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Scientific Reports volume 16, Article number: 9107 (2026)
1692
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This paper introduces an improved current-sensorless maximum power point tracking (MPPT) approach, coupled with a battery charging unit, specifically designed for single-phase standalone photovoltaic (PV) power systems. An interleaved hybrid DC-DC boost converter with high voltage gain, previously developed by the authors, is used to boost the low and non-linear voltage output of the PV array to the usable DC grid voltage level. Since the energy yield of PV systems is highly sensitive to variations in solar irradiance, fast and accurate tracking of the maximum power point (MPP) is essential. Unlike conventional MPPT techniques that rely on both voltage and current measurements, the proposed method estimates the input current using only the inductor voltage observed during the switch ON-state, thus removing the need for direct current sensing. This sensorless approach simplifies hardware design and reduces implementation costs, particularly in experimental environments where current sensors may introduce complexity and noise susceptibility. In addition, the proposed system includes a battery charging unit which ensures effective energy transfer to the battery in isolated operating conditions for single-phase AC off-grid power applications. The control structure regulates charging dynamics based on voltage behavior and operating constraints, contributing to stable performance under changing environmental conditions. The system’s effectiveness is verified through MATLAB/Simulink simulations under dynamic irradiance profiles. Predicted and actual current values are compared to validate estimation accuracy. Furthermore, experimental validation using a digital signal processor (DSP) demonstrates reliable real-time operation, confirming the practical applicability of the proposed method in cost-sensitive, off-grid solar energy systems.
The rapid increase in global energy demand, coupled with the urgent need to mitigate environmental degradation, has accelerated the shift towards clean and sustainable energy sources. While fossil fuels still play a significant role in energy production, their limited reserves and contribution to greenhouse gas emissions make them an unsustainable long-term solution. In this context, renewable energy sources—particularly solar, wind, and hydrogen—emerge as clean, inexhaustible, and environmentally friendly alternatives1,2,3. Among these, solar energy stands out as a key player in the development of sustainable energy systems due to its widespread availability, scalability, low operational costs, and minimal environmental impact4,5,6.
A PV-based single-phase standalone power system typically consists of PV arrays, a DC-DC converter for voltage regulation and MPPT, a battery bank energy storage system, and an inverter to supply AC loads. The DC-DC converter ensures that the PV array operates at its maximum power point, regardless of irradiance and temperature conditions. The AC output is provided through a single-phase inverter, which is often controlled to maintain voltage and frequency within standard limits (e.g., 230 V, 50 Hz)7,8,9. Despite the reliability and modularity of such systems, two major challenges persist: (i) maintaining energy harvesting efficiency under dynamic environmental conditions and (ii) ensuring safe and effective management of the energy storage unit. The former is typically addressed through MPPT algorithms, whereas the latter requires a battery management system (BMS).
The rapid advancements in PV panel manufacturing have propelled solar energy technology forward, making it a viable alternative to fossil fuels due to its clean, safe, and sustainable energy supply capabilities. This progress is evident in the dramatic increase in installed solar capacity, which has increased at least tenfold in the last decade10. However, current PV technology still faces significant challenges, such as a loss of up to 25% of generated energy due to inefficiencies and dependence on varying climatic conditions11. To address this, the MPPT technique plays a crucial role in optimizing energy production from PV arrays. MPPT techniques aim to continuously match the PV panel’s output load by controlling a DC voltage converter, ensuring maximum power transfer under varying environmental conditions11,12,13. These methods typically rely on real-time monitoring of voltage and/or current generated by the PV modules. Using a controller device and an algorithm, the MPPT system dynamically adjusts the duty cycle or switching frequency of the converter to maintain the optimal power output14,15,16. In doing so, MPPT ensures that PV systems generate the highest possible energy output, enhancing overall power transfer efficiency.
The MPPT technique, which is crucial for extracting maximum energy from PV systems, has been extensively studied in literature with various algorithms. These include Incremental Conductance (IC), Perturb and Observe (P&O), Artificial Neural Networks, Fuzzy Logic Controllers and adaptive approaches such as FOCV algorithm, metaheuristic algorithms9,17,18,19,20,21,22,23,24,25,26,27. Among these techniques, the P&O method is preferred due to its simplicity, ease of implementation, and relatively low computational cost9,14. However, under rapidly changing weather conditions, its performance may degrade due to oscillations around the maximum power point. All MPPT methods require accurate voltage and current measurements from the PV array; however, traditional sensing circuits can increase system costs and introduce susceptibility to noise15,18,20. This is particularly problematic in low-power, standalone systems. Despite their varying levels of complexity, these methods aim to optimize energy extraction from PV arrays while minimizing system costs and enhancing efficiency.
In traditional MPPT implementations, current and voltage sensors are employed to determine the instantaneous power output of the PV array. However, these sensors not only add to the overall system cost but also introduce complexity in signal conditioning and noise filtering—especially in harsh outdoor environments20,21. To mitigate these issues, sensorless MPPT techniques have emerged as a promising alternative22,23. These techniques rely on estimation algorithms to infer the PV current or voltage using known electrical relationships within the power converter topology.
While most sensorless methods in the literature focus on eliminating current sensors, voltage sensors are typically retained due to the difficulty of accurately modeling PV voltage behavior under dynamic load and irradiance changes. However, there remains a research gap in developing low-cost MPPT methods that also minimize voltage sensing requirements, particularly for embedded systems with limited computational resources22,23. In addition, some recent studies have focused on improving the performance of photovoltaic MPPT algorithms under dynamic environmental conditions while reducing system complexity and sensor dependency28,29,30. These advanced and adaptive MPPT techniques have been proposed to enhance tracking efficiency, accelerate convergence speed, and mitigate steady-state oscillations compared to conventional methods. These approaches emphasize robustness against irradiance and temperature variations, as well as practical feasibility for real-time implementation. Experimental and simulation-based validations consistently demonstrate improved dynamic response and stable operation, highlighting the effectiveness of adaptive and sensor-reduced control strategies in modern PV energy conversion systems. Table 1 presents a qualitative comparison of the proposed MPPT method with representative conventional P&O, sensor-based, and some sensorless type MPPT approaches reported in the literature. The comparison is conducted in terms of required sensing hardware, algorithmic complexity, robustness, and practical feasibility, which are some key criteria for real-world photovoltaic applications. Conventional P&O methods are characterized by their simplicity and widespread adoption but remain sensitive to measurement noise due to their reliance on current sensing. Sensor-based MPPT techniques generally achieve improved robustness and tracking performance at the expense of increased hardware complexity and cost. Recent sensorless approaches reduce sensing requirements; however, many rely on computationally intensive observers or estimation algorithms, which may limit their practical deployment. In contrast, the proposed method offers a balanced trade-off by minimizing sensor dependency and implementation complexity while maintaining robustness and ease of integration into low-cost embedded PV systems.
The role of boost type DC-DC power electronic converters is extremely important in on-grid or off-grid PV systems. A boost converter operating in continuous conduction mode (CCM) steps up the low and non-linear voltage generated by the PV array to a higher, more usable level at its output. This is particularly critical in stand-alone and off-grid systems where the PV voltage is often well below the required load voltage. Boost type DC-DC converters are used not only to regulate the DC voltage required for the inverter used in PV systems but also to enable the implementation of MPPT algorithms. Among the different boost type DC-DC topologies, the classical single-switch DC-DC boost circuit is one of the most widely used due to its cost-effectiveness31,32. However, in cases where high voltage gain is required, the classical single-switch boost circuit is insufficient33,34. In this case, interleaved and hybrid interleaved boost circuits with high voltage gain have been developed. Interleaved boost converters offer significant advantages over classical boost circuits, especially in high-power and wide-output PV systems. Thanks to developing control systems and power electronic components, the use of this topology in PV systems is increasing.
In standalone PV systems, batteries serve as critical components for maintaining energy supply during periods of low or no solar irradiance. However, battery performance is highly sensitive to charging and discharging profiles, ambient temperature, aging, and operational stress. To ensure safety, reliability, and longevity, a BMS is employed to monitor and control key battery parameters, including voltage, current, temperature, rates of charge/discharge and state-of-charge (SOC)17,18. Advanced BMS architectures incorporate several layers of protection: overvoltage and undervoltage cutoff, overcurrent protection, thermal management, cell balancing, and predictive maintenance algorithms. Additionally, in PV-powered systems, the BMS often plays a role in coordinating with the MPPT controller to optimize energy flow between the PV source, the battery, and the load. Improper battery charging can lead to reduced capacity, shortened lifecycle, or in severe cases, thermal runaway and safety hazards19,35. Recent literature also emphasizes the integration of smart battery management with renewable energy forecasting and adaptive control. Techniques such as fuzzy logic, neural networks, and model predictive control have been explored to improve the responsiveness and intelligence of battery management in PV-based systems36,37.
The Full Bridge Isolated DC-DC converter is a crucial component in BMS, offering a robust and efficient solution for power conversion in energy storage systems. By providing electrical isolation between the input and output stages, it ensures optimal power transfer and protection for the battery-powered devices. Its high efficiency and ability to manage bidirectional power flow make it an ideal choice for applications that require reliable voltage and current regulation during charging/discharging. These isolated topologies play a vital role in safeguarding the system, ensuring the battery operates within its safe limits and enhancing the overall performance and longevity of energy storage systems.
This study presents an improved current-sensorless MPPT control strategy, integrated with a battery charger specifically designed for single-phase off-grid PV systems. The key contribution of this study lies in the development of a simplified sensorless MPPT approach which, in contrast to many existing methods that depend on complex observers, optimization routines, or computationally intensive algorithms, employs a lightweight current estimation strategy integrated with conventional control logic. This design significantly reduces implementation complexity while enhancing robustness against sensor noise and sensor-related failures. Moreover, the proposed framework facilitates the seamless integration of MPPT and battery management functions within a single microcontroller, making it particularly suitable for low-cost embedded photovoltaic systems. The subsequent sections of the paper introduce the proposed current-sensorless MPPT method, which is based on an interleaved hybrid DC-DC boost converter with a high voltage gain, a technique previously developed by the authors. Additionally, a BMS utilizing an isolated full-bridge DC-DC converter is also introduced. The simulation results of the entire proposed system are then analyzed using the MATLAB/Simulink environment. Lastly, experimental results, controlled via a DSP, are presented, and related comparisons are provided.
