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Model Predictive Control of Bidirectional Isolated DC-DC Converter for Energy Conversion SystemParvez Aktera, Nadia Mei Lin Tanb, Saad Mekhilefc & Hirofumi Akagida Power Electronics and Renewable Energy Research Laboratory (PEARL), Department ofElectrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysiab Member, IEEE, Department of Electrical Power Engineering, Universiti Tenaga Nasional,Kajang 43000, Malaysiac Senior Member, IEEE, Power Electronics and Renewable Energy Research Laboratory(PEARL), Department of Electrical Engineering, University of Malaya, 50603 KualaLumpur, Malaysiad Fellow, IEEE, Department of Electrical and Electronic Engineering, Tokyo Institute ofTechnology, Tokyo 152-8552, JapanAccepted author version posted online: 12 Mar 2015.

To cite this article: Parvez Akter, Nadia Mei Lin Tan, Saad Mekhilef & Hirofumi Akagi (2015): Model Predictive Controlof Bidirectional Isolated DC-DC Converter for Energy Conversion System, International Journal of Electronics, DOI:10.1080/00207217.2015.1028479

To link to this article: http://dx.doi.org/10.1080/00207217.2015.1028479

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Publisher: Taylor & Francis

Journal: International Journal of Electronics

DOI: 10.1080/00207217.2015.1028479

Model Predictive Control of Bidirectional Isolated DC-DC Converter

for Energy Conversion System

Corresponding author:

Parvez Akter

Power Electronics and Renewable Energy Research Laboratory (PEARL), Department

of Electrical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia

E-mail: suzon_cuet06@yahoo.com

Contact No: +60169067036

Co-authors:

Nadia Mei Lin Tan

Member, IEEE, Department of Electrical Power Engineering, Universiti Tenaga

Nasional, Kajang 43000, Malaysia

Saad Mekhilef

Senior Member, IEEE, Power Electronics and Renewable Energy Research Laboratory

(PEARL), Department of Electrical Engineering, University of Malaya, 50603 Kuala

Lumpur, Malaysia

Hirofumi Akagi

Fellow, IEEE, Department of Electrical and Electronic Engineering, Tokyo Institute of

Technology, Tokyo 152-8552, Japan

Model Predictive Control of Bidirectional Isolated DC-DC Converter

for Energy Conversion System

Model Predictive control (MPC) is a powerful and emerging control algorithm in

the field of power converters and energy conversion systems. This paper

proposes a model predictive algorithm to control the power flow between the

high voltage and low voltage DC buses of a bidirectional isolated full-bridge DC-

DC converter. The predictive control algorithm utilizes the discrete nature of the

power converters and predicts the future nature of the system, which are

compared with the references to calculate the cost function. The switching state

that minimizes the cost function is selected for firing the converter in the next

sampling time period. The proposed MPC controlled bidirectional DC-DC

converter is simulated with Matlab/Simulink and further verified with a 2.5 kW

experimental configuration. Both the simulation and experimental results confirm

that the proposed MPC algorithm of the DC-DC converter reduces reactive

power by avoiding the phase-shift between primary and secondary sides of the

high frequency transformer and allow power transfer with unity-power-factor.

Finally, an efficiency comparison is performed between the MPC and dual-

phase-shift based PWM controlled DC-DC converter which ensures the

effectiveness of the MPC controller.

Keywords: Predictive control; bidirectional DC-DC converter; unity power

factor; reactive power; power conversion system.

1. Introduction

An efficient bidirectional DC-DC converter is indispensable to manage the power flow

by switching action to provide high performance and efficiency of an energy conversion

system. Hence, the control algorithm of this DC-DC converter needs to be immensely

effective, as it consist of two conversion stages (single phase inverter and rectifier)

along with an isolated high frequency transformer (Zhao, Song, Liu, & Sun, 2014).

Several improved control techniques, such as fuzzy-neural control (Cheng, Hsu, Lin,

Lee, & Li, 2007), hysteresis control (Leung & Chung, 2005), sliding-mode control

(Cheng et al., 2007; Tsai & Chen, 2007), have been investigated in power electronic

systems to control power converters. The practical applications of these control methods

are confined to simple configured boost, buck, half-bridge, and full-bridge

unidirectional converters topologies till today. Nevertheless, these controls are aimed to

control more complex-configured converters topologies (Bai & Mi, 2008a; Rivetta,

Emadi, Williamson, Jayabalan, & Fahimi, 2006).

The bidirectional isolated DC-DC converter has many advantages over

traditional converters, such as bidirectional energy flow, soft switching, galvanic

isolation, high power density and low system parasitic sensitivity as has been

investigated in (Bai & Mi, 2008b), (N. M. Tan, T. Abe, & H. Akagi, 2012) and

(Naayagi, Forsyth, & Shuttleworth, 2012). A traditional PI-based phase-shift control has

been used previously to control the bidirectional isolated DC-DC converter in

(DeDoncker, Kheraluwala, & Divan, 1991). This method of control has a simple

structure and is very easy to implement. Moreover, a phase-shift modulation control has

been applied for high power transfer in (Kheraluwala, Gascoigne, Divan, & Baumann,

1992) and (Tan, Inoue, Kobayashi, & Akagi, 2008). The major drawbacks of this phase-

shift modulation control are reactive power losses and existence of dead-time effect.

Subsequently, various control algorithms have been introduced to improve the

performance of the system. In order to achieve a higher converter efficiency and expand

the zero-voltage switching region, a phase-shift plus pulse-width modulation (PWM)

algorithm has been applied in (Xu, Zhao, & Fan, 2004) and the effectiveness is verified

for soft-switching in (Lee, Ko, & Chi, 2010) and (Xiao & Xie, 2008). On the other

hand, in literature a dual-phase-shift (DPS) control method is proposed to eliminate the

reactive power in isolated bidirectional full-bridge DC-DC converter (Bai & Mi,

2008b), which is being extended as a model-based phase-shift control and applied in

(Bai, Nie, & Mi, 2010) to improve dynamic performance of the converter. Furthermore,

two PI controller based adaptive control algorithm is presented in (De Breucker,

Engelen, Tant, & Driesen, 2010) to operate and verify the changes of current in small

and large extent, separately. There is a trade-off between transient response and

robustness in parameter selection of a PI controller, which is very empirical. Therefore,

computer aided optimization of DC-DC converter have been carried out by using two

PWM controller integrated circuits (ICs) in (Neugebauer & Perreault, 2003) and

(Garcia, Zumel, de Castro, & Cobos, 2006).

The principle feature of MPC scheme is to predict the future behavior of control

variables. This control algorithm has become the most attractive mode of technique to

control the bidirectional DC-DC converter comparing with all the classical control

techniques discussed above due to its simple and intuitive concept with fast dynamic

responses (Cortes, Kazmierkowski, Kennel, Quevedo, & Rodríguez, 2008; Rodriguez et

al., 2013). Moreover model predictive control algorithm is easy to configure with

constraints and non-linearity and also very easy for practical implementation. The fast

and powerful microprocessors are available today to implement the predictive control

algorithm very easily as it requires higher number of calculations compared with all the

classical controls stated in (Cortes et al., 2008; Muslem Uddin, Mekhilef, Rivera, &

Rodriguez, 2013). So, the application of model predictive control algorithm in power

converter is increasing day by day. Till to date, this algorithm is proposed for a DC-DC

buck converter (Bibian & Jin, 2002), (Geyer, Papafotiou, & Morari, 2008), boost

converter (Bibian & Jin, 2001) and buck-boost converter (Chen, Prodic, Erickson, &

Maksimovic, 2003). The authors of (Xie, Ghaemi, Sun, & Freudenberg, 2012) proposed

model predictive control for full bridge DC-DC converter. However, this model is

limited only for unidirectional power flow. Although model predictive algorithm is an

efficient and attractive alternative for controlling the power converters, it has not been

used to control power flow of a bidirectional isolated DC-DC converter yet.

200 V50 Hz

PWM Converter

4 kHzTransformer

Cdc1V1 V2

Bidirectional Isolated DC-DC Converter

Bridge 1 Bridge 2

Cdc2 Vdc2

Lah Lal

High Voltage DC Bus Low Voltage DC Bus

Power Flow

Figure 1. Energy conversion system based on the bidirectional isolated DC-DC

converter (N. M. L. Tan, T. Abe, & H. Akagi, 2012).

