Development of a low power solar charge controller for use in off grid areas

Most handheld devices used for field measurements are run on batteries, which are charged from the grid. However, batteries have limited run-time and therefore have to be charged periodically in order to extend their operation. Also, vast areas of our country do not have access to reliable source of electricity due to low pre-existing national grid coverage and minimal investments in off-grid power systems. Consequently, undertaking field measurements in off-grid areas using battery‑powered devices is inconvenient. It is therefore imperative to find an alternative source of electricity to recharge these devices while undertaking measurements in off-grid areas. This study involves developing a prototype of a solar charge controller that is capable of sustaining the operation of Nickel‑ion Metal Hydride and Lithium‑ion cell-powered devices in off-grid areas.

The developed solar charge controller integrates two main elements to improve on battery charging using solar panels. First, it implements a Maximum Power Point Tracking algorithm which optimizes the extraction of power from a solar panel. Then it utilizes a switch‑mode power converter to regulate charging voltage and current over a wide range of values, therefore enabling charging batteries of different voltages and chemistry. From the literature review that was done, the Fuzzy-based Perturb and Observe algorithm was deduced to be the most suitable for tracking the Maximum Power Point of a solar panel because it is fast, efficient and easy to implement in commercially available microcontrollers. The Buck converter was deduced to be the most suitable switch mode power converter since it has a linear transfer function which simplifies the control circuitry, and has fewer active components which translate to minimal power loss during operation.

The solar charge controller was developed using MATLAB software. This involved developing a model of a Photovoltaic Power system which consisted of a solar panel, a buck converter and a resistor load. The model was used to simulate, tune and optimize the Fuzzy-based Perturb and Observe algorithm. Thereafter, a prototype of the charge controller was built using commercially available electronic components. The Fuzzy‑based Perturb and Observe and the Charge Control algorithms were loaded into the microcontroller of the prototype using a suitable programmer. The prototype was designed to accommodate a maximum solar panel rating of 30W. This is equivalent to maximum input voltage and current of 22 V and 2 A, respectively. The maximum charging current and voltage were 12 V and 2 A, respectively. These values are based on the ratings of the components used to construct the prototype. The price of the product was estimated to be Ksh.18,000.00, based on the cost of individual electronic components which formed the charge controller.

The simulations that were performed on the model of the Photovoltaic Power System showed that the Fuzzy-based Perturb and Observe algorithm was able to track the Maximum Power Point of a solar panel with an accuracy of 99 %. Also, the algorithm improved tracking speed by 73 % when compared to the conventional Perturb and Observe algorithm with smaller step size. In addition, the algorithm reduced fluctuations about the Maximum Power Point by  99 % when compared to the Perturb and Observe algorithm with large step size. The algorithm increased the power extracted from a solar panel by 67% compared to when no Maximum Power Point tracking algorithm was used.  

The experimental results of the tests that were carried out on the prototype were similar to the simulations results. The prototype increased the power extracted from a solar panel by 71% when compared to a solar power system with no Maximum Power Point tracking algorithm. Moreover, it improved the tracking speed by 93%, and reduced the power fluctuations by 76 % when compared to the conventional Perturb and Observe algorithm with small and large perturbation steps, respectively. Furthermore, the experimental result of charging batteries by the prototype show that it was able to precisely regulate charging current and voltage, and accurately detect full charge.


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