Electric Vehicles

Data-Driven Energy Management Strategy for Plug-in Hybrid Electric Vehicles with Real-World Trip Information

Choi, Yongkeun
Guanetti, Jacopo
Scott Moura
Borrelli, Francesco
2020

This paper presents a data-driven supervisory energy management strategy (EMS) for plug-in hybrid electric vehicles which leverages Vehicle-to-Cloud connectivity to increase energy efficiency by learning control policies from completed trips. The proposed EMS consists of two layers, a cloud layer and an on-board layer. The cloud layer has two main tasks: the first task is to learn EMS policy parameters from historical trip data, and the second task is to provide the policy parameters along a certain route requested from the vehicle. The onboard layer receives the learned policy parameters...

Solving Overstay and Stochasticity in PEV Charging Station Planning With Real Data

Zeng, Teng
Zhang, Hongcai
Scott Moura
2020

This article studies optimal plug-in electric vehicle (PEV) charging station planning, with consideration for the “overstay” problem. Today, public PEV charging station utilization is typically around 15%. When un-utilized, the chargers are either idle or occupied by a fully charged PEV that has not departed. We call this “overstay.” This motivates a strategy for increasing utilization by interchanging fully charged PEVs with those waiting for service-an issue which is not well addressed in the existing literature. Thus, this article studies the PEV charging station planning problem taking...

Charging Infrastructure Demands of Shared-Use Autonomous Electric Vehicles in Urban Areas

Zhang, Hongcai
Sheppard, Colin J. R.
Lipman, Timothy E.
Zeng, Teng
Scott Moura
2020

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM...

Inducing Human Behavior to Alleviate Overstay at PEV Charging Station

Bae, Sangjae
Zeng, Teng
Travacca, Bertrand
Scott Moura
2020

This paper proposes a mathematical framework to optimally operate a plug-in electric vehicle (PEV) charging station, using differentiated charging services. The mathematical framework specifically exploits human behavioral modeling to alleviate "overstay" - when a PEV remains plugged-in after charging service is complete. Discrete Choice Modeling is utilized to capture human decision-making behavior among multiple charging service options that differ in both price and quality-of-service. We reformulate an associated non-convex problem to a multi-convex problem via the Young-Fenchel...

Power-Traffic Network Equilibrium Incorporating Behavioral Theory: A Potential Game Perspective

Zhou, Zhe
Scott Moura
Zhang, Hongcai
Zhang, Xuan
Guo, Qinglai
Sun, Hongbin
2021

This paper examines the interconnections between the power and transportation networks from a game theoretic perspective. Electric vehicle travelers choose the lowest-cost routes in response to the price of electricity and traffic conditions, which in turn affects the operation of the power and transportation networks. In particular, discrete choice models are utilized to describe the behavioral process of electric vehicle drivers. A game theoretic approach is employed to describe the competing behavior between the drivers and power generation units. The power-traffic network equilibrium...

Pareto Optimality in Cost and Service Quality for an Electric Vehicle Charging Facility

Woo, Soomin
Bae, Sangjae
Scott Moura
2021

This paper examines the problem of planning an Electric Vehicle (EV) charging facility that provides a high quality of service in charging EVs and incurs a low cost to the facility manager. This problem is challenging because a facility with a larger charging capacity (hence better service quality) can be more expensive to build and operate. This paper contributes to the literature by planning an EV charging facility that overcomes this trade-off and achieves Pareto optimality, i.e. a facility with a higher quality of service but at a lower cost. We propose an optimization model to size an...

Inducing Human Behavior to Maximize Operation Performance at PEV Charging Station

Zeng, Teng
Bae, Sangjae
Travacca, Bertrand
Scott Moura
2021

Plug-in electric vehicle (PEV) charging station service capability is physically limited by the charger availability and local transformer capacity. However, the station operation performance has become an increasingly important factor for enhancing charging service accessibility. In this work, we propose an innovative station-level optimization framework to operate charging station with optimal pricing policy and charge scheduling. This model incorporates human behaviors to explicitly and effectively capture drivers' charging decision process. We propose a menu of price-differentiated...

Interval Estimation for State-of-Charge and Temperature in Battery Packs with Heterogeneous Cells

Zhang, Dong
Gill, Preet
Scott Moura
Couto, Luis D.
Benjamin, Sebastien
Zeng, Wente
2022

An interval observer based on an equivalent circuit-thermal model for lithium-ion batteries is presented. State of charge-temperature-dependent parameters are considered as unknown but bounded uncertainties in a single cell model. A parallel and a series arrangement of five cells are used for observer design, where cell heterogeneity is accounted for through the uncertainty bounding functions.

Ecological Adaptive Cruise Control of Plug-In Hybrid Electric Vehicle With Connected Infrastructure and On-Road Experiments

Bae, Sangjae
Kim, Yeojun
Choi, Yongkeun
Guanetti, Jacopo
Gill, Preet
Borrelli, Francesco
Scott Moura
2022

This paper examines both mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. Human errors are typical sources of accidents in urban driving, which can be remedied by rigorous control theories. Designing an ECO-ACC is, therefore, a classical research problem to improve safety and energy efficiency. We add two main contributions to the literature. First, we propose a mathematical framework of an online ECO-ACC for plug-in hybrid electric vehicle (PHEV). Second, we demonstrate ECO-ACC in a real world, which...

Thermal-Enhanced Adaptive Interval Estimation in Battery Packs with Heterogeneous Cells

Zhang, Dong
Couto, Luis D.
Gill, Preet S.
Benjamin, Sebastien
Zeng, Wente
Scott Moura
2022

The internal states of lithium-ion batteries need to be carefully monitored during operation to manage energy and safety. In this article, we propose a thermal-enhanced adaptive interval observer for state-of-charge (SOC) and temperature estimation for a battery pack. For a large battery pack with hundreds or thousands of heterogeneous cells, each individual cell characteristic is different from others. Practically, applying estimation algorithms on each and every cell would be mathematically and computationally intractable since battery packs are often characterized by combinations of...