Electric Vehicles

And Control of Electric Vehicle Charging Infrastructure

Kumar, Arun
Singh, Namit
Moura, Scott
Shukla, Sharabh
Sankar, Narayanan
2018

A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for...

EEZ Mobility: A Tool for Modeling Equitable Installation of Electric Vehicle Charging Stations

Clark, Callie
Ozturk, Ayse Tugba
Hong, Preston
González, Marta C.
Moura, Scott J.
2022

Public electric vehicle (EV) chargers are unevenly distributed in California with respect to income, race and education-levels. This creates inequitable access to electric mobility especially for low-income communities of color, which. are less likely to have access to home charging stations. These vulnerable communities are also more likely to be located in areas with poor air quality and would therefore benefit from EV adoption. Currently programs exist in California that fund incentives for public EV chargers in “Disadvantaged Communities” but the process for identifying these...

Pricing Scheme Design for Vehicle-to-Grid Considering Customers Risk-Aversive Behaviors

Ju, Yi
Moura, Scott
2023

The increasing penetration of plug-in electric vehicles (PEVs) brings both opportunities and challenges to the power grid. Vehicle-to-grid (V2G) proposes a promising solution that enhances grid resilience, reduces carbon emissions and saves facility investment. However, the actual motivation of drivers to participate in such programs is questionable: they may have to unexpectedly depart earlier than their schedules, thus worrying that their cars are short in charge by then. As a consequence, they may refuse to accept the flexible charging plan even though it is attractive in the explicit...

Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning

Vijay, Upadhi
Woo, Soomin
Moura, Scott J.
Jain, Akshat
Rodriguez, David
Gambacorta, Sergio
Ferrara, Giuseppe
Lanuzza, Luigi
Zulberti, Christian
Mellekas, Erika
Papa, Carlo
2023

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for their environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification require granular and bespoke data on bus operation that can be difficult to procure. This strict requirement on data and modeling can hinder potential collaborators on bus electrification, such as electric vehicle...

Investigating the “Whole-Life Performance” of Representative Profile Extraction for Microgrid Planning

Xie, Linfeng
Ju, Yi
Wang, Zhe
Su, Zhihan
Moura, Scott
Lin, Borong
2023

Numerous innovations emerge for decarbonization in energy systems. Practitioners and policymakers look for reliable methodology to evaluate their actual contributions unbiasedly. Usually, such evaluation starts from extracting a collection of profiles, which represents the actual application scenarios and certainly influences the ultimate results. However, such a fundamental task has seemingly long been treated casually. Limited literature on this topic rarely extends their attention beyond clustering methods. In this paper, we present mainly three innovations. First, we make a systematic...

Joint Design for Electric Fleet Operator and Charging Service Provider: Understanding the Non-Cooperative Nature

Zhao, Yiqi
Zeng, Teng
Allybokus, Zaid
Guo, Ye
Moura, Scott
2023

This work proposes a new modeling framework for jointly optimizing the charging network design and the logistic mobility planning for an electric vehicle fleet. Existing literature commonly assumes the existence of a single entity – the social planner, as a powerful decision maker who manages all resources. However, this is often not the case in practice. Instead of making this assumption, we specifically examine the innate non-cooperative nature of two different entities involved in the planning problem. Namely, they are the charging service provider (CSP) and the fleet operator (FO). To...

A Nonlinear Fractional-Order Dynamical Framework for State of Charge Estimation of LiFePO4 Batteries in Electric Vehicles

Borah, Manashita
Moura, Scott
Kato, Dylan
Lee, Jaewoong
2023

An efficient state of charge (SOC) estimation for LiFePO4 batteries in electric vehicles (EVs) has been an open problem so far, largely due to its non-measurable nature. This paper tackles this problem by presenting a fractional-order (FO) dynamical framework to unravel and understand the inherent dynamics of the LiFePO4 battery which leads to an improved estimation of SOC. First, a FO model (FOM) is proposed where the parameters are introduced as nonlinear functionalities of SOC. It has been observed that the FO defined as a nonlinear function of SOC is crucial in identifying its...

Global Sensitivity Analysis of 0-D Lithium Sulfur Electrochemical Model

Dangwal, Chitra
Kato, Dylan
Huang, Zhijia
Kandel, Aaron
Moura, Scott
2023

This paper examines global parameter sensitivity in a zero-dimensional lithium-sulfur (Li-S) battery model. Li-S batteries are an appealing cell chemistry due to their high theoretical energy density, abundant supply, and low cost. Due to the lack of complete understanding of the underlying working mechanisms for Li-S cells, the development of mathematical models and state estimation is still in its early stages. Model development and parameter Identification are closely associated. Both are essential for developing battery management systems (BMS) in commercialized Li-S powered...

Distributionally Robust and Data-Driven Solutions to Commercial Vehicle Routing Problems

Keyantuo, Patrick
Wang, Ruiting
Zeng, Teng
Vishwanath, Aashrith
Borhan, Hoseinali
Moura, Scott
2023

In this paper, we study the routing of commercial electric trucks through an application of distributionally robust optimization (DRO) for route planning and dispatch. This approach aims to minimize total cost of operation for the fleet, and considers the variability in energy consumption due to uncertain road conditions, traffic, weather and driving behavior. Furthermore, we augment the distributionally robust energy minimizing vehicle routing problem by learning the energy efficiency distribution over a horizon. We show that convergence to the true distribution is achieved while learning...

Robust Estimation of State of Charge in Lithium Iron Phosphate Cells Enabled by Online Parameter Estimation and Deep Neural Networks

Shi, Junzhe
Kato, Dylan
Jiang, Shida
Dangwal, Chitra
Moura, Scott
2023

This paper addresses the state of charge estimation problem in lithium iron phosphate (LFP) battery cells. LFP cells are particularly challenging because their fat open circuit voltage (OCV) curve means OCV-based battery models are weakly observable. This means standard methods for SOC estimation don't easily converge to the true SOC. Additionally, in practice, estimates must be accurate in the face of biased noise on current input, as well as mean-zero noise on measurements. As such, we aim to create an estimator that is accurate when facing these types of noise. We accomplish this with a...