This research presents a battery charger design that can charge faster than using a PWM type solar charge controller (SCC). SCC is often operated when the battery capacity is 80% so that the charging current that can be provided is only 10% to 20% of the battery capacity. The battery charging method applied in this study uses the principle of fast charging by adjusting the value of the current and the output voltage value of the buck converter. Fast charging has its own characteristic, obviously, the charging rate that is greater than the usual charging method, which is up to 1C of the battery capacity. The principle of fast charging in this study uses the constant current / constant voltage method. This converter is designed with the ability to produce current by the charging rate of 1C from a 12Ah battery capacity of 12 A and an output voltage of 16.8 V. To ensure that the output of the converter matches the setpoint, the duty cycle value is adjusted using fuzzy control. Based on the results obtained from the simulation, the control of this study obtained an output current 12  Amperes with error ripple current around 8.3%. The SOC on this battery increased by 75.74% in 45 minutes.

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Design and Implementation of Buck Converter for Fast Charging with Fuzzy Logic

Various rechargeable electronic devices currently have batteries with different capacities and voltages, while the available chargers are generally fixed for one device. This is considered less effective because different types of electronic devices will require different battery chargers. Therefore, the adaptive power charge is needed to recharge batteries with different voltages and capacities through a single port by adjusting the type of load connected. This system uses buck converter with duty cycle settings through microcontrollers to lower the input voltage to variable output voltage. When the load is connected, the limit switch will be depressed and the system will start the duty cycle tracking process. The voltage will be increased gradually until the current is read at a certain value to identify the load. After the current reads the duty cycle stops tracking, then the current and voltage characteristics are used as input variables for the artificial neural network (ANN) algorithm to determine the target setpoint voltage to be executed by the proportional, integral and derivative (PID) controller. The designed adaptive power charge can identify the connected load accurately. The average ANN output error is 1.46e-4% and the average PID controller error is 6.4e-2%. The system can reach a steady state at 0.01 s.

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Adaptive charging control using ANN-PID controllers on multiple DC loads with varying battery voltages

Distributed generator (DG) connection to the system with the DC grid called medium voltage direct current (MVDC) grid connection has received attention and gradually integrated into the distribution grid. The linear controller, such as the PI controller, usually uses the MVDC grid control for power oscillation damping. The PI controller is limited and does not show satisfactory results when the load and parameters of the system itself are changed. This paper proposes the PI with neuron network (NN) as a feed-forward controller (PINNF) to improve the control of the DG grid connection with the MVDC. The proposed PINNF controller is applied to control the power oscillation damping. Since the NN can estimate the proper feed-forward control signals in each situation to the control system, the proposed PINNF controller performs better than the conventional PI controller. The effectiveness of the proposed PINNF controller is validated using nonlinear dynamic simulations on the MATLAB/Simulink program. Four case tests are presented and discussed in this paper. Results indicate the improvement of power oscillation damping stability and performance in the MVDC grid connection with the proposed PINNF controller.

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Power oscillation damping control using PI-neuron network controller for distributed generator grid connection with MVDC

Battery is a very important necessity as an electrical energy storage for DC load type. However, as electric energy storage, the battery has a limit storage capacity. The battery must be recharged when the electrical energy stored in the battery has been exhausted to keep the DC load in operation. Unfortunately, batteries in different types of DC loads have different voltages and capacities. So for charging the battery also requires a different voltage. While the existing battery charger is generally static specifically for one type of battery. From this problem, the paper proposed an adaptive power charge system. A system that can adaptively charge electrical energy on batteries that have different voltages and capacities through one port. The converter used in this paper is the buck converter to lower the output voltage from the input voltage. The system uses tracking duty cycle to know the type of DC load. After knowing the type of dc load then the system determines the setting point voltage. To keep the output voltage stable, the system uses PID control. With this adaptive power charge, the charging process will be more efficient and multifunction because it can be used for various types of DC load. The system can provide an output voltage of 19 volts when connected to a laptop and provides an output voltage of 5 volts when connected to a mobile phone on setling time 10ms.

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Ayu Wulandari

Papers

Adaptive Power Charge Using PID Controller on DC Load Application

Adaptive charging control using ANN-PID controllers on multiple DC loads with varying battery voltages