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Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms
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DOI：10.1109/ICECET52533.2021.9698563.png)
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Electric vehicles (EVs) with vehicle-to-grid (V2G) capability are a promising source of alternative and renewable energy for the existing power grid. However, it is important to prioritize the health of the EV battery since it is the most expensive single component of the vehicle. In this paper, we present a multiobjective function consisting of grid and EV battery parameters which are optimized using a recently proposed evolutionary algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). The EV aggregator model consists of 5 vehicle clusters connected at various electrical distances within the IEEE 33-node system, and each cluster contains 300 EVs. Battery parameters for the Nissan Leaf 2019 model EV are considered in the simulation. The performance of E-dyNSGA-III is also compared with other well-known nature-inspired algorithms such as hybrid spider monkey algorithm (HSMA), raccoon family optimization algorithm (RFOA) and artificial bee colony (ABC) algorithm. Simulation results indicate that E-dyNSGA-III performs better than the three other algorithms.
Keyword:
electric vehicle
state-of-charge
aggregator
vehicle-to-grid
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