A Closed-Loop Prediction and Optimization Approach for Battery Swapping Station of Electric Vehicles under Demand Uncertainty
Year: 2025
Type: article
Abstract: This work investigates the prediction of the quantity of fully charged batteries required at battery swapping stations for electric vehicles under demand uncertainty. Decision-making commonly follows a sequential model involving forecasting steps without feedback, referred to as an open-loop approach. For instance, during the operation of a battery swapping station, the operator uses predictive decision-making to determine which vehicles to swap in the upcoming time slot. This process helps to prepare the necessary number of fully charged batteries. A discrepancy, such as an insufficient number of charged batteries for swapping, leads to increased costs for the station to meet current demand. To address this, we adopt a closed-loop framework. Within this framework, the optimal prediction model is determined based on an application-specifc cost function. We structure the estimation method as a bilevel optimization problem. This problem is solved using an exact method that relies on the KKT (Karush–Kuhn–Tucker) conditions of the second-level problem. Finally, the case study demonstrates that the closed-loop prediction approach for the battery swapping station operation system outperforms the traditional open-loop method, which is least squares.
Source: IFAC-PapersOnLine
Authors: Na Li, Feifeng Zheng, Ming Liu
Institutions: Donghua University, Tongji University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414568177