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Weaknesses and Improvements of the Extended Kalman Filter for Battery State-of-Charge and State-of-Health Estimation

Jiang, Shida
Shi, Junzhe
Borah, Manashita
Moura, Scott
2024

Battery management systems (BMS) are essential for ensuring battery performance and safety. Accurate estimation of the State of Charge (SOC) and State of Health (SOH), for example, are critical. However, utilizing the conventional Extended Kalman Filter (EKF) for SOC and SOH co-estimation is often challenging due to problems such as overconfident covariance estimation, overly simplistic assumptions about pro-cess noise and measurement noise covariance matrices, and the shift of the open circuit voltage (OCV) curve as the cell ages. To address these issues, this paper introduces an improved...

Optimal Sizing, Operation, and Efficiency Evaluation of Battery Swapping Stations for Electric Heavy-Duty Trucks

Wang, Ruiting
Ju, Yi
Allybokus, Zaid
Zeng, Wente
Obrecht, Nicolas
Moura, Scott
2024

Decarbonization and electrification of long-haul trucks are notoriously difficult due to the high energy demand and limited gravimetric energy density of lithium-ion cells. In this study, we investigate the optimal deployment and operation of a grid-connected battery swapping station (BSS) for electric long-haul trucks as a mixed-integer optimization problem. We construct a model for reliably meeting customer energy needs while providing grid services, to demonstrate the business case and the operation of such a system. The impact of optimal sizing of the station is explored. A comparative...

Method and Apparatus for Operating Electric Vehicle Charging Infrastructure

Ju, Yi
Zeng, Teng
Moura, Scott
Allybokus, Zaid
2024

A method for operating an electric vehicle charging station that comprises a first number of fixed chargers and a second number of mobile devices. Each of the mobile devices moves in the charging station to plug and unplug an electric vehicle. The method includes, at a time step, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the time step, a first charging demand; deriving, upon receiving charging dynamics of an electric vehicle having been staying at the charging station before the time step, a second charging demand; generating, with...

Robust Routing for a Mixed Fleet of Heavy-Duty Trucks with Pickup and Delivery Under Energy Consumption Uncertainty

Wang, Ruiting
Keyantuo, Patrick
Zeng, Teng
Sandoval, Jairo
Vishwanath, Aashrith
Borhan, Hoseinali
Moura, Scott
2024

Electrification of the truck fleet has the potential to reduce the “harder-to-abate” emissions of logistics significantly, but is generally considered to be very challenging. In this study, we focus on the energy-efficient routing of a mixed fleet of conventional and electric heavy-duty trucks with pickup and delivery under energy consumption uncertainty. We propose an energy consumption model that accounts for realistic driving dynamics, road conditions, weight, and distances. Integrating this model into the routing problem, we address energy consumption uncertainty using second-order...

Enhancing Large-Scale Evacuations of Electric Vehicles Through Integration of Mobile Charging Stations

Tang, Xuchang
Lin, Xinfan
Feng, Shuang
Markolf, Samuel
de Castro, Ricardo
Gan, Qijian
Moura, Scott
2024

Electric vehicles (EVs) pose significant challenges for long-distance mass evacuation during natural hazards due to their long charging time compared to traditional gasoline vehicles. This paper studies the evacuation planning for high EV ownership regions by exploring the optimization of route selection, vehicle grouping, and departure and charging scheduling. More importantly, we also consider the Mobile Charging Stations (MCS), which can be deployed temporarily to supplement the Fixed Charging Stations (FCS) for the evacuation operation, and study the optimization of their placement....

The Nature and Strategy of Minimizing the Total Travel Time for Long-Distance Driving of an EV

Shi, Junzhe
Zeng, Teng
Moura, Scott
2024

The famous Cannonball Run, a cross-country driving challenge from New York City to Los Angeles, highlights the unique challenges of long-distance electric vehicle (EV) route planning. The time record for an internal combustion vehicle is 25 h and 39 min. Comparing this to the EV record of 42 h and 17 min achieved with Tesla Model S, which elucidates the complexities inherent to optimal EV route planning. To bridge this divide, our study introduces a system designed for real-time vehicle-to-cloud (V2C) interaction aimed at enhancing online long-distance EV route planning. Our approach...

Synergizing Physics and Machine Learning for Advanced Battery Management

Borah, Manashita
Wang, Qiao
Moura, Scott
Sauer, Dirk Uwe
Li, Weihan
2024

Improving battery health and safety motivates the synergy of a powerful duo: physics and machine learning. Through seamless integration of these disciplines, the efficacy of mathematical battery models can be significantly enhanced. This paper delves into the challenges and potentials of managing battery health and safety, highlighting the transformative impact of integrating physics and machine learning to address those challenges. Based on our systematic review in this context, we outline several future directions and perspectives, offering a comprehensive exploration of efficient and...

Saving Energy with Eco-Friendly Routing of an Electric Vehicle Fleet

Woo, Soomin
Choi, Eric Yongkeun
Moura, Scott
Borrelli, Francesco
2024

This paper fills the research gap between theoretical vehicle routing algorithms and practical solutions in the field. We use commercially developed prediction algorithms for the energy consumption of vehicles and solve for the energy-efficient routing and charging strategies of an electric vehicle fleet to visit a given set of destinations using meta-heuristics. Then we validate the energy saving performance of the efficient routing solutions with real-world vehicle measurements in a real traffic network. We also conduct a sensitivity analysis via simulation to explore some critical...

Remaining Discharge Energy Prediction for Lithium-Ion Batteries Over Broad Current Ranges: A Machine Learning Approach

Tu, Hao
Borah, Manashita
Moura, Scott
Wang, Yebin
Fang, Huazhen
2024

Lithium-ion batteries have found their way into myriad sectors of industry to drive electrification, decarbonization, and sustainability. A crucial aspect in ensuring their safe and optimal performance is monitoring their energy levels. In this paper, we present the first study on predicting the remaining energy of a battery cell undergoing discharge over wide current ranges from low to high C-rates. The complexity of the challenge arises from the cell’s C-rate-dependent energy availability as well as its intricate electro-thermal dynamics especially at high C-rates. To address this, we...

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
Moura, Scott
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....