Academia.eduAcademia.edu

Figure 1 – uploaded by FOREX Publication

See full PDFdownloadDownload figure
: Table 1: Rule Table

Table 1 : Table 1: Rule Table

Related Figures (11)

Here we will go over the basics of electric EEC for a Li-ion battery. It explains how a battery's charge and discharge properties change depending on the pace at which it is charged or discharged. Figure 2 [17-19] depicts the most usual, simple EEC for a Li-ion battery. The open circuit voltage (Vs), the internal resistance (Rint), which has two values (R1 and R2), and the effective capacitance (C1) are the three variables that make up the EEC model. To determine the circuit characteristics for various charge/discharge rates, use this model. The final voltage values predicted by this model are compared to the actual terminal voltage values supplied by the battery suppliers in order to confirm their accuracy. Under constant current charging circumstances, R1, R2, Cl, and Vs are represented mathematically in eq. 1-4 [20]. As a result of charge/discharge percentage, state-of-charge (SOC) for charging situations, and depth-of-discharge (DOD) for discharging scenarios, the battery variables are expressed as equations based on polynomials. Figure 1. General Block Diagram of Battery model
Here we will go over the basics of electric EEC for a Li-ion battery. It explains how a battery's charge and discharge properties change depending on the pace at which it is charged or discharged. Figure 2 [17-19] depicts the most usual, simple EEC for a Li-ion battery. The open circuit voltage (Vs), the internal resistance (Rint), which has two values (R1 and R2), and the effective capacitance (C1) are the three variables that make up the EEC model. To determine the circuit characteristics for various charge/discharge rates, use this model. The final voltage values predicted by this model are compared to the actual terminal voltage values supplied by the battery suppliers in order to confirm their accuracy. Under constant current charging circumstances, R1, R2, Cl, and Vs are represented mathematically in eq. 1-4 [20]. As a result of charge/discharge percentage, state-of-charge (SOC) for charging situations, and depth-of-discharge (DOD) for discharging scenarios, the battery variables are expressed as equations based on polynomials. Figure 1. General Block Diagram of Battery model
A symbolic logic way of thinking, Fuzzy Logic Control (FLC) establishes a grey area between the ideal values of zero and one, where a logical assertion of nonzero is nevertheless viable. Fuzzy inference is process by which input computations of fuzzy variables are fuzzified utilising membership functions which is shown in Figure 2. Technical issues can be resolved by membership function approach, which assigns a range [0,1]. FLC will take place over following truth value in four steps: (1) Fuzzification of input variables, (2) Rule Evaluation, (3) Aggregation of Rule Outputs (4) Defuzzification. The ru based system not only explains how to respond to incomi signals, but also reflects information from outside wor Specifically, the controller design suggests Mamd ani-type ru e- ng d. es as they provide a natural framework for incorporating expert knowledge, rules are composed of a succession of basic THEN statements. An input and output variable mapping system, a fuzzy interference system (FIS) uses if-then princip to make decisions. A defuzzification unit transforms a fuzzy variable into a crisp variable, and a fuzzy set with an arbitrary input and output serves as controller in a FIS. If there are nuances to FLC, it will aid in smooth operation of HES through application of a variety of constraint rules. The battery SoC can be made better with use of FLC. The FLC is a smart algorithm that works effectively with complex systems for which mathematical models are difficult to create. The FLC algorithm is highly resilient in nonlinear and time-varying systems. In addition, FLC is flexible since fuzzy rules can be easily adjusted to meet new requirements. F- es The suggested FLC takes V(K) and I(K) as inputs for fuzzification, with the resulting out variable being used to feed battery. The dP/dV ratio is established by the chopper's pulse width modulation.
A symbolic logic way of thinking, Fuzzy Logic Control (FLC) establishes a grey area between the ideal values of zero and one, where a logical assertion of nonzero is nevertheless viable. Fuzzy inference is process by which input computations of fuzzy variables are fuzzified utilising membership functions which is shown in Figure 2. Technical issues can be resolved by membership function approach, which assigns a range [0,1]. FLC will take place over following truth value in four steps: (1) Fuzzification of input variables, (2) Rule Evaluation, (3) Aggregation of Rule Outputs (4) Defuzzification. The ru based system not only explains how to respond to incomi signals, but also reflects information from outside wor Specifically, the controller design suggests Mamd ani-type ru e- ng d. es as they provide a natural framework for incorporating expert knowledge, rules are composed of a succession of basic THEN statements. An input and output variable mapping system, a fuzzy interference system (FIS) uses if-then princip to make decisions. A defuzzification unit transforms a fuzzy variable into a crisp variable, and a fuzzy set with an arbitrary input and output serves as controller in a FIS. If there are nuances to FLC, it will aid in smooth operation of HES through application of a variety of constraint rules. The battery SoC can be made better with use of FLC. The FLC is a smart algorithm that works effectively with complex systems for which mathematical models are difficult to create. The FLC algorithm is highly resilient in nonlinear and time-varying systems. In addition, FLC is flexible since fuzzy rules can be easily adjusted to meet new requirements. F- es The suggested FLC takes V(K) and I(K) as inputs for fuzzification, with the resulting out variable being used to feed battery. The dP/dV ratio is established by the chopper's pulse width modulation.
