Predictive Active Power-Flow Control of Two-way Wireless Power Transfer System in V2G Services A. A. S. Mohamed, Alberto Berzoy and Osama Mohammed Energy Systems Research Laboratory, Florida International University
[email protected]. Abstract— This paper presents a new methodology of active power flow control for a bidirectional inductive wireless power transfer (BIWPT) system in electric vehicle (EV) ancillary services based on the system analytical model. The controller exists on the vehicle side to consider the EV’s owner’s desire for providing energy to the other sources. The owner is able to choose between three different control modes; Charge, Discharge and Abstain (no interaction). The controller considers the EV’s battery state-of-charge (SOC) which is provided by the battery management system (BMS). The control parameters are predicted based on a simple and an accurate analytical model for the BIWPT system. The misalignment effect on the controller performance is considered by adaptively estimating the wireless pads mutual inductance. The proposed controller is implemented and tested by means of simulations and experiments for stationary and quasi-dynamic wireless power transfer situations. Keywords— Active power flow control; Bidirectional; Electric vehicle (EV); Predictive; Vehicle-to-grid (V2G); Wireless Power Transfer (WPT).
I. INTRODUCTION EV charging based on inductive wireless power transfer (IWPT) technology has evolved into a state where it is considered as robust, efficient (about 85-93%) and reliable in harsh environments [1]. Numerous IWPT structures with different power electronics topologies, compensation techniques and control algorithms have been proposed and implemented for EVs applications [2]. Most of these configurations have been developed for unidirectional operation and they are not suitable for bidirectional applications, such as vehicle-to-grid (V2G) and grid-tovehicle (G2V) services. Power flow control algorithms are essential in these entities to control the magnitude and the direction of the power between the EV and the grid. Generally speaking, power flow control can be categorized based on the controller position (primary, secondary or dual) [3], the resonance high frequency (HF) inverter topology [4] and the modulation technique, such as pulse-width [5], [6], pulse Part of this work was supported by grants for the office of Naval Research. The authors are with the Energy System Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, 33174. (E-mail:
[email protected]).
978-1-5090-1546-7/16/$31.00 ©2016 IEEE
phase-shift [7], [8], pulse frequency modulation [7], [9], [10] and combination between them [7]. Different types of control theory such as classical proportional-integral (PI) [9], [10], proportional-integral-derivative (PID) [11] and fuzzy inference system [6], have been investigated to drive the above modulation variables. Classic PI and PID controllers suffer from large settling time, overshoots, and oscillations and may not resist the uncertainties and disturbances. Fuzzy controllers can be more robust however they present high computational processing and noisy outcome. In addition, the design of FLC system parameters shows tradeoff between complexity and accuracy [12]. Moreover, the design of these controllers need to be adaptive with variation of the system parameters which depend on the relative position of the wireless pads. Model-based active power flow (PAPF) controllers appear to be promising in these applications in terms of simplicity and accuracy. PAPF controllers for BIWPT systems, to the best of the authors’ knowledge, are not presented yet in literature. This paper presents a new strategy of active power flow controller for BIWPT systems in V2G services based on predictive theory. The proposed controller is located on the vehicle side. The EV’s owner desire and the battery SOC are considered in the algorithm to decide the mode of operation and the amount of power flow. Moreover, online mutual estimation is achieved to consider the wireless pad misalignment effect. The controller is developed based on an accurate mathematical model that was published in [13], [14] and extended in this paper.
Fig. 1: BIWPT system components.
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II. POWER FLOW ANALYTICAL MODEL. Many different configurations for BIWPT system can be found in the literature based on the power electronics inverter and the compensation topology. The most commonly used configuration is the one which is based on LCL resonant inverter, since it provides safe operation and it is less sensitive to the misalignment [15]. The BIWPT system under consideration is indicated in Fig. 1. It consists of two HF inverters with controllers, L-filters (Lpi, Lsi), compensating capacitors (Cp, Cs) and magnetic coupler (Lpc, Lsc) with mutual inductance (M). The power transfer between the primary and secondary sides is achieved by magnetic induction during resonance operation through a large air gap (100-250 mm) [16]. The proposed predictive controller is able to generate the phase modulation parameters (Į and ߚ represent the phase shift between the firing of the two legs of the primary and secondary inverter, respectively, and į is the phase shift between the primary and secondary inverter voltage). These parameters are adjusted to drive the magnitude and the direction of the power flow in the system as indicated in Fig. 2. This controller is mainly depends on the system mathematical model presented before in [13]. Following this model, an accurate active power flow estimation will be discussed in this section.