MPPT control algorithms are employed to identify the optimal operating point at which PV panels can extract the maximum possible power under varying solar irradiance conditions. These algorithms enable PV systems to maximize energy generation despite fluctuations in environmental conditions38. In conventional MPPT methods, both the input current and voltage of the PV system must be measured and sampled to accurately determine the point of maximum power. However, in this study, only the input voltage (PV voltage) is measured, while the input current (PV current) is neither measured nor sampled. By eliminating the need for current sensing, the associated circuit components required for current measurement are removed from the implementation, resulting in a reduction in the overall system cost. The proposed improved current-sensorless P&O MPPT control method, developed for a grid-unconnected PV system, is illustrated in Fig. 1.
The proposed current-sensorless P&O MPPT method by using hybrid interleaved DC-DC boost converter.
CCM current waveforms of the hybrid interleaved DC-DC boost converter.
Since the classical and interleaved-type DC-DC boost converter circuits cannot have high voltage gain, they are exposed to high current stresses while producing the high DC bus voltage required for inverters in PV systems. This problem can be solved by an improved hybrid interleaved DC-DC boost circuit developed by the authors39as given in Fig. 1. The average circuit model of the proposed hybrid interleaved DC-DC circuit used for MPPT is obtained from the equivalent circuits depending on the switching states of the converter. The voltage gain and related average-model equations of the 180° phase shifted hybrid interleaved DC-DC boost with the current waveform in CCM mode as shown in Fig. 2 are given in Eqs. (1), (2) and (3)39.
where, L1 = L2=2 L; Cb3=Cb4=2 C; VCb3=VCb4=VDC/2; IPV=IL1+IL2.
As stated before, a modified version of the conventional P&O MPPT algorithm was implemented to determine the maximum power point for an off-grid PV system in this study. The P&O algorithm operates by scanning the power-voltage (P-V) curve and adjusting the operating point accordingly to converge on the maximum power point. As seen from Fig. 3, the voltage is incrementally perturbed: if a positive change in power is observed, the voltage is increased from point A toward the MPP; conversely, if a negative power change occurs, the voltage is decreased from point B toward the MPP. When the change in power approaches zero, the system is considered to have reached the MPP40,41.
P-V curve of the classical proposed P&O method.
The corresponding flowchart outlining the operation of the improved current-sensorless P&O MPPT algorithm is provided in Fig. 4. In the conventional P&O MPPT method, both the voltage and current of the PV system must be measured. Consequently, the output voltage and output current of the PV array are sampled and transferred to the digital controller, such as a DSP. However, employing both voltage and current sensors increases the overall system cost and complexity. Therefore, reducing the number of measured signals is crucial for a cost-effective implementation. When utilizing digital controllers like DSPs, it is feasible to eliminate the need for either current or voltage sampling. In the improved MPPT method presented in Fig. 4, the current sensing circuit is eliminated by leveraging the known values of the passive components used in the designed converter. Instead of directly measuring the current, the output current of the PV system (IPV) is estimated through Eqs. (4) and (5), by using inductor value and the switching period generated by the DSP. Once the output current of the PV system is computed, the instantaneous output power of the PV array is determined using the measured voltage and the estimated current. In the proposed MPPT algorithm, a reference voltage (Vref) is generated to adjust the duty cycle (d) accordingly. This reference voltage dynamically varies based on the comparison between the instantaneous and previously calculated PV output power. Moreover, maintaining a stable DC bus voltage is critical for the operation of the DC-DC converter interfacing the PV array with the load. The generated reference voltage is therefore used as an input to a proportional-integral (PI) controller for regulating the DC bus voltage to the desired level.
The flow chart of the proposed current-sensorless P&O MPPT method.
The proposed current-sensorless MPPT method is implemented in a PV-battery hybrid power supply system designed for stand-alone AC loads, as illustrated in Fig. 5. In this system, the battery group functions as a secondary power source, acting as a charge/discharge unit to balance power flow within the hybrid configuration. A hybrid interleaved DC-DC boost converter is employed for MPPT implementation, while a classical isolated full-bridge bidirectional DC-DC converter is utilized to integrate the battery group into the DC bus. The DC electrical energy obtained from the PV panels or stored in the batteries can be directly used for DC applications. Alternatively, it can be converted to AC using a suitable DC-AC inverter for powering AC loads.
Bidirectional converters are essential for both storing electrical energy in batteries and utilizing the stored energy when needed. These converters are generally categorized as either isolated or non-isolated. Non-isolated bidirectional converters are typically preferred in low and medium voltage bus applications, whereas isolated ones are favored for higher voltage applications due to their safety benefits. The use of transformers in isolated converters provides galvanic isolation between the battery and the main circuit components, ensuring both equipment and user safety. Bidirectional converters employing high-frequency transformers operate in two distinct modes: buck mode for charging the batteries, and boost mode for discharging them. Unlike low-frequency transformers, which increase in physical size with power rating, high-frequency switching in the isolated full-bridge bidirectional DC-DC converter allows the use of smaller transformers.
