell current begins to increase from zero, a sudden drop of the output volt-

where R SH age of the fuel cell is seen. This drop of the cell voltage is due illuminated PV cell has the shape shown in Fig. 35.13 as the to activation voltage loss. Then, almost a linear decrease of the voltage across the measuring load is swept from zero to V OC . cell voltage is seen as the cell current increases beyond certain The power produced by the cell in Watts can be easily calcu- values, as shown in Fig. 35.16, which is a result of the ohmic lated along the I – V sweep by the equation P = IV . At the loss. Finally, the cell voltage drops sharply to zero as the load

I SC and V OC points, the power will be zero and the maximum current approaches the maximum current density that can be value for power will occur between the two.

generated of the fuel cell. The sharp voltage drop is the effect of

V FC (t)

i FCREF (t)

i FC (t)

Hydrogen

Fuel cell

from bottle

PEMFC stack

controller

Flux control

Humidifier

Air from compressor

Excess

Condenser

Heat exchanger

Excess

FIGURE 35.14 Simplified diagram of the Fuel Cell PEMF-C system.

35 Novel AI-Based Soft Computing Applications in Motor Drives 1009

=E − NAln(i The com-

D. DC Side Green Plug Filter Compensator GPFC

E oc

mon concerns of power quality are the long duration voltage Internal

fc )

i fc variations (overvoltage, under-voltage, and sustained interrup- resistance

tions), short duration voltage variations (interruption, sags, and swells), voltage imbalance (voltage unbalance), waveform

distortion (DC offset, harmonics, inter-harmonics, notching E −

i fc

V fc and noise), voltage fluctuation (voltage flicker), and power fre- quency variations [33] . To prevent the undesirable states and

to reduce the power consumption, a GPF scheme is used to FIGURE 35.16 DC-Equivalent circuit of an electrochemical fuel cell.

stabilize the common DC bus.

E. Dynamic Error driven Control The proposed control the concentration loss in the fuel cell. The fuel cell can be com- system developed by First Author comprises of four sub- monly modeled by simple equivalent first order circuit shown regulators or controllers named as a Diesel DC generator in Fig. 35.10. The open circuit voltage is modified as follows: set value control regulator, DC side Green Plug Filter Com-

pensator GPFC–SPWM regulator, the PMDC motor drive

E oc = N(E n − A ln(i o )) (35.13) speed controller, and the AC/DC power converter regulator. Figures 35.11–35.14 depict the proposed multi-loop dynamic

where self-regulating controllers based on MOO search and optimiza- tion technique based on soft computing PSO and GA. The

RT

(35.14) global error is the summation of the three loop individual

ZαF

errors including voltage stability, current limiting, and synthe- size dynamic power loops. Each multi-loop dynamic control

where R = 8.3145 J/(mol K), F = 96485 A s/mol, z = Number scheme is used to reduce a global error based on a tri-loop of moving electrons, E n = Nernst voltage, which is the thermo- dynamic error summation signal and to mainly track a given dynamics voltage of the cells and depends on the temperatures speed reference trajectory loop error in addition to other sup-

and partial pressures of reactants and products inside the stack, plementary motor current limiting and dynamic power loops

i 0 = Exchange current, which is the current resulting from the are used as auxiliary loops to generate a dynamic global total continual backward and forward flow of electrons from and to error signal that consists of not only the main loop speed error the electrolyte at no load. It also depends on the temperatures but also the current ripple, over current limit and dynamic over and partial pressures of reactants inside the stack, α = Charge load power conditions. transfer coefficient, which depends on the type of electrodes

The global error signal is input to the self-tuned controllers and catalysts used, T = Temperature of operation. The fuel shown in Figs. 35.17–35.20. The (per-unit) three dimensional- cell voltage VFC is modeled as [31, 32]: error vector (e vg ,e Ig ,e pg ) of the diesel engine controller scheme

is governed by the following equations:

V FC =E oc −V Activation Loss −V Ohmic Loss −V Concentration Loss

1 + ST g where

1 + ST g 1 + SD

+i (35.19) n

FC

V Activation Loss = A log

V Ohmic Loss =R m ( I FC +i n )

Concentration Loss = B log 1 −

V (35.20)

1 1 Fuel cell stacks were connected in series/parallel combina-

e Pg ( k) =I g ( k) ×V g ( k)

1 + ST g 1 + SD tion to achieve the rating desired. The output of the fuel cell

array was connected to a DC bus through a DC/DC converter.

The DC bus voltage was kept constant via a DC bus voltage

(35.21) controller.

−I g ( k) ×V g ( k)

1 + ST g

1010 A. M. Sharaf and A. A. A. El-Gammal

D = 10 mSec D

Current limiting loop

1 1 − e + Id γ

Id

i d I dbase

S e SPWM

Tuning controller

S A =S C = Not (S B )

1 I +T d S

Voltage stabilization loop v d 1 1 e Vd

V dbase

I +T d S

γ vd

V dref FIGURE 35.17 Tri-loop error driven self-regulating VSC/SMC/B–B Controller for the common DC side – GPFC Scheme.

D = 10 mSec D

Current limiting loop

1 1 e − Ig +

I I gbase

Tuning controller

0<g v <1

1 I+T g S

Voltage stabilization loop v g 1 1 e Vg

V gbase

I+T g S

γ vg

V gref FIGURE 35.18 Tri-loop error driven self-regulating VSC/SMC/B–B Controller for the diesel engine generator set.

The total or global error e tg (k) at the AC side scheme at a following equations: time instant:

In the same manner, the (per-unit) three dimensional-error

(35.23) vector (e vd ,e Id ,e pd ) of the GPFC scheme is governed by the

−V d ( k)

1 + ST d

35 Novel AI-Based Soft Computing Applications in Motor Drives 1011

ω mref

Speed loop ω m

1 1 e ωm

S 1 = Not (S 2 ) Speed tracking loop

S SPWM

Dynamic momentum loop 3 ×

Tuning controller

DC − DC converter pulsing

I+T p S

Current loop

1 1 e Im

I mbase

I+T I S

γ Im

Current limiting loop

D FIGURE 35.19 Tri-loop error driven self-regulating VSC/SMC/B–B Controller for dynamic speed control PMDC motor drive.

D = 10 mSec D

Current limiting loop

1 1 − + e IR γ

i R I Rbase I+T R S

Tunned controller

Voltage stabilization loop

1 1 e VR

V Rbase

I+T S

γ vR

V Rref

FIGURE 35.20 Tri-loop error driven self-regulating VSC/SMC/B–B Controller firing angle α- controller for Diesel AC/DC interface rectifier scheme.

And the total or global error e td (k) for the DC side green

1 + ST d

plug filter compensator GPFC scheme at a time instant:

e Pd ( k) =I d ( k) ×V d ( k)

1 + ST d 1 + SD

e td ( k) =γ vd e vd ( k) +γ id e id ( k) +γ pd e pd ( k) (35.26)

1012 A. M. Sharaf and A. A. A. El-Gammal In addition, the (per-unit) three dimensional-error vector by the following:

(e vR ,e IR ,e pR ) of the three-phase controlled rectifier scheme is governed by the following equations:

J = 1 = Minimize <|e tg |, |e tR |, |e td |, |e tm | (35.35)

e vR ( k) ( k) J 2 = Steady State Error = |e ω (k)

J 3 = Settling Time

J 4 = Maximum Over Shoot

J 5 = Rise Time

1 1 In general, to solve this multi-objective complex optimality

search problem, there are two possible optimization techniques based on PSO: Single aggregate selected Objective Optimiza-

−I R ( k) ×V R ( k) (35.29) tion (SOO), which is explained and MOO. The main procedure

1 + ST R

of the SOO is based on selecting a single aggregate objective function with weighted SO parameters scaled by a number of

The total or global error e tR (k) for the three-phase con- weighting factors. The objective function is optimized (either trolled converter rectifier scheme at a time instant:

minimized or maximized) using either GA or PSO methods to obtain a single global or near optimal solution. On the

e tR ( k) =γ vR e vR ( k) +γ iR e iR ( k) +γ pR e pR ( k)

(35.30) other hand, the main objective of the MO problem is find- ing the set of acceptable (trade-off) Optimal Solutions. This

Finally, the (per-unit) three dimensional-error vector (e ω m , set of accepted solutions is called Pareto front. These accept-

e Im ,e pm ) of the PMDC motor scheme is governed by the able trade-off multi-level solutions give more ability to the following equations:

user to make an informed decision by seeing a wide range of near optimal selected solutions that are feasible and accept- able from an “overall” standpoint. SO optimization may ignore

e ω m ( k) =ω m ( k) this trade-off viewpoint, which is crucial. The main advantages

1 + ST m

1 + SD

of the proposed MOO method are: It doesn’t require a priori

1 knowledge of the relative importance of the objective func- −ω m ( k)

1 + ST tions and it provides a set of acceptable trade-off near optimal m solutions. This set is called Pareto front or optimality trade-off

1 1 surfaces. Both SOO and MOO searching algorithms are tested,

validated, and compared. The dynamic error driven controller regulates the controllers’ gains using the PSO and GA to mini-

−I m ( k) (35.32) mize the system total error, the settling time, the rising time,

1 + ST m

and the maximum overshoot. The proposed dynamic Tri-Loop Error Driven controller, developed by the First Author, is a

e Pm ( k) =I m ( k) ×ω m ( k) novel advanced regulation concept that operates as an adaptive

1 + ST m

1 + SD

dynamic type multi-purpose controller capable of handling

1 sudden parametric changes, load and/or DC source excursions. −I m ( k) ×ω m ( k)

+ ST By using the Tri-Loop Error Driven controller, it is expected to have a smoother, less dynamic overshoot, fast, and more robust speed controller when compared to those of classical

And the total or global error e tm (k) for the MPFC scheme at control schemes. The proposed general PMDC Motor Drive

a time instant: Model with the novel Tri-Loop Error Driven controller is fully

validated in this application for effective reference speed tra-

e tm ( k) =γ ω m e ω m ( k) +γ im e im ( k) +γ pm e pm ( k)

(35.34) jectory tracking under different loading conditions and para- metric variations such as temperature changes while driving

a complex mechanical load with nonlinear parameters and/or optimize using the PSO algorithm. These functions are defined torque–speed characteristics.

A number of conflicting objective functions are selected to

35 Novel AI-Based Soft Computing Applications in Motor Drives 1013

d/dt

FIGURE 35.21 VSC/SMC/B–B Controller block diagram.

ANN controlled controller

FIGURE 35.22 ANN incremental Controlled Controller block diagram.