described in [13]. Then, substituting by their formulas in (1) for estimating the secondary side active power. ܲெ ൌ σୀଵǡଷǡହǡǥ ࣬ሺܸ௦̴ ܫ௦̴ כሻ
2) Fundamental Active Power Model (FAPM): In this model the harmonics components of Isi are neglected and a simple formula for the fundamental secondary current Isi_1 is given in (2). ܫ௦̴ଵ ൌ
ெఊ ఠೝ ೞ
where, ߛ ൌ
ೞ ೞ ோೞ ାఊఠೝ ெమ ோೞ మ మ
̴ܸଵ
ೞ ൫ೞ ାೞ ோೞ మ ൯ ೞ
ெఊ ್ ଼
ܲிெ ൌ ቀ మቁ ൦
, Vsi_1
ఈ
ఉ
ቀ ቁ ቀ ቁ ሺߜሻ ଶ ଶ ൪ (3) ೞ ோೞ ್ మ ଶ ఉ మ ቀ ቁ
ఠೝ ೞ
గ
ೞ ାೞ ோೞ
ଶ
3) Approximate Fundamental Active Power Model (AFAPM): In this case the second term in (2) and (3) is neglected, since it is proportional to the resistive losses which is relatively small with respect to the mutual coupling. The final approximated active power equation is described in (4). ଼
ெఊ ܸ ܸ ఠೝ ೞ ௗ PAPM
Ps (pu)
1.5
PFAPM
G2V mode
-0.5
-1.13 -1.15 -1.17
-1 -150
-100
PAFAPM
V2G mode
90
0
ஒ
ቀ ቁ ቀ ቁ ሺɁሻ൨ (4) ଶ ଶ
1.19 1 1.17 1.15 0.5
-1.5
1) Active Power Model (APM): It represents the total active power including the fundamental and the harmonics power components. It is obtained by formulating Vsi and Isi in terms of the system design and control parameters as it was
(2)
and Vpi_1 are the fundamental component of Vsi and Vpi, respectively. Therefore, an accurate formula for calculating the fundamental active secondary power is obtained by substituting (2) into (1) and considering only the fundamental component as described in (3).
గ
A. Active Power Flow Model. In [13], an approximate fundamental power flow model was presented, but this model is not accurate enough for the proper control design. Thus, in this paper three different mathematical models for the active power estimation with different levels of accuracy are presented and compared. Then, the appropriate one is chosen for the design of the proposed predictive controller.
ܸ௦̴ଵ
൫ ା ோ ோ ൯ሺೞ ାೞ ோೞ ோೞ ሻାఠೝమ ெమ ೞ ோ ோೞ
ܲிெ ൌ ቀ మቁ
Fig. 2: Phase shift control parameters including the deadtime.
(1)
-50
0 50 δ (degree)
-90 100
150
Fig. 3: Comparison between the three active power models (Į = ȕ = 120o).
