Projects

We will implement an interactive user-friendly dashboard to create charging strategies and guide EV battery services to reduce cost, reduce the carbon footprint of vehicles and buildings, and increase resilience, thus expanding the EV value proposition for customers. Analysts expect electricity demand to increase by 900-1800TWh (20-40%) from 2016 to 2050, driven by transport and building electrification. The EV decarbonization trajectory will depend on changes in the electricity generation mix. We will account for this potential evolution in the grid, driven by proposed Federal policy, to more accurately assess the effect of EV adoption on lifetime greenhouse gas (GHG) emissions.

Analyses of the GHG impacts of EVs typically assume a static electricity grid. We will draw on forecasts (e.g., NREL’s Cambium model) of the electric generation mix under different decarbonization scenarios. We will therefore account for the fact that the use-phase GHG emissions from EVs will diminish over the lifetimes of the vehicles. This is especially important given the Federal government’s ambition to fully decarbonize power generation by 2035. The combination of vehicle charging and building electrification could sharply increase peak electricity demand, stressing the electricity system. Time of use rates, critical peak pricing, and demand changes could increase the cost of electrification. We will develop an interactive model that leverages the ability to integrate EV batteries with buildings (as is already possible with the Ford F150 Lightning) to cut energy cost, emissions, or peak electricity demand of the EV-building system. The model will allow users, including fleet operators, to change parameters like driving habits, charging constraints, and building characteristics. The tool will quantify the lifetime GHG emissions reductions attainable by EVs, including their potential contribution to reducing building GHG emissions; for example, by lowering the cost of electrification.

A 2016 National Renewable Energy Laboratory (NREL) study found that 79% of people who consider buying EVs do so to cut fuel costs and 87% consider EVs because of their environmental benefits. The key barrier to EV adoption (55% of respondents) is the upfront cost. This study will produce knowledge and tools that help customers to use their EVs to maximize the benefits they value most (i.e., lower GHG emissions and fuel costs). It will develop tools to enable customers to balance the upfront cost of EVs with their greater capabilities and benefits.

Many current analyses of the GHG benefits of EVs ignore the fact that the electricity grid will get cleaner over the lifetime of the vehicle. Given the strong Federal and state policy drive towards clean electricity, both assumptions likely underestimate the benefits of EVs. Moreover, environmental and private benefits and costs of EVs will depend heavily on when and how EVs are charged. Recent work by the U-M team shows that optimal charging strategies for electric delivery vans can reduce their GHG emissions by 8-37% relative to naïve strategies and reduce costs by a similar margin. The proposed study will aid Ford’s efforts to expand the customer value proposition of EVs by leveraging the ability of EV batteries to shape user interactions with the electricity grid in ways that reduce cost, reduce the environmental impact of energy use, and increase resilience. Therefore, the study will quantify the contribution Ford EVs can make to transport and building decarbonization.

Task 1—Quantify and maximize lifetime EV private and GHG benefits under grid decarbonization: We will develop a model to optimize the charging profile for an EV. Users will be able to assign weights to damages from GHGs and private charging costs. For fleet owners, the model will produce optimal charging profiles for multiple vehicles. We will account for different vehicle types, locations, and utility rate structures (including time-of-use or demand charges). We will draw on the NREL’s Cambium model to assess the GHG emissions of EV operation over the life of the vehicle. Cambium allows analysts to estimate marginal emissions from EV charging as the grid evolves. Based on Cambium, user inputs, a battery degradation model, and other publicly available data, we will estimate the lifecycle greenhouse gas reductions from operating an EV relative to comparable internal combustion engine vehicles (ICEV).

Task 2—Optimize EV storage services for residential energy costs and GHG emissions: We will optimize EV charging and discharging as part of a building-vehicle system. We will estimate the value of the vehicle battery as a resource available to building owners, while accounting for the costs of battery degradation. We will allow users to select archetypical buildings based on certain characteristics (e.g., build year, size, location, cooling and heating technologies). Users will be able to specify utility rate structures and the presence and size of rooftop solar panels. Users will have the option to run the model to propose an optimal size for a rooftop solar system given location; home and vehicle characteristics; and the proportion of clean energy desired. Users will be able to constrain when the vehicle is connected to the grid. The model will optimally “dispatch” the vehicle battery to minimize a user-defined combination of private cost (electricity cost and battery degradation) and damages from GHG emissions. We will compare this cost to the costs without the vehicle battery in the loop to estimate the value of the battery to the user and to the environment. The model will perform this analysis over the lifetime of the vehicle and account for the evolution of the electric grid.

Task 3—Lifetime GHG and cost dashboard for integrated building-EV system: We will demonstrate an interactive tool that will give users an estimate of their lifetime and per-mile GHG emissions and charging costs, given user-specified vehicle characteristics, charging constraints, and home energy use attributes. Given these inputs and constraints, the tool will propose optimal charging profiles. Users will be able to choose attributes from a pre-defined list (e.g., sedan or pickup truck; highway or city driving profiles) or to develop custom use profiles. Users will also be able to input location, estimates of vehicle annual mileage, and anticipated vehicle life. The tool will show users how much the battery can save them in total home / building energy costs and GHG emissions under different scenarios: e.g., if they have rooftop solar PV, adopt different utility rate structures, or install a heat pump or electric water heater.