Research Article
A Novel Electric Vehicle Battery Management System Using an Artificial Neural Network-Based Adaptive Droop Control Theory
Table 1
Comparative analysis between the previous work and proposed work.
| Reference | Dataset | Technique | Findings |
| [3] | Smart sensors | RL | Comprehensive overview of the many RL techniques and how these could be implemented in power system management. | [4] | Optimal charging schedule (SOC) | ANN | Margin of error of the simulation is minimized. | [15] | Real-world charging | RL | Proposed control mechanism is effective and robust. | [18] | Energy storage | ANN | Voltage tracking, reduced grid connection frequency, and more use of photovoltaics. | [20] | Power flow | ANN | Power flow changes in microgrids can happen quickly. | Proposed work | Optimal charging schedule (SOC) | ANFIS and droop control | Control of energy and management of EV batteries. |
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