Synthesis and Implementation of a Multiport Dual Input-Dual Output Converter for Electric Vehicle Applications

Shaik, Reddi Khasim; Dhanamjayulu, C.

doi:https://doi.org/10.1155/2022/9279475

International Transactions on Electrical Energy Systems

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Abstract Introduction Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 9279475 | https://doi.org/10.1155/2022/9279475

Synthesis and Implementation of a Multiport Dual Input-Dual Output Converter for Electric Vehicle Applications

Reddi Khasim Shaik¹and C. Dhanamjayulu¹

Academic Editor: Qiuye Sun

Received12 May 2022

Revised02 Sept 2022

Accepted19 Sept 2022

Published05 Oct 2022

Abstract

In recent years, multiport DC-DC converters are seen in a variety of power converter applications in electric vehicles. The design of multiport converter architectures plays a major role in DC microgrids and electric vehicle applications. This research examines a modified multiport converter structure interface with dual inputs and dual outputs used in electric vehicles. The versatility of accommodating energy sources with varying voltage and current nature characteristics is the most notable feature of this converter. During operation, the proposed architecture can offer a boost as well as buck operations at the same time. The suggested dual input-dual output (DIDO) converter is built with fewer components and a simpler control technique which makes it more dependable and the converter is cost-effective. Furthermore, this structure allows the power to flow in both directions making it to be utilized in electric vehicle battery charging during regenerative braking. The converter’s steady-state and dynamic behavior are investigated, and a control strategy for regulating the power flow among the varied input energies is proposed. To develop the suggested converter, a small-signal model is modeled. MATLAB simulation and experimental findings are used for the verification of converter design and validated the performance behavior experimentally using a hardware setup.

1. Introduction

Electric vehicles (EVs) should be used instead of fossil-fuel cars because of the world’s fast-expanding population and energy demand, raising the costs of gas and oil and the reduction of fossil fuels. As a result, enthusiasm for producing EVs powered by clean and renewable energy sources to replace fossil-fuel vehicles has constantly grown. Electric vehicles (EVs) are a promising alternative for transportation-related applications that may enable environmental operations by utilizing renewable energy sources [1, 2]. The solar PV system is utilized as a clean energy source in the case of electric vehicles. Solar PV energy sources produce electrical energy utilizing solar energy. The maximum power has been extracted from the solar panel by using the technique of maximum power point tracking (MPPT) [3]. At present, the adoption of solar PV energy systems meets the required demands in electric vehicle applications. These provide energy to the required load and charge the battery as long as the energy is available. Hence, there exist many limitations of the solar PV system, such as the various irradiance levels causing the system less effective in power transfer to the load and the availability is not constant throughout the way. Hence, to overcome these limitations, the solar PV system along with a secondary source of battery is utilized to provide energy from source to load. The hybridized combination of energy storage systems provides a continuous power transfer to the load and meets the high-power requirements of the load during high speed and terrains [4–7]. The energy storage systems such as batteries and supercapacitors consist of different voltage ratings to interface them in the systems. Implementing the system with individual DC-DC converters for the respective source leads to the system being bulky and complex which makes the system costly. Hence, a converter along with various voltage-rated inputs is required to handle several energies and fed to the system. Hybrid energy storage systems are generally interfaced with a multi-input converter, which is of various variants based on the isolation requirement concerning the nonisolation and isolation type DC-DC converters. The general layout representing the multi-input converter-fed electric motor is represented in Figure 1.

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(b)

In an isolated multiport converter configuration, a transformer is employed with high frequency to produce isolation between the electric constraints. This provides efficient isolation and the matching of impedance among both sides of the converter. Leakage inductance is utilized as a storage device that stores energy in isolated converters to transmit the power among both sides of the converter. In addition to the high-frequency transformer, isolated dc-dc converters often comprise power converters. To maintain the efficient power transfer from both ports of the converter, the transformer phase shift among the primary and secondary voltage constraints has been changed [8–10]. These include various types of the converters such as half-bridge converters, full-bridge converters, and combinational multiport converters [11–13]. These are bulky because of the utilization of the transformer. The requirement of inverters in these converters is at the input port of the transformer with which the dc can be converted to ac supply and also the ac can be converter to dc by using the converters. Hence, there exist various switches utilized in all terminals of the converters increasing the losses with which the system losses get increased with the increase of transformer winding losses. These drawbacks are rectified in nonisolated multiport converters which are well-suited for electric vehicle applications.

