Asynchronous Differential Evolution with Lissajous Mutation for Efficient Energy Management of Plug-In Electric Vehicles

The transportation sector contributes significantly to global greenhouse gas emissions, driving the transition to plug-in electric vehicles (PEVs) for environmental sustainability. However, the widespread adoption of electric vehicles presents significant challenges to the reliability and quality of the power grid due to unpredictable loading patterns and diverse vehicle types and user behaviors, leading to a large-scale optimization problem. The state of charge (SoC) is a crucial performance parameter that can be optimized using computational techniques to facilitate efficient vehicle charging. This study introduces a novel approach to maximize the state of charge in all vehicles in 24-hour scenarios, allowing the PEVs to depart around 80% of the SoC, similar to real-world situations. An asynchronous differential evolution with the Lissajous mutation algorithm (ADELI) is proposed to address this nonlinear optimization problem. ADELI enhances the standard differential evolution (DE) by combining the asynchronous nature of DE with the Lissajous curves used as a mutation operator, thus improving exploration and exploitation. The proposed algorithm is assessed under different charging scenarios. Results demonstrate that ADELI consistently outperforms traditional optimization methods regarding solution quality and convergence speed.