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In this paper, we introduce a novel direct maximum power point tracking (MPPT) approach that combines the backstepping controller (BSC) and the super-twisting algorithm (STA). The direct backstepping super-twisting algorithm control (BSSTAC) MPPT was developed to extract the maximum power point (MPP) produced by a photovoltaic (PV) generator connected to the battery through a boost DC-DC converter. To reduce the number of sensors required for the BSSTAC implementation, a high gain observer (HGO) was proposed to estimate the value of the state of the PV storage system from measurements of the PV generator voltage and current. The suggested technique is based on the quadratic Lyapunov function and does not employ a standard MPPT algorithm. Results show that the suggested control scheme has good tracking performance with reduced overshoot, chattering, and settling time as compared to the prevalent MPPT tracking algorithms such as perturb and observe (P&O), conventional sliding mode control (CSMC), BSC, and integral backstepping controller (IBSC). Finally, real-time findings using the dSPACE DS 1104 software indicate that the generator PV can accurately forecast the MPP, as well as the efficacy of the suggested MPPT technique. The provided approach's effectiveness has been validated by a comprehensive comparison with different methods, resulting in the greatest efficiency of 99.88% for BSSTAC. [ABSTRACT FROM AUTHOR]

This paper examines the problem of controlling the exchange of current in photovoltaic-plus-storage systems to achieve photovoltaic (PV) maximum power point tracking (MPPT). This work is motivated by the need for MPPT algorithms that are less costly and complex to implement in PV farms with integrated battery energy storage. We study the online optimal control of a "hybrid" PV/lithium (Li)-ion battery integration topology that is self-balancing in nature. The self-balancing behavior ensures that the state of charge (SOC) across different cells balances to the same stable equilibrium value without needing any balancing power electronics, thereby significantly reducing the integration cost. The DC-DC converters in this hybrid system are controlled to achieve PV MPPT that maximizes energy generation and storage. However, sensing needs for traditional MPPT controllers can render the hybrid system unnecessarily complex and costly. We surmount this problem by: (i) developing a novel model-based PV power estimation algorithm that only requires voltage measurement, and (ii) using this algorithm together with extremum-seeking (ES) control to achieve closed-loop, estimation-based PV MPPT. Simulation case studies show that this estimation-based MPPT controller is able to harness more than 99% of the maximum available solar energy under different irradiation profiles. [ABSTRACT FROM AUTHOR]

This paper examines the problem of controlling the exchange of current in photovoltaic-plus-storage systems to achieve photovoltaic (PV) maximum power point tracking (MPPT). This work is motivated by the need for MPPT algorithms that are less costly and complex to implement in PV farms with integrated battery energy storage. We study the online optimal control of a "hybrid" PV/lithium (Li)-ion battery integration topology that is self-balancing in nature. The self-balancing behavior ensures that the state of charge (SOC) across different cells balances to the same stable equilibrium value without needing any balancing power electronics, thereby significantly reducing the integration cost. The DC-DC converters in this hybrid system are controlled to achieve PV MPPT that maximizes energy generation and storage. However, sensing needs for traditional MPPT controllers can render the hybrid system unnecessarily complex and costly. We surmount this problem by: (i) developing a novel model-based PV power estimation algorithm that only requires voltage measurement, and (ii) using this algorithm together with extremum-seeking (ES) control to achieve closed-loop, estimation-based PV MPPT. Simulation case studies show that this estimation-based MPPT controller is able to harness more than 99% of the maximum available solar energy under different irradiation profiles. [ABSTRACT FROM AUTHOR]

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESSs) have been integrated with wind turbines to mitigate wind intermittence and make wind power dispatchable as traditional power sources. This paper presents two phases of optimizations, namely, power scheduling and real-time control that allows an integrated wind turbine and BESS to provide the grid with consistent power within each dispatch interval. In the power scheduling phase, the desired battery state of charge (SOC) under each wind speed is first determined by conducting an offline probabilistic analysis on historical wind data. With this information, a computationally efficient one-step-ahead model predictive approach is developed for scheduling the integrated system power output for the next dispatch interval. In the real-time control phase, novel control algorithms are developed to make the actual system power output match the scheduled target. A wind turbine active power controller is proposed to track the reference power set point obtained by a steady-state optimization approach. By combining an internal integral torque control and a gain-scheduled pitch control, the proposed active power controller can operate around a desired tip speed ratio (TSR) without an accurate knowledge of turbine power coefficient curve. Compared to the conventional power scheduling and real-time controller, implementing the new methodology significantly reduces the ramp rate, generator torque changing rate, battery charging rate, and the power output deviation from the scheduled target. BESSs with various capacities and different wind profiles are considered to demonstrate the effectiveness of the proposed algorithms on battery sizing. [ABSTRACT FROM AUTHOR]