Overall schematic of the proposed PV based standalone system.
The designed bidirectional converter operates within a power range of 100–800 W, with high-voltage side outputs ranging from 280 to 400 V, and low-voltage side outputs 48 V. In this topology, switching elements connected to the primary winding of the transformer generate an AC square wave, which is then rectified by output diodes. The rectified voltage is filtered using an output inductor (Lbd1) and battery capacitor (Cbd). The ideal relationship between input and output voltages for the isolated full-bridge bidirectional DC-DC converter, as shown in Figs. 5 and 9, is given by Eq. (6), which involves the primary winding (Np), secondary winding (Ns), and the duty cycle of the full-bridge converter (dfb).
For buck mode operation, switches Qbd1-Qbd3 and Qbd2-Qbd4 must be switched with a 180° phase shift and a maximum duty cycle of 50%. Meanwhile, Qbd5 and Qbd6 should remain off (cut-off state), and the diodes connected in parallel should conduct or block current based on the polarity of the voltage from the transformer. Under these conditions, the 400 V Vbd voltage is stepped down to 48 V (Vbattery), thereby charging the battery group. Conversely, for boost mode operation, Qbd5 and Qbd6 are switched with a 180° phase difference and a duty cycle greater than 50%. At the same time, Qbd1-Qbd3 and Qbd2-Qbd4 are turned off, and the parallel-connected diodes conduct or block based on the transformer’s output voltage polarity. In this mode, the energy stored in the batteries is delivered to the system.
In hybrid PV–battery standalone systems, real-time power management is critical to ensure system stability, efficient energy utilization, and battery health. The proposed system incorporates a BMS that dynamically adjusts its charging or discharging behavior based on the instantaneous power flow between the PV array, load, and battery bank. In the proposed study, the BMS operates by referencing both the instantaneous power demand of the load and the maximum available power from the PV system. Based on the power difference between the PV system and the load, the BMS determines the appropriate charging or discharging mode of the battery bank. This decision mechanism directly influences the operational mode boost or buck of the isolated full-bridge DC-DC converter integrated within the BMS. BMS operates using two reference signals: the maximum available power from the PV system, PPV, obtained via the MPPT algorithm, and the load power demand, PL. The power balance condition for the system can be expressed as:
where, Pbd is positive when discharging and negative when charging.
Meanwhile, the hybrid interleaved boost DC-DC converter interfacing the PV source is actively controlled to extract maximum power through MPPT and to regulate the DC bus voltage. The primary function of the system controller is to maintain power balance across the entire hybrid energy system, enforce automatic battery charge/discharge management, and continuously operate the PV array at its maximum power point. For instance, if the load power (PL) exceeds the maximum power output of the PV array, the BMS enables the battery bank to compensate for the power deficit. In this scenario, the isolated full-bridge converter transitions to boost mode, and the battery discharges energy into the system. Conversely, when the load demand is lower than the PV system’s available power, the PV array alone supplies the load. The surplus energy is diverted to charge the battery bank, during which the converter operates in buck mode.
Six distinct operational scenarios given belove have been defined to illustrate the dynamic behavior of the BMS under different power conditions. These scenarios capture all possible interactions between the PV array, the battery bank and the load.
Scenario-1 If the power produced by the PV Panel system is greater than the sum of the load and battery power; the power required by the load is met by the PV system, and if the battery is not fully charged, the excess power produced by the PV system is transferred to the battery. The system is operated by PI1, PI2, PI3, and MPPT controllers.
Scenario-2 PV greater than battery, PV panel charges the battery. The MPPT controller operates PV panel to harvest the maximum available solar energy. PI1, PI3, and MPPT controllers are manage the power flow.
Scenario-3 PV Maximum Power feed the load, the load demand matches exactly with the PV’s maximum power output. In this condition, the entire load is supplied by the PV array operating at the MPP. PI1, PI2, and MPPT controllers are active.
Scenario-4 PV panel generates insufficient power for the load requires. PV and battery combined to meet the energy demands for load. PI1, PI2, PI4, and MPPT controllers work together to feed the load.
Scenario-5 Zero PV Output, Load Exceeds Battery Discharge Limit; in low-irradiance conditions (e.g., at night), the PV output approaches zero, and the load demand exceeds the battery’s maximum discharge capacity. The system must either shut down or perform load shedding to prevent over-discharge and protect the battery. PI2, and PI3 controllers provide enough power for the load.
Scenario-6 Zero PV Output, Load and battery; The system is not operated. All controllers are passive.
These six scenarios form the foundation for the real-time decision-making logic of the proposed power management strategy, ensuring optimal energy distribution, safe battery operation, and maximum utilization of solar energy under diverse operating conditions. A concise overview of the various operating conditions described above is provided in Table 2, which categorizes the system’s behavior under different power generation and load scenarios. Furthermore, the logical sequence of the proposed power management and control strategy is illustrated through the flow diagram shown in Fig. 6, providing a clear visualization of the decision-making process employed by the digital controller.
Flow diagram of the proposed BMS.
In this section, the implementation and simulation of the proposed current-sensorless MPPT control method and BMS are conducted using the MATLAB/Simulink platform. Initially, a hybrid interleaved boost-type DC-DC converter was modeled according to the design parameters39. The current-sensorless MPPT control strategy was then integrated into this converter structure within the simulation environment. To estimate the PV system output current without employing a physical current sensor, a dedicated computational block was developed in Simulink. This block calculates the current by sampling the input voltage of the boost converter, which is equivalent to the output voltage of the PV array. The calculation relies on the known inductance value (L), the duty cycle (d) generated by the DSP, and the sampled voltage. These parameters are used in conjunction with (4) to estimate the PV current. During each switching cycle, the inductor current exhibits a ripple that varies depending on the switching state. When the switch is conducting (ON state), the current ramps up; when the switch is open (OFF state), it ramps down. The PV output current corresponds to the average of the inductor current over a full switching period. Therefore, the simulation block computes the average of the peak and valley current values in each cycle to represent the PV output current accurately. This estimation process is executed continuously during the simulation, allowing the MPPT algorithm to operate effectively without requiring real-time current measurements. The recalculated average current is updated each switching period, enabling the controller to respond dynamically to changing irradiance and load conditions.
The hybrid interleaved boost-type DC-DC converter, powered by the PV system, was evaluated under the proposed current-sensorless MPPT control strategy through MATLAB/Simulink simulations. The system was tested at an ambient temperature of 25 °C under 400 W resistive load and varying irradiance levels of 800, 1000, and 900 W/m². The proposed MPPT algorithm, based on the P&O technique, operates without direct current measurement. Instead, it estimates the PV output current and generates a reference voltage, which is then utilized by a PI controller to regulate the DC bus voltage. This control scheme ensures that the PV array consistently operates at its maximum power point, while the DC bus voltage is maintained within the desired limits under all tested irradiance conditions. As illustrated in Figs. 7 and 8a, the system successfully tracked the MPP across all irradiance scenarios while supplying the load. Although the current input and PV voltage varied with irradiance changes, the output voltage remained virtually constant, validating the effectiveness of the PI controller in maintaining voltage regulation.
The simulation results of power, voltage and current for input and output under variable irradiances.
To validate the performance of the proposed current-sensorless MPPT method, both the measured and estimated output current values of the PV system were analyzed. The comparative results are illustrated in Fig. 8a. As expected, the measured current exhibits an analog waveform due to the continuous nature of physical sensing, while the estimated current—derived through the algorithmic computation within the DSP—presents a discrete or digital profile. Although the two current signals are not perfectly time-synchronized due to sampling and processing delays, the comparison demonstrates a high degree of agreement. The deviation between the measured and estimated PV output currents remains within an acceptable range of approximately 1% to 3%, confirming the reliability and accuracy of the proposed estimation-based MPPT strategy under real-time operating conditions. Steady-state oscillations are directly related to the amplitude of power fluctuations induced by the MPPT algorithm in the vicinity of the maximum power point. As observed in the presented results (Fig. 8b), both the PV power and the load power exhibit bounded and periodic oscillations around the MPP, without any divergence or amplification that could jeopardize system stability. Quantitatively, the steady-state ripple of (:{P}_{PV})is limited to approximately 4–5 W peak-to-peak, corresponding to about ± 0.5% of the 500 W power. In contrast, the oscillation amplitude of the load power further reduced to approximately 2–3 W, i.e., ± 0.3–0.4%, indicating effective attenuation of MPPT-induced fluctuations by the power conversion stage. This reduction in load-side power ripple demonstrates the algorithm’s ability to suppress oscillations and minimize steady-state power losses. In the existing literature, such low-amplitude steady-state oscillations are widely recognized as a hallmark of a well-tuned MPPT scheme, reflecting an appropriate trade-off between fast tracking dynamics and stable steady-state operation. Overcurrent, overvoltage, and SOC limits were determined in simulation and experimental studies.
It is worth noting that in systems utilizing MPPT, power is delivered to the load in accordance with the load’s instantaneous demand. Consequently, the operating point of the PV array and hence the MPP may shift with changing irradiance or load conditions. Despite these variations, the proposed method maintained accurate MPPT performance under both continuous conduction mode and discontinuous conduction mode. Moreover, the method correctly predicted system behavior in open-circuit scenarios where irradiance was present but no load was connected. In such cases, no current was generated, as expected. Conversely, whenever a load was connected under irradiance, current was successfully produced, and the control system responded accordingly. These results confirm that the proposed MPPT approach has accurate performance across diverse operating conditions.
The simulation results for, (a) IPV and the predicted IPV, (b) PPV-PL.
The entire PV based standalone system by using proposed current-sensorless P&O MPPT method and BMS.
Following the validation of the proposed current-sensorless MPPT method using the hybrid interleaved boost DC-DC converter, comprehensive simulation studies were conducted by modeling the entire power conversion system, as depicted in Fig. 9, within the MATLAB/Simulink environment. This full-system simulation enabled the evaluation of the integrated operation of the PV array, battery management system, power converters, and digital control algorithms under various operating conditions. The nominal parameters of the key system components used in the MATLAB/Simulink-based simulation model are summarized in Table 3. The selection of the parameters for the converters and the determination of the coefficients for the controllers are obtained through detailed design studies. These design studies are described in detail in references41,42. The values given in Table 3 were also calculated based on the design studies in the references given above. Here, Ziegler–Nichols tuning method was used to determine all PI controller coefficients. In this method, integral and derivative gains are initially set to zero and the proportional gain is increased until sustained oscillations are observed. The corresponding ultimate gain (Ku) and oscillation period (Tu) are then used to calculate the proportional and integral parameters42.
During the simulation studies, system behavior was analyzed under various operating scenarios defined for the BMS. The DC bus voltage controller was configured to regulate the voltage within the predefined limits of 380 V to 420 V. Additionally, the inverter and filter components for the single-phase AC load were designed to deliver an output voltage of 220 V rms. Since the DC bus voltage and the AC output voltage remained unaffected by changes in BMS operating modes, their corresponding simulation results are presented for a representative operating condition only. As an example, Scenario-1 represents a condition in which the PV array generates sufficient power to meet both the demand of a single-phase AC load and to charge the battery bank, assuming the battery is not yet fully charged. Under standard test conditions—specifically, an irradiance of 1000 W/m² and an ambient temperature of 25 °C—the PV panels successfully delivered the necessary power to supply a 400 W AC load at 220 V rms, while concurrently charging the battery bank at a current of 4 A with an initial SOC of 10%. The key performance indicators under this scenario, including PV array voltage, current, and power; battery charging parameters; and inverter output characteristics are depicted in Figs. 10 and 11, which demonstrates the effective coordination of the MPPT controller and the battery management system within the proposed hybrid power architecture.
The simulation results of power, voltage and current under 400 W output load; (a) for PV system, (b) for battery, (c) for output.
In the proposed system, when operating under Scenario-1, the BMS transitions into the charging mode. In this state, switch S4 is turned OFF, while the remaining switches are conducting. The PI controller block associated with the hybrid interleaved boost converter remains inactive during this operation. Under these conditions, the isolated full-bridge bidirectional DC-DC converter operates in buck mode to facilitate energy transfer from the PV array to the battery bank. Specifically, the Qbd5 and Qbd6 switches remain OFF, and current conduction occurs through the corresponding body diodes Dbd5 and Dbd6, enabling battery charging. Meanwhile, the Qbd1–Qbd4 switches are actively switched to regulate the charging process, and their associated freewheeling diodes Dbd1–Dbd4 protect the circuit against reverse current flow. Additionally, to ensure proper inverter operation and prevent reverse current damage, all inverter switches (Qi1–Qi4) and their corresponding diodes (Di1–Di4) remain active. This coordinated switching configuration enables safe and efficient power flow from the PV array to both the AC load and the battery bank during Scenario-I operation.
The simulation results under 400 W output load; (a) VDC, IDC, (b) IDC, IL and Ibd, (c) Vbd, Ibd, (d) VDC, IL.
In the proposed system, the DC bus voltage, which corresponds to the output voltage of the hybrid interleaved boost converter, must be equal to both the input voltage of the isolated full-bridge bidirectional DC-DC converter and the input voltage of the single-phase inverter. This common DC link voltage is regulated within the range of 380–420 V DC, a condition achieved by the implementation of the proposed MPPT control algorithm. Furthermore, for proper power balance and system integrity, the output current of the boost converter must be equal to the sum of the input currents drawn by the inverter and the bidirectional DC-DC converter. This current continuity ensures that the entire power delivered by the PV array is accurately distributed between the AC load and the battery charging system.
The voltage and current waveforms observed at the output of the inverter, along with the total harmonic distortion (THD) value of the output voltage, are presented in Fig. 12. Since the THD values of both the output voltage and current were found to be identical under the given operating conditions, only the voltage THD is reported for brevity. In the simulation studies, the performance of the entire system was evaluated under six predefined operating scenarios corresponding to the BMS control logic. These scenarios were specifically designed to assess the system’s power control capability under various irradiance, temperature, and load conditions. The resulting system responses are depicted in Fig. 13. The detailed operating conditions for each time interval shown in Fig. 13 are as follows:
0–1 s: The PV array supplied both a 350 W battery charging load and a 400 W single-phase AC load under 1000 W/m² irradiance at an ambient temperature of 25 °C. 1–2 s: The PV panels continued to charge the 350 W battery bank at reduced irradiance of 400 W/m² and an ambient temperature of 30 °C. 2–3 s: The PV array solely supplied a 600 W AC load under irradiance conditions of 750 W/m² at 35 °C. 3–4 s: At 600 W/m² irradiance and 25 °C, both the PV array and the battery bank jointly supplied a 700 W AC load. 4–5 s: The battery bank independently powered a 500 W AC load without contribution from the PV system. 5–5.5 s: No power transfer occurred, simulating a no-load and no-generation condition.
The voltage and current waveforms observed at the output of the inverter, along with THD.
The power, voltage and current results of the entire system under various irradiance, temperature, and load conditions for each time interval.
These test scenarios effectively demonstrate the flexibility and robustness of the proposed system under dynamic real-world conditions, validating the coordination between the MPPT controller and the BMS under varying generation and load profiles. The simulation results confirm that the proposed current-sensorless MPPT algorithm, the implemented BMS, and the associated system components operate in full accordance with the predefined BMS operating scenarios. Notably, the simulation also reveals that the battery current exhibits transient high-frequency oscillations for a brief duration, particularly during periods when the battery bank supplies power to the load. This behavior is primarily attributed to the combined effects of the leakage inductance of the transformer used for galvanic isolation in the isolated full-bridge bidirectional DC-DC converter, and the parasitic capacitance of the DC bus capacitor. The interaction between these inductive and capacitive elements creates a resonant circuit, which induces temporary current oscillations until the system stabilizes under steady-state conditions.
Following the successful validation of the proposed system through simulation studies, experimental investigations were conducted using a 400 W resistive load, as depicted in Fig. 14. The components utilized in the power converters for the experimental setup including their nominal ratings, sources, and load characteristics were selected to match exactly those used in the simulation studies. This ensured consistency between simulation and experimental conditions, facilitating a direct comparison of system performance across both validation environments. The control algorithms, initially developed and tested in the MATLAB/Simulink environment, were deployed onto a LAUNCHXL-F28379D DSP microcontroller platform for real-time implementation. The experimental hardware included power electronic converters designed by us, specifically: a hybrid interleaved boost DC-DC converter, an isolated full-bridge bidirectional DC-DC converter, and a single-phase full-bridge inverter. For the PV energy source, three series-connected SK125 × 125-M-72–195 W model PV panels were employed, while the energy storage system consisted of four 12 V, 100 Ah batteries connected in series to match the required system voltage levels. This experimental configuration enabled comprehensive testing of the integrated PV–battery hybrid power generation system under realistic conditions. brevity.
As previously discussed, the proposed current-sensorless MPPT method eliminates the need for direct measurement of the PV output current. In simulation studies, the accuracy of the estimated PV current was validated by comparison with the actual current values generated by the system model. For experimental validation, the accuracy of the current estimation algorithm implemented on the DSP controller was assessed by measuring the PV output current with a physical current sensor and comparing it to the DSP-calculated current values. Using integrated visualization and monitoring tools available within the Code Composer Studio (CCS) environment, both the measured and estimated current values were simultaneously displayed numerically and graphically on the DSP interface. As illustrated in Fig. 15, the comparison demonstrates a strong correlation between the measured and calculated PV current values, confirming the high accuracy and reliability of the proposed estimation technique under practical operating conditions.
The picture of the experimental setup.
The measured and calculated PV current values.
The proposed hybrid interleaved boost DC-DC converter, which integrates the current-sensorless MPPT method, incorporates both phase-interleaved power stage paralleling and hybrid converter characteristics. Owing to its hybrid topology, the converter achieves approximately twice the voltage gain compared to a conventional single-phase boost converter. As illustrated in Fig. 16, the input current of the converter—equivalent to the output current of the PV source—is the sum of the inductor currents from the two parallel power stages, denoted as IL1 and IL2. The phase-shifted switching operation of the two power stages causes the current in one stage to increase while the current in the other decreases, resulting in a significant reduction of the input current ripple when the two currents are combined. Moreover, the ripple frequency characteristics of the input current differ from those of the individual power stage currents. While each power stage operates with a switching frequency of 20 kHz, the interleaving effect effectively doubles the ripple frequency of the combined input current to 40 kHz. This frequency doubling leads to improved dynamic performance, reduced input filtering requirements, and enhanced electromagnetic compatibility (EMC) characteristics of the overall converter system.
The experimental results of hybrid interleaved boost converter; gate signal and IL1, VPV-VDC, IL1-IL2, IPV-IL1.
The output voltage of the boost converter also serves as the DC bus voltage for the system. As depicted in Fig. 16, the system maintained a stable DC bus voltage of approximately 407 V under varying irradiance levels from the PV source. Across different MPPT operating conditions, minor fluctuations were observed, with the DC bus voltage ranging between 390 V and 410 V, demonstrating the effectiveness of the proposed control scheme in maintaining voltage stability despite dynamic changes in input power.
An LCL-type output filter was designed and integrated into the single-phase full-bridge inverter, which represents the final stage of the proposed system architecture. As illustrated in Fig. 17, the inverter output voltage derived from the DC bus was measured both before and after filtering to evaluate the filter’s effectiveness. Despite fluctuations in the DC bus voltage between 390 V and 410 V due to dynamic MPPT operation, the inverter consistently delivered a stable 220 V rms AC output. The THD of the inverter output voltage was measured to be within the range of 1% to 2%, confirming that the LCL filter effectively attenuated high-frequency components and ensured high-quality power delivery to the load. Furthermore, as shown in Fig. 18, the voltage-current characteristics of both the DC bus and the inverter output were recorded under a nominal load condition of 400 W. The single-phase inverter, operating at a switching frequency of 40 kHz, demonstrated a measured efficiency of approximately 94% at the rated power level.
Unlike in simulation studies, it is not feasible to simultaneously capture all operating states of the BMS in real time on the oscilloscope during experimental validation. Nevertheless, comprehensive experimental tests were conducted under all predefined BMS operating conditions, and the system consistently exhibited stable and reliable performance.
The experimental results of the inverter output voltage.
The experimental results of; input voltage/current of the inverter and output voltage/current of the inverter for a 400 W load.
The overall system efficiency was found to fluctuate depending on the battery bank’s charging/discharging status and whether the battery system was active or inactive. As a result, a consolidated system efficiency curve was not presented. However, the individual conversion efficiencies of the primary power stages were measured. Specifically, the hybrid interleaved boost DC-DC converter achieved an efficiency of 96% at a nominal power output of 400 W, while the single-phase full-bridge inverter recorded an efficiency of 94% under the same load condition.
This paper presented the design, simulation, and experimental validation of an improved current-sensorless MPPT control method, integrated with a BMS, for single-phase standalone PV-battery hybrid power systems. The proposed system utilizes a hybrid interleaved boost DC-DC converter for enhanced voltage gain and reduced input current ripple, significantly improving dynamic response and efficiency over conventional boost converter topologies. By eliminating the need for direct current measurement, the current-sensorless MPPT algorithm reduces system complexity, cost, and susceptibility to sensor-related noise, making it highly attractive for off-grid and cost-sensitive applications.
The effectiveness of the proposed approach was validated under both simulation and experimental conditions. MATLAB/Simulink simulations demonstrated accurate power tracking and stable DC bus voltage regulation across varying irradiance levels and load conditions. Experimental studies, conducted with a 400 W resistive load and real-time DSP implementation, confirmed the robustness and practicality of the system. The hybrid interleaved boost DC-DC converter achieved an efficiency of 96% at a nominal power output of 400 W, while the single-phase full-bridge inverter recorded an efficiency of 94% under the same load condition. The THD of the inverter output voltage was measured to be within the range of 1% to 2%, confirming that the LCL filter effectively attenuated high-frequency components and ensured high-quality power delivery to the load. As illustrated in Figs. 7 and 8, following a step change in irradiance from 800 to 1000 W/m², the proposed MPPT algorithm converges to the new maximum power point within approximately 50–100 ms. This response time is significantly faster than that reported for conventional P&O-based MPPT methods, while maintaining limited transient overshoot and stable DC-side operation. The results confirm the effectiveness of the proposed method under dynamic operating conditions. Quantitatively, the steady-state ripple of PPV is limited to approximately 4–5 W peak-to-peak, corresponding to about ± 0.5% of the 500 W power. The average tracking efficiency is estimated to be around 99.4%, indicating that the proposed approach can extract nearly all the available PV power with minimal oscillatory losses. These results confirm both the dynamic effectiveness and steady-state robustness of the proposed MPPT scheme.
Additionally, the BMS successfully coordinated energy flow between the PV array, battery bank, and load, ensuring continuous operation under diverse power generation and consumption scenarios. The system maintained operational stability even under transient conditions, such as load switching and irradiance fluctuations.
Future work will focus on extending the control strategy to incorporate advanced battery state-of-charge (SOC) estimation algorithms and improving the efficiency of the whole system by applying soft-switching methods.
The datasets generated and/or analysed during the current study are not publicly available because a standalone dataset was not created for the simulation outputs; however, the simulation data can be provided by the corresponding author on reasonable request. The experimental results are reported as oscilloscope outputs, and no separate dataset is available for these.
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This research was funded by THE SCIENCE COMMITTEE OF THE MINISTRY OF SCIENCE AND HIGHER EDUCATION OF THE REPUBLIC OF KAZAKHISTAN under Grant No. AP23488947.
Faculty of Engineering, Electrical and Electronics Engineering Department, Yalova University, Yalova, Turkey
Naci Genc
Electrical Engineering Department, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Kazakhstan
Naci Genc & Zhansaya Kalimbetova
Faculty of Engineering, Electrical and Electronics Engineering Department, Van Yuzuncu Yil University, Van, Turkey
Hasan Uzmus
Department of Electrical and Energy, Agri Ibrahim Cecen University, Agri, Turkey
Mehmet Ali Celik
Department of Physics, Khoja Akhmet Yassawi International Kazakh-Turkish University, Turkestan, Republic of Kazakhstan
Sherzod Ramankulov
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Conceptualization, H.U. and N.G.; methodology, H.U., N.G. and M.A.C.; software, H.U., N.G., Z.K.; validation, H.U. and M.A.C.; formal analysis, N.G., Z.K.; investigation, H.U., N.G., Z.K.; writing—S.R., Z.K.; visualization, S.R. and Z.K.; funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript.
Correspondence to Zhansaya Kalimbetova.
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Genc, N., Uzmus, H., Kalimbetova, Z. et al. Current sensorless MPPT method with battery management for PV based single phase standalone system. Sci Rep 16, 9107 (2026). https://doi.org/10.1038/s41598-026-40097-2
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