Electric Vehicles

Distributed Optimal Charging of Electric Vehicles for Demand Response and Load Shaping

Le Floch, Caroline
Belletti, Francois
Saxena, Samveg
Alexandre Bayen
Scott Moura
2015

This paper proposes three novel distributed algorithms to optimally schedule Plug-in Electric Vehicle (PEV) charging. We first define the global optimization problem, where we seek to control large heterogeneous fleets of PEVs to flatten a net Load Curve. We demonstrate that the aggregated objective can be distributed, via a new consensus variable. This leads to a dual maximization problem that can be solved in an iterative and decentralized manner: at each iteration, PEVs solve their optimal problem, communicate their response to the aggregator, which then updates a price signal. We...

To Pool or Not to Pool? Understanding Opportunities, Challenges, and Equity Considerations to Expanding the Market for Pooling

Lazarus, Jessica
Caicedo, Juan
Alexandre Bayen
Susan Shaheen
2021

On-demand mobility services such as bikesharing, scooter sharing, and transportation network companies (TNCs, also known as ridesourcing and ridehailing) are changing the way that people travel by providing dynamic, on-demand mobility that can supplement public transit and personal-vehicle use. Adoption of on-demand mobility has soared across the United States and abroad, driven by the flexibility and affordability that these services offer, particularly in urban areas where population density and land use patterns facilitate a reliable balance of supply and demand. The growth of app-based...

Fast Charging Batteries via Electrochemical Model-based Control

Scott Moura
2014

Title: Fast Charging Batteries via ElectroChemical Model-Based Control In telecommunications, there were 5.2B active mobile handsets and over 1.7B mobile phone sales worldwide for 2012. Mobile phones are also a powerful tool for solving poverty and financial inequity in third world countries. In electrified transportation, there were 53,000 were plug-in electric vehicles sold in the U.S. for 2012. Despite growing sales, range anxiety is considered the largest inhibitor of electrified transportation. Significant reduction in charge times, e.g. comparable to filling a gas tank, would...

Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

Sun, Chao
Hu, Xiaosong
Scott Moura
Sun, Fengchun
2015

The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor...

Quantifying EV Battery End-of-Life Through Analysis of Travel Needs with Vehicle Powertrain Models

Saxena, Samveg
Le Floch, Caroline
MacDonald, Jason
Scott Moura
2015

Electric vehicles enable clean and efficient transportation, however concerns about range anxiety and battery degradation hinder EV adoption. The common definition for battery end-of-life is when 70–80% of original energy capacity remains, however little analysis is available to support this retirement threshold. By applying detailed physics-based models of EVs with data on how drivers use their cars, we show that EV batteries continue to meet daily travel needs of drivers well beyond capacity fade of 80% remaining energy storage capacity. Further, we show that EV batteries with...

Comparison of Velocity Forecasting Strategies for Predictive Control in HEVs

Sun, Chao
Hu, Xiaosong
Scott Moura
Sun, Fengchun
2014

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANN-based method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The...

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Sun, Chao
Scott Moura
Hu, Xiaosong
Hedrick, J. Karl
Sun, Fengchun
2015

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in...

Charging Ahead on the Transition to Electric Vehicles with Standard 120V Wall Outlets

Saxena, Samveg
MacDonald, Jason
Scott Moura
2015

Electrification of transportation is needed soon and at significant scale to meet climate goals, but electric vehicle adoption has been slow and there has been little systematic analysis to show that today’s electric vehicles meet the needs of drivers. We apply detailed physics-based models of electric vehicles with data on how drivers use their cars on a daily basis. We show that the energy storage limits of today’s electric vehicles are outweighed by their high efficiency and the fact that driving in the United States seldom exceeds 100km of daily travel. When accounting for these...

Integrating Traffic Velocity Data into Predictive Energy Management of Plug-in Hybrid Electric Vehicles

Sun, Chao
Sun, Fengchun
Hu, Xiaosong
Hedrick, J. Karl
Scott Moura
2015

Recent advances in the traffic monitoring systems have made traffic velocity information accessible in real time. This paper proposes a supervised predictive energy management framework aiming to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV) by incorporating dynamic traffic feedback data. Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SOC) planning level is constructed in this framework. A power balance PHEV model is developed for this upper level to rapidly generate optimal battery SOC trajectories,...

Optimal Charging of Vehicle-to-Grid Fleets via PDE Aggregation Techniques

Le Floch, Caroline
Di Meglio, Florent
Scott Moura
2015

This paper examines modeling and control of a large population of grid-connected plug-in electric vehicles (PEVs). PEV populations can be leveraged to provide valuable grid services when managed via model-based control. However, such grid services cannot sacrifice a PEV's primary purpose - mobility. We consider a centrally located fleet of identical PEVs that are distributed to and collected from drivers. The fleet also provides regulation services to the grid, contracted a priori. We develop a partial differential equation (PDE)- based technique for aggregating large populations of PEVs....