Transportation electrification, which refers to replacing fossil fuel use in the transportation sector with electricity, has evolved into a global mission to reduce greenhouse gas (GHG) emissions from the transportation sector. As an action, promoting the adoption of battery electric vehicles (BEVs), vehicles that are powered purely by electricity and produce zero emissions when in use, has emerged as a promising solution to an electrified low-emission transportation system. Despite widespread enthusiasm for BEVs, multiple research questions need to be addressed in order to achieve large-scale BEV adoption and emission reduction goals. These questions can be grouped into three categories: 1) How can we quantify the spatial and temporal distribution of BEV charging demand? How to model the main factors that can affect users’ charging behaviors and demand? 2) How can we place charging infrastructure in an economically sustainable way? How to find the optimal placement of multiple station types and account for demand uncertainty in station placement? How can we efficiently solve the problem when it’s computationally expensive? 3) How does charging affect energy generation and emissions from supplying the demand? How much could renewable energy help to reduce emissions from BEV charging? And how to coordinate renewable energy and charging infrastructure planning for better emission reduction? This dissertation employed a system modeling approach, proposing mathematical models and optimization algorithms in Chapters 2–4 to address each group of questions, respectively. In Chapter 2, we proposed an integrated activity-based BEV charging demand simulation model and analyzed the charging demand distribution by considering scenarios with various charging behaviors in a mature future market. The model took into account individual travel and charging behaviors, constructed a novel charging behavior model for charging mode choice, and delivered the high-resolution spatio-temporal distribution of charging demand in a real-world case. A case study for the Atlanta metropolitan area indicates that non-residential charging, especially direct-current fast charging (DCFC), has a significant potential market, and users’ charging behaviors have a substantial impact on charging demand distribution. In Chapter 3, we developed an agent-based charging station placement model (ACPM) that optimizes the location and capacity of multi-type charging infrastructure to maximize the net present value (NPV) of public charging stations. The ACPM accounts for demand uncertainty and provides a more realistic estimation of charging demand by simulating individual random travel and charging behaviors. We also proposed a solution algorithm based on random embedding Bayesian optimization (REMBO) that significantly improves computation efficiency and, for the first time, solves the CSLP on a large scale using agent-based simulation. In Chapter 4, we proposed solutions to coordinate the planning of renewable energy and BEV charging infrastructure to achieve the joint emission goal by creating a coupled simulation model that incorporates both transportation and grid networks. An agent-based charging demand simulation model is used to provide more realistic spatio-temporal charging load profiles. In addition, an economic dispatch model is built to simulate grid network operation at high resolution. We analyzed 161 scenarios with a focus on carbon emissions from energy generation and BEV charging, and investigated the impact of a variety of factors such as charging options, solar and wind energy capacities, BEV market size, charging rate, charging price, charging availability, and user charging behaviors. The study conducted the analysis in a real-world case and provided policy insights on how to coordinate the planning of renewable energy and BEV charging infrastructure to achieve the joint emission goal in a real-world application.