A nonisolated multiport converter has been implemented concerning the H-bridge [14]. In practice, the various voltages can able to obtain by cascading H-bridge configurations by considering various dc-link voltage levels. The negative modes are not explored due to the coupling mechanism of the converter to the multiple ports [15]. The concept of a multiphase converter is first presented in [13, 16, 17]. The reduced order transfer function method is discussed in [18, 19] which is very advantageous with the higher order transfer function systems. By utilizing this method, the design of the controller becomes simple and the analysis gets simplified. The energy can be absorbed as well as delivered among the load from the energy storage sources of the converter. There exists an individual inductor for each source, and hence, this is considered a drawback of the converter. The author offers a three-input converter for the connection of batteries, solar cells, and fuel cells. With correct converter switching, you may charge and discharge the battery from various sources and loads. A systematic technique for designing nonisolated topologies is presented in [20] using a mix of buck type, boost type, sepic, and cuk type converters. Two types of converters are described such as PVSC and PCSC which are pulsing voltage and current sources, respectively. PVSC can be linked with the inductor in series to form a double-input converter as it represents a voltage source. Because the PCSC is a current source, it may be used to build a double-input converter by connecting it in series with a capacitor. In [21, 22], various heuristic dynamic programming techniques are proposed, and with the increase of the number of outputs to the load, the complexity of the system gets implemented. In this case, the adaptive dynamic programming approach is essential to simplify the analysis and to design an efficient control technique. The energy management strategy, among the various inputs such as battery, SC, and the electric motor, is suggested for use in an electric automobile. Here, instead of employing two separate inductors as energy, storage components are used. When compared to two independent inductors, it is claimed that employing the inductors with coupling can save 22–26% in volume. Connected inductors, on the contrary, have a larger volume than a single inductor. This converter also enables braking energy regeneration to the battery and SC [23] and proposes that load power be transferred across input sources using a multiport converter with a single inductor. This converter can also be used to transport power between sources. In [24], the author introduced a revolutionary single-stage multiport converter that is expandable. The layout of conventional converters utilized in electric vehicles is represented in Figure 1(a) whereas the proposed converter configuration is represented in Figure 1(b).

Handling the low torque ripple plays a vital role in electric cars. Torque ripple in ac motors is related to voltage harmonics, and these can be minimized with the utilization of multilevel inverters. To create multilayer voltage, multilevel inverters require dc excitations with equal or different values of voltages. Multiple dc-links can be generated using multiport converters. Naik et al. and Rekik et al. [24, 25] offer a multiport converter that may create numerous voltages at its output sections, and these are the preset values regardless of the variation of the load power and input voltage. Podder et al. [26] present a novel multi-output buck converter control technique that delivers suitable dynamic performance. However, this converter is worthless in applications such as electric cars, where various input energy excitations, such as solar and a battery, are utilized. This can be solved by incorporating multi-port converters. Babaei et al. and Chittathuru et al. [27, 28] present a nonisolated single inductor multi-port converter. This converter’s downside is that it employs a high switch count with low efficiency. The suggested converter’s inability to transmit energy across input sources is another flaw.

A novel multi-port nonisolated DIDO or D²-M² converter is suggested in this work, which is based on the mixture design of multiple inputs and outputs of the converter. In comparison to previous scenarios, the suggested converter contains fewer components. This converter can regulate the flow of electricity between sources and loads. Furthermore, the suggested converter includes many outputs, each of which can have a different voltage level. The suggested converter has benefits like a modular structure with fewer components and integration of several sources with various voltage-current characteristics in the input. In addition to the capabilities mentioned above, the proposed converter can produce output that is either more than the maximum input voltage or lower than the minimum input voltage (buck) (boost). The efficiency of the suggested converter is increased by lowering switching losses.

The following is a breakdown of the paper’s structure. The main idea for designing DIDO and its operation modes is described in Section 2. Section 3 presents the converter’s dynamic model utilizing small-signal analysis. In Sections 4 and 5, the suggested converter’s functions are validated using experimental data and a power budgeting control technique. Finally, Section 6 has the conclusion.

2. Performance Analysis of DIDO Converter

2.1. DIDO Converter Configuration

As the power requirement in the electric vehicle is varying concerning time, a single energy input cannot meet the requirement of the load. As a result, the combination of various energy sources is necessary. The goal of this study is to create a converter architecture that can connect diverse energy sources to a vehicle’s motor train. Figures 1(a) and 1(b) show the layout of power converter components interfaced in an electric vehicle. The following are some of the most notable characteristics of the suggested converter: Bidirectional power flow capabilities Power flow control among the sources to load Design and control should be simple

The adjustable switches S₁, S₂, and S₃ govern the power flow between the load and the input sources, as represented in Figure 2. The suggested structure can be examined in five operating states represented in Figures 3(a)–3(e). In the first state of operation, single input is provided to the circuit and dual outputs are at the load. The drive train of an electric vehicle (load) is powered by PV power shown in Figure 3(a). In the suggested topology, the battery can be charged either from PV power or from the load represented in Figures 3(b)–3(e).

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The energy from the load can be stored in the battery due to regenerative braking in condition 5. If solar PV cannot generate enough electricity due to low irradiation, the battery discharges to meet the total desired load shown in Figure 3(c). Both battery and PV give the required electricity for the drive train at peak power demand. The converter subsequently enters the DIDO mode of operation (see Figure 3(d)). Figures 4 and 5 show the proposed converter’s switching schemes as well as similar circuits in various operational states.

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2.2. Modes of Operation of the Proposed DIDO Converter

2.2.1. Mode 1: PV to Load

In this stage, the power from the solar PV energy source transfers to the load independently. The switching strategies of the controlled switches are represented in Table 1. Switches S₁ and S₃ are turned on and S₂ is switched off for 0 to d₁T_s time interval. When V_pv > V_bat, the voltage V_pv occurs across the inductor L₁, resulting in a positive slope rise in the inductor current. During the time interval d₁T_s to T_s, switches S₁, S₃, and S₂ are turned on and off represented in Figure 3(a) and 3(b). Energy stored in inductor L₁ during the previous period d₁T_s is discharged to the output capacitor through the diode D₁. T_s stands for the transition period. In steady-state operation, the output voltage is given by

2.2.2. Mode 2: PV to Battery and Load

This mode of operation is similar to state 1. S₂ operates with d₂ 0 : 5 to charge the battery when it is charged from solar PV represented in Figures 3(c) and 3(d). S₃ produces boosted output across the load in the same way as S₁ does, with d₁ > 0.5. The equations for the respective input and output port parameters are represented below:

2.2.3. Mode 3: Battery to Load

During this mode of operation, the battery discharges the stored energy and is fed to the load. The energy discharge and the variation of the inductor current are at 0 to d₃T_s time intervals. The negative slope arises between the intervals d₃T_s and T_s where the current I_L2 gets decreased. The switch S₃provides a boosted output to the drive train during its ON-OFF state process. Because S₁ does not participate in the energy transfer from battery to load and remains to be turned off during the operation represented in Figures 3(e) and 3(f), the load voltage discharging from the battery is given by

2.2.4. Mode 4: PV and Battery to Load

Whenever the requirement of energy is high to meet the desired load, the battery and PV systems provide enough energy to satisfy the demand represented in Figures 3(g) and 3(h). S₁ and S₃ switches can charge the inductor, and the inductor currents i_L1 and i_L2 get increased during 0 to d₁T_s time interval. S₂ is given with the complementing gate signal at this period. During the off period of switches S₁ and S₃, on the contrary, the currents i_L1 and i_L2 fall with a negative slope. Diodes D₁ and D₂ deliver power from both input sources to the load. The net output voltage is given by

2.2.5. Mode 5: Load to Battery

The battery gets charged from the energy from the power train during regenerative braking represented in Figures 3(i) and 3(j). The following is the switching for this state: S₁ is always turned off, S₃ and S₂ are turned on and off, respectively, and Q₂ is ON state, as well as S₃ charges the battery:

The battery is supplied via regenerative braking power in the same state. The control relationship is deduced as follows:

3. Dynamic Modelling

To manage the output voltages and the charging and discharging activities of the battery, a proper control system is necessary for each switch in the converter. Because of this, a small-signal model of the converter is needed to develop such controllers. The four-port converter’s state-space average model is constructed from the state-space description of the converter in each state of switching. Determining an average model is done as follows. The state-space equations are derived for each switching state of the switches. The steps involved in deducing the state-space model are using perturbation and matrix creation methods.

To create the small-signal model, the current flowing through the inductors and the voltage across the capacitors are treated as state variables. Many distinct models can be created because of the given converter’s operating states that use different combinations of the input sources to create either buck, boost, or buck-boost outputs at the same time. Element components (I, d, V) and perturbations are used to construct the state variables in equation (8) Perturbations are thought to fluctuate modestly during a single switching phase. Replace the state parameters with the sum of steady-state and perturbation values to create the following restrictions:

3.1. Mode 1: PV to Load

The switches in Figures 3(a) and 3(b) are used to indicate that the converter is in CCM mode. For a time of d₁T_s, S₁ is ON, S₂ is OFF, and S₁ is OFF, and S₂ is ON (1 − d₁T_s). It is feasible to obtain by substituting the perturbations:

3.2. Mode 2: PV to Battery and Load

S₁ and S₃ remain ON in this state, while S₂ remains OFF, as shown in Figure 3(c) and 3(d). The perturbations are incorporated as follows:

3.3. Mode 3: Battery to Load

The switch S₃ is on and S₂ is off in this state, and the inductor L₂ is charged. Figures 3(e) and 3(f) show that switch S₃ is turned off and switch S₂ is turned on, and inductor L₂ is drained. The following are examples of small-signal equations that are inferred:

3.4. Mode 4: PV and Battery to Load

State 4 combines the operations of states 1 and 3 represented in Figures 3(g) and 3(h), and the average converter model with the switching sequence shown in Table 1 can be obtained as

3.5. Mode 5: Load to Battery

Because the load power is negative, this state is referred to as regenerative. S₁ is turned off, and S₂ and S₃ are adjusted as illustrated in Figures 3(i) and 3(j) to store the regenerative braking energy in the battery. Because the output voltage is greater than V_pv during regenerative operation, the converter functions in buck mode and charges the battery. The small-signal model can be deduced by averaging the state equations over one period, as shown in the equation below:

In summary, the five operational states are explained, and the closed-loop analysis of the proposed DIDO converter is performed using the small-signal model of the transfer function (output voltage/duty cycle).

4. Control Strategy and Hardware Results

A solar panel of 100 W consisting of two monocrystalline 50 W PV panels is considered the primary source, and a 12 V, 7 Ah battery is considered the storage element. To determine the maximum power point, the solar panel is equipped with a traditional P&O MMPT controller. The developed converter is designed and implemented in hardware represented in Figure 11(e), and Figure 9 represents the various modes of operation for various states. Table 2 shows the experimental specifications. MOSFETs (IRFP250 N) switches are utilized in the developed topology. The switch and state selection control algorithms are shown in Figures 5(a) and 5(b). The flowchart of energy management is represented in Figure 6. The duty cycles of the MOSFETs S₁, S₂, and S₃ are generated using a basic PI controller; Figure 7 represents the duty cycles of S₁, S_2, and S₃. To determine the current references, the reference output voltages (V_{o_ref} and V_{01_ ref}) are compared to the real load output voltages (V₀ and V₀₁) and an error is generated (I_L1ref and I_L2ref). The PI controller’s k_p and k_i parameters were discovered using MATLAB. The reference signals of the S₁ and S₂ duty cycles are determined by the current error. The duty cycles of S₁ and S₂ are calculated by comparing the 10 kHz carrier signal with the reference signal. S₁ and S₃ are operated at the same time with the same duty cycle, whereas S₂ is operated in a complementary manner.

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4.1. Mode 1 and 2

The controller processes the feedback states and then delivers the duty cycles to operate the converter under a certain condition shown in Figure 10. If V_pv > V_bat, the controller evaluates V_pv and V_bat and selects states 1 and 2. Switch S₁ is triggered based on the PWM technique after state selection and the converter runs in SIDO mode. The output voltage V₀ and V₀₁ is 67 V.

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4.2. Mode 3

If V_pv and V_bat are set, mode 3 has opted and operates in the same way as state 1 with the battery as the primary power source. As indicated in Figure 11, the battery (where V_bat is 12 V and I_bat is 8 A) that was charged in the past cycle now drains to generate an increased output: V0 and V₀₁ are 56 V and, I_out is 1 A.

4.3. Mode 4

When V_pv and V_bat occur, the converter is in mode 4. The load is powered by the primary source (V_pv) and the battery to satisfy the predicted demand. Figure 11 shows the experimental results for this stage of operation. When V_pv is 20 V, I_pv is 4.8 A, V_bat is 20 V, I_bat is 4.8 A, and V₀ and V₀₁ are 36 V.

4.4. Mode 5

When the power exceeds the input power, the state changes in the process of regenerative braking. The motor drive oppositely provides energy. Due to the converter’s bucking activity, the energy from the brake is drawn and the battery gets charged. Figure 11 shows the charging parameters such as voltage and current: V_bat are 10.2 V and I_bat is 9.3 A when V₀ (brake) is 34 V and I₀ (brake) is 2.8 A.

The converter should have a higher degree of control freedom when used in an electric car. With this goal in mind, the converter is put through its paces with various duty cycles. Based on the results, it is clear that the suggested converter has a greater degree of control freedom and can give a higher output voltage.

For instance, in mode 1 represented in Figure 10, the input voltage is reduced from 22 to 20 V and then abruptly boosted to a value of 25 V greater than the supplied input voltage. The duty ratios d₁ and d₂ control buck and boosted voltages. Figure 11 illustrates the controller’s capacity to correct for a 9% fall in input voltage and a 14% rise in input voltage using the idea of line regulation. Figure 11 shows the controller’s load regulation performance against a 20% rise in the step-and-step drop-in R₀₁. This demonstrates that the planned controller can maintain outputs of 42.81 V and 42.67 V across both loads regardless of load or input variations.

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4.4.1. Battery SoC and SoH Measurement

The battery gets discharged to the load in modes 3 and 4 and the load supplies power to the battery in mode 5. The developed configuration is carried out experimentally along with a battery SOC of 45.1256% at the start. The SOC of the battery gradually decreases during state 3 and state 4 (battery discharges). At the end of stage four, the measured SOC is 45.0345%. Because the states operate for such a short time and are exercised at such a high-switching frequency interval, the change in SOC is quite modest. Similarly, the battery SOC has been raised from 45.0345 to 45.0526% during mode 5. The SOH values are also calculated as 80.001 to 8.002 from mode 1 to the conclusion of mode 5. The proposed DIDO converter is compared with the various existing topologies and presented in Table 3 whereas the graphical representation is presented in Figure 12. The simulation and experimental results for various irradiances are represented in Figures 8 and 9, respectively.

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5. Efficiency-Loss Analysis

Switching losses can be evaluated under various modes of operations using a standard buck, boost, or buck-boost converter considered as a reference. There are no additional losses because the developed converter acts as a standard boost converter in modes 3 and 4, and a buck converter in mode 5. As a result, the comparative change in efficiency for states 1 and 2 must be determined separately. The increased conduction loss owing to S₂ can be represented as in mode 1, whenever the converter acts as a general boost converter:

Similarly, in mode 2, the developed converter functions in buck mode, charging the battery from the solar PV energy source and interconnecting the load and PV in boost mode. As a result, a loss change may be calculated as

Hence, efficiency is calculated with the following equation:

Here, the above equation represents the efficiency of a traditional buck-boost type converter and 0 represents the efficiency of a developed buck-boost converter. The input and output power are denoted by the letters P_IN and P_O, respectively. State 1: during boost mode, State 2: during boost mode, During buck mode,

The proposed topology efficiency, passive elements, and switches are compared to other similar converter topologies in Table 3. The results show that the suggested converter’s efficiency is comparable to that of other converters. Furthermore, the suggested converter provides advantages such as a modular structure with fewer components and the integration of a variety of input sources with varying voltage-current characteristics. This converter can be used in a high-voltage electric vehicle (HEV) power circuit. The proposed converter, in particular, can be used in a regenerative mode.

6. Conclusion

This study aims to create a DIDO (dual input, dual output) converter that can be used to integrate a variety of power sources into electric vehicles. This converter, in comparison to existing buck-boost converter topologies in the literature, can produce buck, boost, and buck-boost output without the use of an additional transformer, and it can handle multiple resources of varying voltage and current capacity. To demonstrate the converter’s functionality, a mathematical analysis was carried out. A simple control technique was used to control the power flow between the input sources. Finally, the converter’s performance was evaluated using a low-voltage prototype model. The DIDO buck-boost topology that has been proposed has been successfully tested experimentally with a hardware setup.

Data Availability

No data were used to support the findings of the study.

Conflicts of Interest

The authors declare that there are no conflicts of interest on behalf of all authors.

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Copyright © 2022 Reddi Khasim Shaik and C. Dhanamjayulu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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