B. Power Flow Models Assessment. The abovementioned three models are simulated, analyzed and compared in MatLab environment. The active power is estimated at different phase shift parameter (į) as shown in Fig. 3. It can be noticed that, the PFAPM is very close to the PAPM with 0.14% normalized mean square error (NMSE). On the other hand, the PAFAPM shows inaccurate performance with 2.32% NMSE. From the control design point of view the FAPM is the most appropriate model for the following reasons: it is more accurate than the AFAPM, and it is simple and does not need iterative solution unlike the APM. Thus, this model is
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considered for achieving the proposed predictive control design. By considering the fundamental power model, it can be noticed that the power flow capacity in V2G and G2V mode is different (1.19 and 1.15 pu, respectively). This is due to the second term in (3) which is fixed with respect to į. This term will be subtracted from the first one in case of G2V and added in case of V2G. Thus, this asymmetric operation is considered during the control design. C. Mutual Coupling Estimation. After deriving and verifying the analytical model, the next step for the predictive control operation is to solve this model. For solving the model, the system design parameters need to be known. The BIWPT system under consideration is designed for fixed resonance frequency operation in both the primary and secondary sides. Also, the resonance capacitors are tuned to resonate with the self-inductance of the coils which are known after the design and are relatively fixed [17]. Hence, the compensation capacitors are fixed as well. Under these constraints, only the mutual inductance varies based on the vehicle position and the misalignment between the wireless pads. Thus, this parameter needs to be adaptively estimated before applying the control algorithm. In this paper, a new online technique to estimate the mutual inductance has been proposed. It is based on a mix between analytical approach and measurements technique. A certain known condition for the primary control parameter (Į) is applied and the open circuit secondary voltage (Es) is measured. The primary coil current at open circuit in the secondary (Ipc_OCS) is calculated from (5). Then, the mutual inductance is estimated using (6). ܫ̴ைௌ ൌ െ̴݆ܸଵ Τൣ߱ ൫ܮ ܥ ܴ ܴ ൯൧ ܯൌ ܧ௦ Τ൫߱ ܫ̴ைௌ ൯
(5) (6)
III. THE PROPOSED PREDICTIVE CONTROL The proposed controller is located on the vehicle side to control the magnitude and the direction of the power flow between EV and the grid. The power flow control can be achieved based on the primary and/or the secondary inverters. In this paper, it is assumed that both inverters will be driven by the same phase modulation parameter (i.e Į=ߚ) to control the active power flow magnitudeǤ The third parameter į is assigned to ±90o, to shift between the different modes of operation (G2V when į=-90o and V2G when į=90o). Keeping į=±90o, will help to minimize the reactive power flow and achieve high power factor operation [18]. Based on the previous assumptions, the control parameters (Į and ߚ) will be predicted based on (7) and (8) which are deduced from (3). ߙ ൌ ߚ ൌ
ିଵ ቀͳ െ ൫ʹܲ Τܲ௫ ൯ቁ ଼
ெఊ ್
గ
ఠೝ ೞ
ܲ௫ ൌ േ ቀ మቁ
଼
ቀ మቁ గ
ೞ ோೞ మ
ೞ ାೞ ோೞ మ
(7) (8)
where, Pref is the desired active power which depends on many factors such as the EV owner desire, EV’s battery SOC and the grid power price [19]. The first term in (8) will be subtracted or added to the second term, which is fixed, based on the mode of operation. In G2V mode the first term is negative while for V2G mode it is positive. Thus, the maximum transferred active power will be less in G2V than V2G mode, as was indicated in Fig. 3. start Mode I
NO
interact?
end NO end YES end
YES YES
source available?
charge?
NO
YES
soc socmin?
NO
NO end
YES
soc socmax?
set į=-90
grid need?
YES end
NO o
o
set į=90 Estimate M, Pref and Pmax
set Į=ȕ=180o
NO
Pref Pmax?
YES calculate Į and ȕ from (7)
update Į, ȕ, į
Fig. 4: The proposed control algorithm.
The proposed algorithm considers the EV’s owner desire by giving the ability to choose between three different modes of operation: Mode I: Abstain which means no interaction, Mode II: Discharge, and Mode III: Charge. The sequence of the algorithm implementation linked with the available communication system is indicted in Fig. 4 and described as follows: - A vehicle enters the coverage area of the communication system and it receives a notification that there is a WPT service available in the area. - If the vehicle wants to interact, it will send a request for service (charge or discharge) to the grid side unit, otherwise it will ignore the notification. - The grid side unit checks its own resource availability. If it is capable to serve the new vehicle, it will accept the service request; otherwise, it will reject the service. - Once the grid approve its capability, the controller is going to check the EV battery SOC to avoid battery over and under charging. If the battery status is within the interaction range the controller will assign į and estimate the parameters M, Pref and Pmax, otherwise it will stop the service. - The controller checks the maximum available power of the charger Pmax. If Pref is within the charger limit, the control
3
V pi 10I
pi
VExp.
si
100 50 0 -50
I Sim.
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For the purpose of testing the proposed controller, a Simulink/MatLab model of BIWPT system (shown in Fig. 1) for EV applications was developed. Also, an experimental prototype for the same configuration was built. It consists of: (1) two 40 kHz LCL H-bridge resonance inverters to achieve the bidirectional power flow operation, (2) two identical circular wireless pads with ferrite bars, (3) a firing board to generate synchronized switching signals for the inverters, (4) a Li-ion battery to emulate the EV battery performance, (5) a programmable MAGNA power supply to emulate the DC bus, and (6) sensor board based on LEM voltage and current transducer and oscilloscope. The entire system setup is depicted in Fig. 5, and the design parameters are described in Table I. The simulated and experimental systems are analyzed and their results were compared for verification purposes. Then, the Simulink model was used to verify the effectiveness of the proposed controller.
V si 10I
IV. SIMULATION AND EXPERIMENTAL RESULTS
capacity by setting Į=ȕ=180o and į=-90o. Fig. 6 indicates the primary and secondary side variables (voltages and currents) during this mode of operation. The two inverters generate full square waves with 90o phase shift (the secondary lags the primary). This phase shift is responsible for directing the power flow from the DC link to the EV battery. Good correlation can be noticed between the experimental and the simulation results. A second scenario has been investigated using the same system in which the EV supports the power grid (discharging or V2G). This mode has been achieved by setting į=90o. The simulated and experimental system performance is described in Fig. 7. In this case, the secondary voltage leads the primary one to allow the power to flow from the battery to the DC bus.
pc
parameters Į and ߚ will be estimated based on (7), otherwise the maximum parameters will be used Į=ߚ=180o. - The controller will update the control parameters and send the new angle Į to the primary side.
0.04
0.05
0.06
0.07
0.08
0.09
50 0 -50
0.1 (d)
0.03
0.04
0.05 0.06 0.07 0.08 0.09 0.1 Time (msec) Fig. 6: Tested and simulated system performance under full supply voltages for G2V operation (Į=ȕ=180o, į=-90o) (a) primary inverter, (b) secondary inverter, (c) primary coil, and (d) secondary coil. VExp.
V pi 10I
pi
Figure 5: BIWPT system setup. TABLE I BIWPT SYSTEM DESIGN PARAMETERS
Lpi
26 μH
Lsi
25.5 μH
Cp
0.64 μF
Rpi
32 mȍ
Rsi
31 mȍ
Cs
0.66 μF
Lpc
25 μH
Lsc
24 μH
k
0.25
Rpc
30 mȍ
Rsc
29 mȍ
f
40 kHz
100 50 0 -50
50 0 -50
Ferrite Si Power Stranded copper MOSFET Wires I93/28/16 IXFB110N60P3 H07V-K
Capacitor Ceramic
Driver
FAN7391
Sensors
Battery Li-ion Polymer (PL-1055275-14STM) Air-gap
150 mm
A. Experimental Verification for BIWPT System Model. The simulated and experimental prototype were analyzed during the charging operation (G2V) and the results are shown in Fig. 6. The system was adjusted to supply the full
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Hall effect
I Sim.
(a)
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V sc 5I
Core
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si
Value
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sc
Value
V si 10I
Param.
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I Exp.
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Param.
VSim.
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0.03
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0.05 0.06 0.07 0.08 0.09 0.1 Time (msec) Fig. 7: Tested and simulated system performance under full supply voltages for V2G operation (Į=ȕ=180o, į=90o) (a) primary inverter, (b) secondary inverter, (c) primary coil, and (d) secondary coil.
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0.08
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o
Ps(W)
0.1
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0.16 (b)
actual 0.02
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reference
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50
Vsi(V)
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(c)
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(c)
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0.09
50 0 -50
0.1 (d)
0.03
0.04
0.05 0.06 0.07 Time (msec)
0.08
0.09
0.1
Fig. 8: Tested and simulated system performance under full supply voltages for G2V operation (Į=ȕ=90o, į=-90o) (a) primary inverter, (b) secondary inverter, (c) primary coil, and (d) secondary coil.
B. Simulation Results for the Proposed Control. After verifying the system model, the proposed PAPF control is implemented and tested in this section. The controller performance under Mode I and II are indicated in Fig. 9. The figure shows the transition from the “Abstain” to the “Discharge” mode at 0.04 sec. During Mode I, all the control parameters were set to zero and no power flow occurs. After applying Mode II (Discharge), į was adjusted to 90o to achieve V2G operation, and Į and ȕ were set to match with the desired power (Pref), as shown in Fig. 9(a) and (b). At 0.08 sec., Pref exceeds the power limits, thus the controller adjusts Į and ȕ to 180o to provide the maximum available power. Then, at 0.12 sec. the required power decreased and the control system follows the new value. During these transitions of power flow, the variation of Vsi can be noticed in Fig. 9(c) and (d). The performance of the proposed controller during the transition from Mode II to Mode III, is investigated in Fig. 10. In this test, the controller switches the system from
0.08 0.1 Time (sec)
0.12
0.04
50 0 -50 0.1201
0.0802
0.14
0.16 (d)
0.1203
Figure 9: Control performance during Mode I and II, (a) control parameters, (b) secondary power, (c) secondary voltage, and (d) zoomed secondary voltage. Mode II
Mode III
200 o
0.04
0.06 50 0 -50
0
100
Angle
pc
0.06
(a) 0.02
(b)
0.03 sc
0.05
β =α
0
Ps(W)
si
V si 10I
0.04
50 0 -50
Mode II
δ
100
I Sim.
(a)
0.03 V pc 5I
I Exp.
50 0 -50 0.03
V sc 5I
VSim.
Mode I 200
δ
0 -100 0.02 50 0 -50 -100 -150 0.02
Vsi(V)
V pi 10I
pi
VExp.
discharging (V2G) to charging (G2V) operation at 0.06 sec. The controller changes the angle į from 90o to -90o (see Fig. 10(a)). As a result of į variation, the power flow is reversed from positive to negative following the new reference. The system stays working in Mode III the rest of the time with different power level. The inverter secondary voltage variations are due to the control actions, are indicated in Fig. 10(c) and (d). During the entire period of operation, the controller succeeded to follow the reference value and the system was capable of transferring the required power. It shows fast transient response with accurate steady state performance.
Angle
The abovementioned two scenarios verify the capability of the model to achieve the bidirectional power flow operation (charge and discharge). Another test has been done to validate the system ability to change the magnitude of power flow. In this test, the system is charging the battery with less power level than the first scenario. The small power level is obtained by changing the phase shift between two inverters legs for both the primary and secondary inverters. The used control parameters in this case are Į=ȕ=90o, į=-90o, and the results are shown in Fig. 8. As can be observed, the two inverters generate quasi-square wave voltage with zero level appears. This zero level reduces the root mean square voltage and current value, and the power as well. In general, the results shows good agreement between the Simulink model and the experimental prototype.
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50 0 -50 0.09 0.1 0.11
0.1399 0.14 0.14
Figure 10: Control performance during Mode II and III, (a) control parameters, (b) secondary power, (c) secondary voltage, and (d) zoomed secondary voltage.
V. CONCLUSION A simple vehicle side predictive active power flow controller for BIWPT system in V2G applications is proposed in this paper. The controller is suitable for stationary and
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quasi-dynamic interaction between EV and the power grid. The mathematical model that is used to predict the control parameters is derived and verified. Three modes of control are provided for the EV’s owner to choose between them. The proposed controller considers the misalignment effect by adaptively estimating the system mutual inductance. A BIWPT system model driven by the proposed controller is built and analyzed by simulation and experimental tests. The controller shows accurate and fast performance during both the transient and steady state operation.
[11]
ACKNOWLEDGMENT
[14]
Part of the effort involved in this work was partially supported by scholarship supported from Cultural Affairs and Missions Sector, Egypt, for doctoral student; Mr. Ahmed A. S. Mohamed.
[15]
[12] [13]
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