Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.

Integrating electric vehicle (EV) charging infrastructures into the utility grids requires solutions for coordinated charging of EVs. Load management systems aim at computing coordinated charging schedules for electric vehicles based on predetermined charging objectives. Contributions on coordinated charging for EVs predominantly assume that the EVs or the charging stations are controllable entities in the load management systems. However, in practice, charging infrastructures may consist of controllable and uncontrollable entities. This paper proposes architecture and control strategies for EV charging infrastructures consisting of controllable and uncontrollable entities. Simulations based on real-world charging sessions show how the share of uncontrollable entities in a charging infrastructure affects the performance of different control strategies in the system architecture. We show that a certain number of uncontrollable entities in a charging infrastructure does not affect the scheduling objectives significantly. EV fleet and charging infrastructure operators can develop pragmatic investment and operation strategies based on the proposed control strategy and architecture.

A novel meshless electromagnetic (EM) simulation framework based on Physics-Informed Neural Networks (PINNs), enhanced by the integration of Kolmogorov–Arnold Networks (KANs) is presented. The proposed method addresses both electrostatic and electrodynamic problems governed by Laplace and Helmholtz equations, respectively. A modular and interpretable simulation architecture is developed using KANPINNs which enables accurate field learning in multilayered printed circuit boards (PCBs) with spatially varying permittivity. Three canonical structures: an electrostatic box, a parallel-plate transmission line (TL) and a stripline TL are modeled to demonstrate the validity of the method. The results are validated against commercial full-wave solvers showing excellent agreement with normalized root-mean-square errors (RMSE) below 0.1. Moreover, a 4-layer PCB structure is simulated at 100 MHz to demonstrate the method's capability at higher frequencies. For this case, the model achieves a normalized RMSE of 0.153 while reducing simulation time by a factor of three compared to numerical solvers. The proposed framework provides a scalable and fully mesh-free alternative to traditional electromagnetic solvers. This introduces new potential for efficiently simulating complex PCB structures in EMI applications.

End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed ANTISCAM dataset and an existing PERSUASIONFORGOOD dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks.