Electric Vehicles

Relax, Estimate, and Track: A Simple Battery State-of-Charge and State-of-Health Estimation Method

Jiang, Shida
Shi, Junzhe
Scott Moura
2025

Battery management is a critical component of ubiquitous battery-powered energy systems, in which battery state-of-charge (SOC) and state-of-health (SOH) estimations are of crucial importance. Conventional SOC and SOH estimation methods, especially model-based methods, often lack accurate modeling of the open circuit voltage (OCV), have relatively high computational complexity, and lack theoretical analysis. This study introduces a simple SOC and SOH estimation method that overcomes all these weaknesses. The key idea of the proposed method is to momentarily set the cell's current to zero...

Immediate Remaining Capacity Estimation of Heterogeneous Second-Life Lithium-Ion Batteries via Deep Generative Transfer Learning

Tao, Shengyu
Guo, Ruohan
Lee, Jaewoong
Scott Moura
Casals, Lluc Canals
2025

The reuse of second-life lithium-ion batteries (LIBs) retired from electric vehicles is critical for energy storage in underdeveloped regions, where power infrastructures are weak or absent. However, estimating the relative remaining capacity (RRC) of second-life batteries using field-accessible data stream remains challenging due to its scarcity and heterogeneity, despite efforts in battery passports and other initiatives to secure data integrity. This study proposes a deep generative transfer learning framework to address these two-fold challenges by generating voltage dynamics...

Optimization of Electric Vehicle Evacuation Integrating Mobile Charging Stations and Considering Vehicle Diversity

Tang, Xuchang
Kuang, Simon
Lin, Xinfan
Feng, Shuang
de Castro, Ricardo
Gan, Qijian
Scott Moura
2025

This paper addresses the challenges of electric vehicles (EVs) long-distance mass evacuations, particularly those posed by the extended charging time. We focus on optimizing evacuation planning in high EV ownership areas by considering route selection, vehicle grouping, departure timing, and charging scheduling, while incorporating Mobile Charging Stations (MCS) to supplement the existing Fixed Charging Stations (FCS). A two-stage optimization approach is used, i.e. route optimization through a recursive Dijkstra algorithm, followed by vehicle scheduling and MCS deployment via Mixed...

An Adaptive Estimation Approach based on Fisher Information to Overcome the Challenges of LFP Battery SOC Estimation

Shi, Junzhe
Jiang, Shida
Tao, Shengyu
Lee, Jaewong
Borah, Manashita
Scott Moura
2025

Robust and Real-time State of Charge (SOC) estimation is essential for Lithium Iron Phosphate (LFP) batteries, which are widely used in electric vehicles (EVs) and energy storage systems due to safety and longevity. However, the flat Open Circuit Voltage (OCV)-SOC curve makes this task particularly challenging. This challenge is complicated by hysteresis effects, and real-world conditions such as current bias, voltage quantization errors, and temperature that must be considered in the battery management system use. In this paper, we proposed an adaptive estimation approach to overcome the...

Two-Stage Mechanism Design for Electric Vehicle Charging with Day-Ahead Reservations

Su, Pan-Yang
Ju, Yi
Scott Moura
Sastry, Shankar
2025

We propose a general two-period model where electrical vehicles (EVs) can reserve charging sessions in the day-ahead market and swap them in the real-time market. Under the model, we explore several candidate mechanisms for running the two markets, compared using several normative properties such as incentive compatibility, efficiency, reservation awareness, and budget balance. Specifically, reservation awareness is the only property coupling the two markets and dictates that an EV will not get a lower utility by joining the real-time market. Focusing on the real-time market, we show that...

Joint Price and Power Optimization Experiment for Workplace Charging Stations

Ozturk, Ayse Tugba
Obeid, Hassan
Zeng, Teng
Zeng, Wente
Scott Moura
2025

Workplace electric vehicle (EV) charging infrastructure is a key enabler of sustainable urban transitions—by facilitating daytime charging aligned with renewable energy and expanding access for drivers without home-charging options. However, financial sustainability remains challenging as these services are often provided for free or at flat rates. This paper demonstrates the effectiveness of joint price and power optimization in increasing revenue and shifting load at workplace charging stations. We integrate empirically estimated behavioral models to influence user decisions through...

Health-Aware Energy Management for Multiple Stack Hydrogen Fuel Cell and Battery Hybrid Systems

Shi, Junzhe
Aarsnes, Ulf Jakob Flø
Tao, Shengyu
Wang, Ruiting
Nærheim, Dagfinn
Scott Moura
2025

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i....

Joint Price and Power MPC for Peak Power Reduction at Workplace EV Charging Stations

Cambronne, Thibaud
Bobick, Samuel
Zeng, Wente
Scott Moura
2025

Demand charge often constitutes a significant portion of electricity costs for commercial electric vehicle (EV) charging station operators. This paper explores control methods to reduce peak power consumption at workplace EV charging stations in a joint price and power optimization framework. We optimize a menu of price options to incentivize users to select controllable charging service. Using this framework, we propose a model predictive control approach to reduce both demand charge and overall operator costs. Through a Monte Carlo simulation, we find that our algorithm outperforms a...

Online Energy Management System for a Fuel Cell/Battery Hybrid System with Multiple Fuel Cell Stacks

Shi, Junzhe
Aarsnes, Ulf Jakob Flø
Tao, Shengyu
Wang, Ruiting
Nærheim, Dagfinn
Scott Moura
2025

Fuel cell (FC)/battery hybrid systems have attracted substantial attention for achieving zero-emissions buses, trucks, ships, and planes. An online energy management system (EMS) is essential for these hybrid systems, it controls energy flow and ensures optimal system performance. Key aspects include fuel efficiency and mitigating FC and battery degradation. This paper proposes a health-aware EMS for FC and battery hybrid systems with multiple FC stacks. The proposed EMS employs mixed integer quadratic programming (MIQP) to control each FC stack in the hybrid system independently, i.e.,...

Leveraging Commuting Patterns and Workplace Charging to Advance Equitable EV Charger Access

Wang, Ruiting
Kwon, Ha-Kyung
Jordan, Katherine H.
Scott Moura
Boloor, Madhur
Machala, Michael L.
2026

This study introduces a framework for improving accessibility to and quantifying social equity priorities in electric vehicle charging infrastructure through strategic workplace charger placement. We develop a customizable equity evaluation model that quantifies access disparities across demographic groups. This model is used to construct an optimization framework that informs charging infrastructure deployment decisions. Leveraging commuting patterns, we demonstrate in the case study of Oakland, California that strategically placing workplace charging can achieve, on average, a 1.8-fold...