This paper proposes a MPC algorithm and its application for a bidirectional

isolated full-bridge DC-DC converter and is organized in the following manner. The

system configuration and working principle of energy conversion system topology are

elaborately described in section 2. The formulation of MPC method with discrete time

model, the cost function used for selection of switching state and a detailed explanation

of control scheme and algorithm are mentioned in section 3. The efficiency and

performance of the proposed MPC controlled bidirectional isolated DC-DC converter is

tested with Matlab/Simulink and the simulation results are analyzed in section 4.

Therefore, the DC-DC converter is further verified with a 2.50 kW experimental set-up,

which is depicted in section 5. Finally the conclusions are drawn in section 6.

2. Description of energy conversion system topology

2.1. System configuration

Figure 1 shows the energy conversion system topology, which bi-directionally converts

the AC power from three-phase AC-grid to low voltage DC bus and is configured with a

three phase bidirectional PWM controlled AC-DC converter and a bidirectional full-

bridge isolated DC-DC converter. The energy conversion system in Figure 1 is similar

to that in (N. M. Tan et al., 2012) because this paper intends to improve the efficiency

and performance of the bidirectional isolated DC-DC converter using MPC algorithm.

Vdc2DC-ACVdc1 Cdc2Cdc1V2V1

LeqReq

Bridge 1 Bridge 2

Power flow

Figure 2. Simplified bidirectional isolated high frequency transformer linked DC-DC converter scheme.

The bidirectional DC-DC converter consist of two symmetrical structured

converters denoted as bridge 1 and bridge 2, which are isolated with a high frequency

(4-kHz) transformer. Bridge 1 consists of four IGBT-Diode switches (S1-S4). Each leg

of the converter contains two IGBTs in series. A snubber capacitor is connected with

each of the IGBTs for minimizing the turn-off overvoltage and also achieves zero-

voltage switching. Again, bridge 2 is configured with four MOSFET switches (S5-S8),

as it is operated in low voltage (60 V) condition. To minimize the switching loss, small

snubber capacitor is connected with each MOSFET switches.

2.2 Working principle

Model predictive control (MPC) algorithm is applied to control the power flow of

bidirectional isolated DC-DC converter. The working principle of MPC method is based

on a finite number of possible switching states, which utilizes the discrete behaviour of

a static power converter. In the case of bidirectional isolated DC-DC converter, MPC

algorithm utilizes the discrete nature of equivalent inductances (Leq) to control the

power flow by appropriate switching action. The equivalent inductance (Leq) is defined

with the simplified diagram of bidirectional DC-DC converter presented in Figure 2, as

the equivalent inductance value of the transformer high side auxiliary inductance (Lah),

low side auxiliary inductance (Lal) and leakage inductance (Lleak). For the selection of

the appropriate switching state to be applied to the converter, a selection criterion must

be defined with a cost function which measures the error between references and

predicted values. Finally, the state that minimizes the cost function is selected for the

next sampling interval.

The gating signals Sa, Sb, Sc and Sd determine the switching states of the

Bidirectional DC-DC converter as follows:

=on is and off is ,0off is andon is ,1

Sa (1)

Sb (2)

Sc (3)

Sd (4)

Hence, the switching function (S) for the bridges 1 and 2 of the DC-DC converter can

be expressed as:

babridge SSS −=1 (5)

dcbridge SSS −=2 (6)

The bidirectional DC-DC converter operates in two modes. First one is buck

mode, which allows power transfer from high voltage DC bus to low voltage DC bus.

During this buck mode of operation, bridge 1 of the bidirectional isolated DC-DC

converter (Figure 3) is working as a DC-AC inverter, supplying power to high-

frequency transformer while bridge 2 working as AC-DC rectifier. On the other hand, in

case of boost mode, power flows in opposite direction and bridge 2 of the DC-DC

converter (Figure 4) acts as DC-AC inverter and bridge 1 as AC-DC rectifier.

4 kHzTransformer

V1 V2Vdc1

Idc1 Buck Mode of DC-DC Converter

Bridge 1 Bridge 2

Power flow

Lah Lal

Figure 3. Buck mode operation of DC-DC converter.