Figure 3. Flowchart of charging—discharging controller
Figure 3. Flowchart of charging—discharging controller
Figure 6. The results of Soc (%), Battery current, Battery Voltage and Power variation with PI During discharging of batter, the results of SoC (%), Battery Voltage and Power variation with PI and Fuzzy Logic Controller shown in Figure 6 and Figure 7 respectively.it is observed the SoC of Battery is 45% and the output voltage of battery is 25.6V when the reference Voltage is det to 28V. The battery output current is 22A and Battery Power is 550W. the load Power is -440W with PI controller. With Fuzzy Logic controller it is observed the SoC of Battery is increased to 65% and the output voltage of battery is 24V when the reference Voltage is det to 28V. The battery output current is 200A and Battery Power is 5.6kW. the load Power is -2.5W. So that using FLC during discharging of Battery obtained more power and Load variation also improved.
Figure 6. The results of Soc (%), Battery current, Battery Voltage and Power variation with PI During discharging of batter, the results of SoC (%), Battery Voltage and Power variation with PI and Fuzzy Logic Controller shown in Figure 6 and Figure 7 respectively.it is observed the SoC of Battery is 45% and the output voltage of battery is 25.6V when the reference Voltage is det to 28V. The battery output current is 22A and Battery Power is 550W. the load Power is -440W with PI controller. With Fuzzy Logic controller it is observed the SoC of Battery is increased to 65% and the output voltage of battery is 24V when the reference Voltage is det to 28V. The battery output current is 200A and Battery Power is 5.6kW. the load Power is -2.5W. So that using FLC during discharging of Battery obtained more power and Load variation also improved.
Figure 4. Controller for the proposed fuzzy-based charging— discharging model Figure 5. MFs for input variables Prequirea. (6) MFs for input variables SOCaitr. (c) MFs for output variables current.
Figure 4. Controller for the proposed fuzzy-based charging— discharging model Figure 5. MFs for input variables Prequirea. (6) MFs for input variables SOCaitr. (c) MFs for output variables current.
Figure 9. The results of SoC (%), Battery current, Battery Voltage and Power variation with FLC Figure 8. The results of Soc (%), Battery current, Battery Voltage and Power variation with PI
Figure 9. The results of SoC (%), Battery current, Battery Voltage and Power variation with FLC Figure 8. The results of Soc (%), Battery current, Battery Voltage and Power variation with PI
Ref voltage is 25.95 of battery which is fully charged. voltage saturation of this PI is the max ref current which is 22 A max charging current of battery. During charging of batter, the results of soc (%), Battery Voltage and Power variation with PI and Fuzzy Logic Controller shown in Figure 8. and Figure 8 respectively.it is observed the SoC of Battery is 45% and the output voltage of battery is 25.6V when the reference Voltage iS det to 28V. The battery output current is -18.5A and Battery Power is -1777W. the load Power is -330W with PI controller. With Fuzzy Logic controller it is observed the SoC of Battery iS increased to 65% and the output voltage of battery is 24V when the reference Voltage is det to 28V. The battery output current is -230A and Battery Power is -6.4kW. the load Power iS -3.5W. So that using FLC during charging of Battery obtained more power and Load variation also improved.
Ref voltage is 25.95 of battery which is fully charged. voltage saturation of this PI is the max ref current which is 22 A max charging current of battery. During charging of batter, the results of soc (%), Battery Voltage and Power variation with PI and Fuzzy Logic Controller shown in Figure 8. and Figure 8 respectively.it is observed the SoC of Battery is 45% and the output voltage of battery is 25.6V when the reference Voltage iS det to 28V. The battery output current is -18.5A and Battery Power is -1777W. the load Power is -330W with PI controller. With Fuzzy Logic controller it is observed the SoC of Battery iS increased to 65% and the output voltage of battery is 24V when the reference Voltage is det to 28V. The battery output current is -230A and Battery Power is -6.4kW. the load Power iS -3.5W. So that using FLC during charging of Battery obtained more power and Load variation also improved.
Figure 7. The results of Soc (%), Battery current, Battery Voltage and Power variation with FLC
Figure 7. The results of Soc (%), Battery current, Battery Voltage and Power variation with FLC
Figure 11. Comparison results of Soc (%), Battery current, Battery Voltage and Power variation at charging Mode Figure 10. Comparison results of Soc (%), Battery current, Battery Voltage and Power variation at Discharging Mode
Figure 11. Comparison results of Soc (%), Battery current, Battery Voltage and Power variation at charging Mode Figure 10. Comparison results of Soc (%), Battery current, Battery Voltage and Power variation at Discharging Mode
Related topics:
Fuzzy Logic ControllerState of Charge
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved