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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....

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

Tao, Shengyu
Guo, Ruohan
Lee, Jaewoong
Moura, Scott
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...

Electrifying Heavy-Duty Trucks: Battery-Swapping vs Fast Charging

Wang, Ruiting
Martinez, Antoine
Allybokus, Zaid
Zeng, Wente
Obrecht, Nicolas
Moura, Scott
2025

The advantages and disadvantages of Battery Swapping Stations (BSS) for heavy-duty trucks are poorly understood, relative to Fast Charging Stations (FCS) systems. This study evaluates these two charging mechanisms for electric heavyduty trucks, aiming to compare the systems efficiency and identify their optimal design. A model was developed to address the planning and operation of BSS in a charging network, considering in-station batteries as assets for various services. We assess performance metrics including transportation efficiency and battery utilization efficiency. Our evaluation...

Physics-Aware Robotic Palletization with Online Masking Inference

Zhang, Tianqi
Wu, Zheng
Chen, Yuxin
Wang, Yixiao
Liang, Boyuan
Moura, Scott
2025

The efficient planning of stacking boxes, especially in the online setting where the sequence of item arrivals is unpredictable, remains a critical challenge in modern warehouse and logistics management. Existing solutions often address box size variations, but overlook their intrinsic and physical properties, such as density and rigidity, which are crucial for real-world applications. We use reinforcement learning (RL) to solve this problem by employing action space masking to direct the RL policy toward valid actions. Unlike previous methods that rely on heuristic stability assessments...

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
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.e.,...

Energy-Aware Lane Planning for Connected Electric Vehicles in Urban Traffic: Design and Vehicle-in-the-Loop Validation

Kim, Hansung
Choi, Eric Yongkeun
Joa, Eunhyek
Lee, Hotae
Lim, Linda
Moura, Scott
Borrelli, Francesco
2025

Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral decisions, such as lane changes, on overall energy efficiency, especially in environments with traffic signals and heterogeneous traffic flow. To address this gap, we propose a novel energy-aware motion planning framework that jointly optimizes longitudinal speed and lateral lane-change decisions using vehicle-to-infrastructure (V2I) communication. Our...

Trajectory-Integrated Accessibility Analysis of Public Electric Vehicle Charging Stations

Ju, Yi
Wu, Jiaman
Su, Zhihan
Li, Lunlong
Zhao, Jinhua
González, Marta C.
Moura, Scott J.
2025

Electric vehicle (EV) charging infrastructure is crucial for advancing EV adoption, managing charging loads, and ensuring equitable transportation electrification. However, there remains a notable gap in comprehensive accessibility metrics that integrate the mobility of the users. This study introduces a novel accessibility metric, termed Trajectory-Integrated Public EVCS Accessibility (TI-acs), and uses it to assess public electric vehicle charging station (EVCS) accessibility for approximately 6 million residents in the San Francisco Bay Area based on detailed individual trajectory data...

A New Framework for Nonlinear Kalman Filters

Jiang, Shida
Shi, Junzhe
Moura, Scott
2025

The Kalman filter (KF) is a state estimation algorithm that optimally combines system knowledge and measurements to minimize the mean squared error of the estimated states. While KF was initially designed for linear systems, numerous extensions of it, such as extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc., have been proposed for nonlinear systems over the last sixty years. Although different types of nonlinear KFs have different pros and cons, they all use the same framework of linear KF. Yet, according to our theoretical and empirical...

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

Jiang, Shida
Shi, Junzhe
Moura, Scott
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...