CHARGE CONTROL USING FUZZY LOGIC
This work modified an existing lead-acid battery charging system by developing and simulating the conventional lead-acid battery and the fuzzy logic control of the lead-acid battery charging systems. The fuzzy logic system is developed using Mamdani inference system in Matlab toolbox and Simulink, and consequently employed in controlling the conventional charging system adopted. The output voltage of the battery is controlled using matrix laboratory (MATLAB) in creating Fuzzy Inference system (FIS). FIS is created by typing “fuzzy” in MATLAB workspace and clicking “Edit” selecting “add variable”, and click input to increase the number of inputs to two. The inputs are labeled voltage error (e) and derivative of error (De/dt). The membership function of the inputs, Mandani, and output blocks and the parameters of theses blocks are filled as shown in Figures 3.7 to 3.9, respectively. Furthermore, the Mandani rules are editted as shown in Figure 3.11. The model shows that the power outputs from the solar cells increased linearly when the voltage changes from 0 to 250V and then increased exponentially from 250 to 420V. The curves showed that the power outputs from the solar cells increased linearly when the voltage changes from 0 to 250V. The increase in voltage from 300 to 350V produces a maximum output power of about 1400W at 20 oC and 1000 W/m2. For the same range of voltage at 400C and 1000 W/m2, the maximum output power is approximately 1200W. Similarly, at 200C and 500 W/m2, and 40 0C and 500 W/m2 the maximum output power is 550W and 500W, respectively. In the case of 200C and 200 W/m2 ancd 400C and 200 W/m2 the maximum output power is 240W and 235W, respectively. The collector current increased from 0 to 800 ampere with the increase in the collector-emitter voltage from 1 to 5V. The only exception is the case where emitter-voltage (Vge) is 8V.
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