This study aims to determine the optimal deployment of charging stations for battery electric vehicles (BEVs) by maximizing the covered path flows taking into account the path deviation and nonlinear elastic demand, referred to as DCSDE for short. Under the assumption that the travel demand between OD pairs follows a nonlinear inverse cost function with respect to the generalized travel cost, a BCAP-based (battery charging action-based path) model will be first formulated for DCSDE problem. A tailored branch-and-price (B&P) approach is proposed to solve the model. The pricing problem to determine an optimal path of BEV is not easily solvable by available algorithms due to the path-based nonlinear cost term in the objective function. We thus propose a customized two-phase method for the pricing problem. The model framework and solution method can easily be extended to incorporate other practical requirements in the context of e-mobility, such as the maximal allowable number of stops for charging and the asymmetric round trip. The numerical experiments in a benchmark 25-node network and a real-world California State road network are conducted to assess the efficiency of the proposed model and solution approach.

这项研究旨在通过考虑路径偏差和非线性弹性需求（简称DCSDE），通过最大化覆盖的路径流量，确定电池电动汽车（BEV）充电站的最佳配置。假设OD对之间的旅行需求相对于广义旅行成本遵循非线性逆成本函数，则将首先针对DCSDE问题建立基于BCAP（基于电池充电行为的路径）模型。提出了一种量身定制的B＆P（B＆P）方法来解决该模型。由于目标函数中基于路径的非线性成本项，因此无法通过可用算法轻松解决确定BEV最佳路径的定价问题。因此，我们针对定价问题提出了一种定制的两阶段方法。可以轻松扩展模型框架和解决方案方法，以结合电动汽车方面的其他实际要求，例如，充电所允许的最大停车位数量和非对称往返行程。在基准的25个节点的网络和真实的加利福尼亚州道路网络中进行了数值实验，以评估所提出的模型和解决方案方法的效率。