2.1.1. Buck Mode Operation

Buck mode operation of DC-DC converter with MPC method is illustrated in Figure 3,

where bridge 1 of the DC-DC converter acts as an inverter and the equivalent

inductance Leq, resistance Req and rest of the part of the circuit act as an equivalent load

for the inverter. By applying Kirchhoff’s voltage law at the AC side of inverter (bridge

1), the model current equation of the inverter becomes,

2111 nVVS

dtdiL dcbridgeeq −= (7)

Where, Leq is the sum of transformer leakage inductance (Lleak), Lah and n2Lal

mentioned in Fig. 3. Vdc1 is the DC voltage at high voltage DC bus acting as supply

voltage for bridge 1.

On the other hand, bridge 2 of the converter is working as a single phase voltage

mode controlled rectifier. The transformer secondary winding voltage (V2) and current

(i2) act as a source voltage and current for this rectifier. Therefore, the model voltage

equation of the rectifier can be obtained by using the Kirchhoff’s current law at the low

voltage DC bus side,

2 dcbridgedc

dc ISidt

dVC −= (8)

Where, Cdc2 is the capacitance connected in parallel with low voltage DC bus; Vdc2 and

Idc2 are the DC voltage and DC current respectively at the low voltage DC bus.

4 kHzTransformer

V1V2VDC1

IDC1 Boost Mode of DC-DC Converter

Bridge 1 Bridge 2

Power flow

Lah Lal

Figure 4. Boost mode operation of DC-DC converter.

2.1.2. Boost Mode Operation

Figure 4 describes the boost mode operation of the DC-DC converter, in which the

energy is transferred from low voltage DC bus to high voltage DC bus. In this case,

bridge 2 of the DC-DC converter acts as inverter and bridge 1 as rectifier. Hence,

similar to the equations (7) and (8), model of the inverter and rectifier in boost mode

can be obtained and presented in equations (9) and (10) as below:

dtdiL dcbridgeeq

2 −= (9)

where, Vdc2 is the DC voltage at low voltage DC bus acting as supply voltage for the

bridge 2.

1 dcbridgedc

dc ISidt

dVC −= (10)

where, Cdc1 is the capacitance connected in parallel with high voltage DC bus; Vdc1 and

Idc1 are the DC voltage and DC current respectively at the high voltage DC bus.

3. Formulation of MPC method for DC-DC converter

The formulation of model predictive control (MPC) algorithm for bidirectional isolated

DC-DC converter is described in the following section. It is necessary to transform the

dynamic system of DC-DC converter for both buck and boost mode of operation

represented in equation (7)–(10) into discrete time model at a specific sampling time Ts.

3.1 Discrete Time Model for Prediction Horizon

The discrete time model is used to predict the future values of currents and voltages in

the next sampling interval (k+1), from the measured currents and voltages at the kth

sampling instant. The system model derivative dtdx from Euler approximation can be

expressed as:

sTkxkx

dtdx )()1( −+

≈ (11)

Using the above approximation, the discrete time model of predictive currents and

voltages for the next (k+1) sampling instant of the bidirectional full-bridge DC-DC

converter in buck and boost mode can be derived.

3.1.1. Buck Mode

During the buck mode operation of the system, bridge 1 of the DC-DC converter is

working as inverter and it is controlled in current mode. On the other hand, bridge 2 is

controlled with voltage mode as it is working as a rectifier. Hence the discrete time

model of predictive currents at the next sampling instant (k+1) for the inverter (bridge

1) of the DC-DC converter can be evaluated from equation (7) and (11) as:

( ) )()()(1 21111 knVkVSLTkiki dcbridge

s −+=+ (12)

and the discrete time model of predictive voltage at the next sampling instant (k+1) for

the rectifier (bridge 2) can also be presented from equation (8) and (11) as follows:

( ) )()()(1 2222

22 kIkiSCTkVkV dcbridgedc

sdcdc −+=+ (13)

3.1.2. Boost Mode

In boost mode operation, the DC-DC converter operates in reverse mode corresponding

to buck one. Therefore, bridge 1 of this converter works as rectifier and bridge 2 as

inverter. Then, discrete time model of predictive voltage and current for the inverter and

rectifier can be written as:

( ) )()()(1 1111

11 kIkiSCTkVkV dcbridge

sdcdc −+=+ (14)

−+=+

nkVkVS

LTkiki dcbridge

s )()()(1 12222 (15)

3.2. Cost function:

The main objective of model predictive control algorithm is to minimize the error with

fast dynamic response between the predicted and reference values of the discrete

variables. To achieve this objective, an appropriate cost function is defined with a

measurement of predicted input error. Hence, the cost function for inverter and rectifier

can be expressed with the absolute error between the predictive and reference values for

both the buck and boost mode as below:

)1()1( +−+= kikig prefi (16)

)1()1( +−+= kVkVg prefr (17)

where, gi and gr are the cost function for inverter and rectifier respectively. iref(k+1) and

ip(k+1) are the reference and predicted current for the inverter. On the other case,

Vref(k+1)and Vp(k+1)are the reference and predicted voltage for the rectifier.

3.3. Control Scheme

Figure 5 shows the proposed control strategy of model predictive control (MPC)

algorithm. At first, the operating mode is selected depending on the direction of power

flow. During buck mode of operation, the input current i1(k) is measured and the future

value of this current i1 (k+1) is predicted by using the discrete time equation (12) for

each one of four possible switching vector (Sbridge1) of bridge 1 which is act as a

inverter. Simultaneously, for bridge 2, the present value of output voltage Vdc2(k) is

measured and the prediction of its future voltage Vdc2(k+1) is also generated for four

possible switching vector (Sbridge2) by using the equation (13). These future currents and

voltages are compared with the reference current (bridge 1) and voltage (bridge 2) by

utilizing the cost function equation (16) and (17). Finally, the switching states of bridge

1 and bridge 2, which minimizes the cost functions, are selected for the next interval.

Similarly, in boost mode of operation, the future value of input current i2(k+1)

for bridge 2 and output voltage Vdc1(k+1) for bridge 1 is predicted by using the discrete

time equation (15) and (14). Hence, the optimizing switching sates are selected for

firing the switches by using cost function equation (16) and (17).

The amount of power transfer by the DC-DC converter is controlled with the

reference currents and voltages of the inverter and rectifier bridges respectively for both

buck and boost mode. In buck mode, the power transferred to the low voltage DC bus is

222 dcdcdc IVP ×= (18)

where, Pdc2 is the transferred power by the converter. Vdc2 and Idc2 are the DC voltage

and DC current respectively at the low voltage DC bus.

Low DC bus voltage (Vdc2) is controlled precisely at the giving reference voltage (Vref)

for bridge 2, while an accurate value of current Idc2 is achieved by fixing an optimum

value of reference current (iref) for bridge 1.

Similarly in boost mode, power transfer (Pdc1) is also controlled with the

reference current (iref) of bridge 2 and voltage (Vref) of bridge 1 as:

111 dcdcdc IVP ×= (19)

where, Vdc1 and Idc1 are the DC voltage and DC current respectively at the high DC bus.

3-PhasePWM

Converter3 DC-AC

Bridge 1AC-DCBridge 2

Optimization of Cost Function

Predictive ModelVdc1(k)

Idc1(k)

i1(k) V1(k) V2(k) i2(k)

Idc2(k)

Vdc2(k)

Vdc1(k+1) i1(k+1) i2(k+1)Vdc2(k+1)

Sa Sb Sc Sd

refirefV

Vdc2(k)Vdc1(k)

AC Supply

Idc2(k)i1(k)

V1(k) V2(k)

4 kHz Transformer

Idc1(k)

Figure 5. Proposed control scheme of MPC algorithm for the bidirectional isolated DC-DC converter.

3.4. Control algorithm

The control algorithm of model predictive control (MPC) is presented in Figure 6. The

whole predictive control process completes the following steps for selecting the

optimized switching state of the converter in the next sampling interval (k+1).

• The control algorithm starts with measuring and sampling the high DC bus

voltage Vdc1(k), current Idc1(k); low DC bus voltage Vdc2(k), current Idc2(k);

transformer high side voltage V1(k), current i1(k) and transformer low side

voltage V2(k) and current i2(k) for the kth sampling period.

• Then the reference currents iref and reference voltages Vref for inverter and

rectifier are calculated and fixed up correspond with the amount of power flow.

• For buck mode of operation, predicted currents i1(k+1), predicted voltages

Vdc2(k+1) and for boost mode of operation predicted currents i2(k+1), predicted

voltages Vdc1(k+1) are determined for each one of four (j = 4, where j denotes

the possible switching states) possible switching states of the converter by

utilizing the discrete model of the system.

• Then the cost functions of inverter (gi) and rectifier (gr) is calculated with the

predicted and reference values of currents and voltages.

• Finally, the switching state associated with the minimum cost function is finally

selected for firing the converter in the next sampling time period (k+1).

Sampling of Measured Currents and Voltages

Calculation of Reference Currents and Voltages

Initialization of Cost Function, gopt

for j=1:4,

Prediction of Currents and Voltages

Calculation of Cost Functions, gi and gr

Minimization of Cost Functions, gi and gr

j ≥ 4 ?

Apply Selected Switching State

Switching State Selection

Figure 6. Proposed control algorithm of model predictive control (MPC).

4. Simulation Results Analysis

The proposed MPC algorithm is carried out by using MATLAB/Simulink to validate

the feasibility of the proposed control algorithm. To verify the proposed method in

bidirectional isolated high-frequency link DC-DC converter for energy conversion

system, both the buck and boost mode operations have been investigated for 2.34 kW

and 1.91 kW power transfer respectively. The parameters shown in Table I, are used for

simulation in both buck and boost modes. In addition, the high frequency transformer

model parameters are given in Table II. Sampling time for the inverter bridge (Tsi) and

rectifier bridge (Tsr) are taken as 10 µs and 250 µs respectively.

TABLE I

Simulation parameters

Parameter Symbol Value Sampling time (inverter bridge) Tsi 10 µs

Sampling time (rectifier bridge) Tsr 250 µs

Rated high DC bus voltage Vdc1 360 V

Rated low DC bus voltage Vdc2 60 V

Capacitance across high voltage DC bus Cdc1 7 mF

Capacitance across low voltage DC bus Cdc2 22 mF

Auxiliary inductance at the high side of transformer Lah 5 µH

Auxiliary inductance at the low side of transformer Lal 0.14 µH

IGBT internal resistance Ron 1 mΩ

Snubber capacitor across IGBTs Csh 10 nF

MOSFET on-state resistance RDS(on) 0.5 mΩ

Snubber resistance across MOSFET Rsl 1.67 Ω

Snubber capacitor across MOSFET Csl 141 nF

TABLE II

Simulation parameters of the 4 kHz transformer

Parameter Symbol Value Nominal Power P 6.5 kW

Nominal frequency fn 4 kHz

Turns ratio N 6:1

Winding 1 nominal voltage (RMS) V1 360 V

Winding 1 Resistance R1 34 mΩ

Winding 1 leakage inductance L1 8.1 µH

Winding 2 nominal voltage (RMS) v2 60 V

Winding 2 Resistance R2 0.94 mΩ

Winding 2 leakage inductance L2 0.225 µH

4.1. Buck Mode Operation

In buck mode of operation, bridge1 operates as a voltage source inverter with current

mode control, hence current is precisely controlled during the operation of bridge 1. On

the other hand, bridge 2 works as a current source rectifier with voltage mode control

fed by the power from bridge 1. In this case, voltage is controlled precisely to the low

side transformer voltage. Due to the current mode and voltage mode control of the

bridge 1 and bridge 2 during the buck operation, some expected behaviours appears in

the system model as explained in this section.

In current mode control, the relations of high frequency transformer primary

winding current (i1) with the secondary winding current (i2) and primary winding

voltage (V1) with the secondary winding voltage (V2) are presented in Figure 7. It

shows the value of primary winding current i1 = 10 A (peak) and secondary winding

current i2 = 60 A (peak), which exactly follow the high frequency transformer turn ratio

of 6:1. Again, in the same mode, the high side primary winding voltage V1 = 360 V and

low side secondary winding voltage is V2 = 59.60 V, where a slight amount of voltage

is dropped across the equivalence inductance (Leq), which is expected due to current

mode control of bridge 1.

A further important improvement in this investigation is the unity power factor

control compared to the previous study of DC-DC converter with phase-shift

modulation. In Figure 7, it shows the zero displacement angles between the primary

winding voltage (V1) and current (i1) and also appears to same zero phase-shift between

the secondary winding voltage (V2) and current (i2). As a result, reactive power is

minimized and unity power factor is controlled in the power conversion through the

high frequency (4 kHz) transformer.

In the voltage mode control, low DC bus voltage is 60 V and current has a value

of 39.0 A throughout the simulation time except the transient period, which is consistent

to expected values and are depicted in Figure 8.

Figure 7. Voltages and current waveforms for 2.34 kW power transfer at the buck mode

of operation; transformer (a) primary winding voltage, (b) secondary winding voltage,

(c) primary winding current, and (d) secondary winding current.

Figure 8. Waveforms of (a) high DC bus voltage Vdc1 and current Idc1 and (b) low DC

bus voltage Vdc2 and current Idc2, at buck mode operation for 2.34 kW power transfer.

4.2. Boost Mode Operation

In boost mode of operation, bridge 2 is working as a voltage source inverter with

current mode of control. Therefore, current is precisely controlled during the operation

of bridge 2. On the other hand, bridge 1 is operating as a current source rectifier with

voltage mode of control linked by the power from bridge 2 and voltage is controlled

precisely to the high side voltage of the transformer. Because of the current mode and

voltage mode control of the bridge 2 and bridge 1 during the boost operation, the

following behaviours appear in the system model described in this section.

In current mode control, the relations of high frequency transformer primary

winding current (i1) with the secondary winding current (i2) and primary winding

voltage (V1) with the secondary winding voltage (V2) are presented in Figure 9. The

Figure 9 shows that the primary winding current i1 = 75 A (peak) and secondary

winding current i2 = 12.5 A (peak), which exactly follows the high frequency

transformer turn ratio of 6:1. Again, in the same mode, the high side voltage V1 = 360 V

and low side voltage is V2 = 60 V.

In voltage mode control, high bus DC voltage follows the transformer secondary

values around the 360 V. Figure 10 shows the DC current at the values of -5.29 A (

negative sign denotes the opposite direction of power flow), in the whole simulation

time except the transient period.

Besides, similar to the buck operation, the boost operation also shows the unity

power factor control. In Figure 9, it shows the unity power factor between the primary

winding voltage (V1) and current (i1). It also maintains the same phenomena of zero

phase-shift between the secondary winding voltage (V2) and current (i2). As a result,

reactive power is minimized and unity power factor is maintained in the power

conversion through the high frequency (4 kHz) transformer.

Figure 9. Voltages and current waveforms for 2.1 kW power transfer at the buck mode

of operation; transformer (a) secondary winding voltage, (b) primary winding voltage,

(c) secondary winding current and (d) primary winding current.

Figure 10. Waveforms of (a) low DC bus voltage Vdc2 and current Idc2 and (b) high DC

bus voltage Vdc1 and current Idc1, at the boost mode operation for 1.6 kW power transfer.

cript Figure 11. Experimental system of bidirectional DC-DC converter with MPC control.

5. Experimental Verification and Efficiency Comparison

A 2.5 kW scaled down laboratory prototype of the bidirectional isolated DC-DC

converter has been developed. The schematic layout of the experimental setup is

presented in Fig. 11. The parameters taken for experiment are provided in Table I.

During the experimentation, a portable DC voltage source [TDK-Lamda] was used for

voltage supply. INTERNATIONAL RECTIFIER–IGBT, TO-247AC, 600V, 60A and

STMICROELECTRONICS-MOSFET, TO-264 were used as power devices.

The experimental verification of the proposed model predictive controlled

bidirectional isolated DC-DC converter is carried out by using the rapid prototyping and

real-time interface system dSPACE with DS1104 control card which consist of Texas

Instruments TMS320F240 sub processor and the Power PC 603e/250 MHz main

processor. This dSPACE control desk works together with Mathwork

MATLAB/Simulink R2013a real-time workshop and real-time interface (RTI) control

cards to implement the proposed predictive control algorithm to the bidirectional

isolated DC-DC converter for energy storage system.

5.1. Experimental Results

The voltage is measured with differential probe [PINTEK DP-25] and the current with

current transducer [LEM LA 25-NP]. The following sub-section presents the

experimental results for both the operating modes of the bidirectional isolated high-

frequency linked DC-DC converter.

5.1.1. Buck Mode

Figure 12 and 13 shows the waveforms of the transformer primary and

secondary winding voltages and currents at the buck mode of operation. It shows the

value of primary winding voltage V1 = 360 V and low side secondary winding voltage

is V2 = 60V. Again, the transformer primary winding current i1 = 10 A (peak) and

secondary winding current i2 = 60 A (peak), maintained the zero displacement angles

which ensures the minimization of reactive power.

In the voltage mode control, low DC bus output voltage is 60 V and current has

a value of 37.0 A throughout the experimental time except the transient period, which

ensures the stability of the proposed MPC method of bidirectional isolated DC-DC

converter and are depicted in Figure 14.

Figure 12. Waveforms of the transformer primary and secondary winding voltages.

Figure 13. Waveforms of the transformer primary and secondary winding currents at the

buck mode of operation.

Figure 14. Waveforms of low DC bus voltage (Vdc2) and current (Idc2), at buck mode.

5.1.1. Boost Mode

Figure 15 and 16 shows the waveforms of the transformer primary and

secondary winding voltages and currents at the boost mode of operation. It shows the

value of primary winding voltage V1 = 360 V and low side secondary winding voltage

is V2 = 60V. Again, the transformer primary winding current i1 = 12 A (peak) and

secondary winding current i2 = 72 A (peak), maintained the zero displacement angles

which ensures the minimization of reactive power.

Figure 15. Waveforms of transformer primary & secondary side voltages at boost mode.

Figure 16. Waveforms of the transformer primary and secondary winding currents at the

boost mode of operation.

Figure 17. Output waveforms of high DC bus voltage (Vdc1) and current (Idc1), at the

boost mode of operation.

In the voltage mode control, the high DC bus output voltage is 360 V and

current has a value of 5.0 A throughout the experimental time except the transient

period, which ensures the stability of the proposed MPC method of bidirectional

isolated DC-DC converter and are depicted in Figure 17.

Figure 18. Efficiency comparison between MPC and dual-phase-shift control method.

0 0.5 1 1.5 2 2.5

r Effi

Power transfer [kW]

MPC Algorithm

Dual-Phase-ShiftControl

5.2. Efficiency Comparison

The efficiency of MPC controlled bidirectional isolated DC-DC converter is measured

with FLUKE 1735 Power Logger. The accuracy of this power logger is ±0.2% of its full

scale. The efficiencies of the bidirectional DC-DC converter are measured in MPC

method against the power transfer ranges from 0.5 kW to 2.0 kW. In order to confirm

the effectiveness of MPC algorithm, the efficiencies of MPC controlled DC-DC

converter are compared with dual-phase-shift controlled DC-DC converter (Bai & Mi,

2008a), presented in Fig. 18. The dual-phase-shift PWM control method is applied in

the 2.0 kW bidirectional isolated high frequency linked DC-DC converter topology with

employing the same parameters and measurement techniques as in MPC algorithm.

Although, MPC algorithm has variable switching frequency problem, the efficiencies

associated with the MPC control are higher compared to the dual-phase-shift based

PWM control method due to the elimination of reactive power and minimized DC

voltage ripple. From Fig. 18, it is clear that the maximum efficiency is achieved at 1.60

kW power transfer for both control methods, where the converter efficiency using MPC

method is 92.52 %, while the efficiency is 89.56% using dual-phase-shift based PWM

control method.

6. Conclusions

Predictive control is a powerful control algorithm in the DC-DC power converters for

energy conversion system, which utilizes the discrete behaviour of the DC-DC

converter. The efficiency and performance of the proposed MPC controlled

bidirectional isolated DC-DC converter is simulated in Matlab/Simulink and further

validated with a 2.5 kW experimental configurations. The most important outcomes of

the proposed MPC algorithm in this investigation are zero phase shift between the

primary and secondary voltage of the transformer and unity power factor between the

primary voltage and primary current, also secondary voltage and secondary current,

hence minimizing the reactive power in the DC-DC converter. As a result, the

efficiency of the power improved up to 92.52%. The results associated with the

predictive algorithm in this investigation are very much encouraging and will continue

to play a strategic role in the improvement of modern high performance DC-DC power

converters in energy conversion system and will open a new prospect in the power

electronics research.

References

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