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.

ReferenceDatasetTechniqueFindings

[3]Smart sensorsRLComprehensive overview of the many RL techniques and how these could be implemented in power system management.
[4]Optimal charging schedule (SOC)ANNMargin of error of the simulation is minimized.
[15]Real-world chargingRLProposed control mechanism is effective and robust.
[18]Energy storageANNVoltage tracking, reduced grid connection frequency, and more use of photovoltaics.
[20]Power flowANNPower flow changes in microgrids can happen quickly.
Proposed workOptimal charging schedule (SOC)ANFIS and droop controlControl of energy and management of EV batteries.