About

Scott Moura is a Professor in Civil & Environmental Engineering at the University of California, Berkeley. He serves as the Director of the Energy, Controls, & Applications Lab (eCAL) and is the Berkeley ITS Acting Faculty Director. His academic background includes a B.S. degree from UC Berkeley and M.S. and Ph.D. degrees in mechanical engineering from the University of Michigan, Ann Arbor. Moura's research interests focus on control, optimization, and artificial intelligence for batteries, electrified vehicles, and distributed energy resources. He has held various leadership roles, including PATH Faculty Director and Chair of Engineering Science, and has been recognized for his contributions to the field through his ongoing research projects and leadership in transportation and energy systems.

Research topics

• Computer Science
• Engineering
• Artificial Intelligence
• Algorithm
• Aerospace engineering
• Transport engineering
• Physics
• Real-time computing
• Environmental science
• Mathematics
• Nanotechnology
• Mathematical optimization
• Environmental planning
• Psychology
• Automotive engineering
• Business
• Materials science

Selected publications

• Comparing Automated Driving Systems’ Safety Data with Human Benchmarks

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Toward a sustainable megalopolis by reconciling power system decarbonization and urban health resilience

  Communications Earth & Environment · 2026-01-24 · 3 citations

  articleOpen access

  The escalating frequency of extreme heatwaves coupled with the deep decarbonization of the power system pose growing challenges to the reliable operation of metropolitan power systems. In megacity clusters, heatwave-induced blackouts leave densely populated and vulnerable communities exposed to prolonged thermal stress, significantly elevating urban health risks. In response, we proposes a health-aware decarbonization framework that integrates meteorology, power systems, and urban health—offering a comprehensive, systems-level solution to support the development of resilient, and sustainable cities under intensifying climate stress. Our results indicate that while decarbonizing the power system is crucial for meeting climate goals, it may unintentionally increase heat-related deaths in large urban areas. In the Guangdong-Hong Kong-Macao megacity cluster, the number of cities with excess death rates over 3% is expected to rise from 1 in 2030 to 9 by 2050. However, health-aware decarbonization strategies can cut excess deaths by 55.38% -65.01% and reduce total annual costs by 8.71% -13.63%. Decarbonization of the power system is crucial for meeting climate goals, but it may unintentionally increase heat-related mortality in large urban areas such as Guangdong-Hong Kong-Macao, according to an analysis that uses climate, weather, and health data and a statistical approach

  PublisherOA PDFDOI

• Trajectory-integrated accessibility analysis of public electric vehicle charging stations

  Sustainable Cities and Society · 2026-05-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Model-Agnostic Energy Throughput Control for Range and Lifetime Extension of Electric Vehicles via Cell-Level Inverters

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Sink Proximity: A Novel Approach for Online Vehicle Dispatch in Ride-Hailing

  IEEE Transactions on Intelligent Vehicles · 2026-01-23

  article
  PublisherDOI

• Design Guidelines for Nonlinear Kalman Filters via Covariance Compensation

  arXiv (Cornell University) · 2026-03-24

  preprintOpen accessSenior author

  Nonlinear extensions of the Kalman filter (KF), such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are indispensable for state estimation in complex dynamical systems, yet the conditions for a nonlinear KF to provide robust and accurate estimations remain poorly understood. This work proposes a theoretical framework that identifies the causes of failure and success in certain nonlinear KFs and establishes guidelines for their improvement. Central to our framework is the concept of covariance compensation: the deviation between the covariance predicted by a nonlinear KF and that of the EKF. With this definition and detailed theoretical analysis, we derive three design guidelines for nonlinear KFs: (i) invariance under orthogonal transformations, (ii) sufficient covariance compensation beyond the EKF baseline, and (iii) selection of compensation magnitude that favors underconfidence. Both theoretical analysis and empirical validation confirm that adherence to these principles significantly improves estimation accuracy, whereas fixed parameter choices commonly adopted in the literature are often suboptimal. The codes and the proofs for all the theorems in this paper are available at https://github.com/Shida-Jiang/Guidelines-for-Nonlinear-Kalman-Filters.

  PublisherDOI

• Model-Agnostic Energy Throughput Control for Range and Lifetime Extension of Electric Vehicles via Cell-Level Inverters

  arXiv (Cornell University) · 2026-04-08

  articleOpen accessSenior author

  A conventional electric vehicle (EV) powertrain relies on a centralized high-voltage DC-AC inverter, thereby limiting cell-level control and potentially reducing overall driving range and battery lifetime. This paper studies an H-bridge-based cell-level inverter topology that performs power conversion at the cell level, enabling independent control of individual cells and expanding the design space for battery management. Leveraging these additional degrees of freedom, we propose a model-agnostic energy-throughput control strategy that extends EV range while improving battery-pack lifetime. Because usable energy (and thus driving range) and lifetime are governed by the cells with the lowest state-of-charge (SOC) and state-of-health (SOH), respectively, the proposed controller preferentially routes energy throughput to healthier cells. Specifically, during charging, it permits cell SOCs to diverge to promote SOH equalization; during discharging, it rebalances SOC to maximize usable capacity under per-cell constraints. The proposed SOC-SOH-aware control strategy is evaluated on two aging models representing lithium manganese oxide and lithium iron phosphate chemistries, using a Tesla Model 3 charge-discharge profile across 14 different parameter settings. Simulations show a 7-38% improvement in lifetime relative to a conventional SOC-only balancing baseline. More broadly, the results suggest a software-defined pathway to extend EV pack life through routine charging, with minimal reliance on specific degradation models or discharge profiles.

  PublisherOA PDF

• Model-Agnostic Energy Throughput Control for Range and Lifetime Extension of Electric Vehicles via Cell-Level Inverters

  arXiv (Cornell University) · 2026-04-08

  preprintOpen accessSenior author

  A conventional electric vehicle (EV) powertrain relies on a centralized high-voltage DC-AC inverter, thereby limiting cell-level control and potentially reducing overall driving range and battery lifetime. This paper studies an H-bridge-based cell-level inverter topology that performs power conversion at the cell level, enabling independent control of individual cells and expanding the design space for battery management. Leveraging these additional degrees of freedom, we propose a model-agnostic energy-throughput control strategy that extends EV range while improving battery-pack lifetime. Because usable energy (and thus driving range) and lifetime are governed by the cells with the lowest state-of-charge (SOC) and state-of-health (SOH), respectively, the proposed controller preferentially routes energy throughput to healthier cells. Specifically, during charging, it permits cell SOCs to diverge to promote SOH equalization; during discharging, it rebalances SOC to maximize usable capacity under per-cell constraints. The proposed SOC-SOH-aware control strategy is evaluated on two aging models representing lithium manganese oxide and lithium iron phosphate chemistries, using a Tesla Model 3 charge-discharge profile across 14 different parameter settings. Simulations show a 7-38% improvement in lifetime relative to a conventional SOC-only balancing baseline. More broadly, the results suggest a software-defined pathway to extend EV pack life through routine charging, with minimal reliance on specific degradation models or discharge profiles.

  PublisherDOI

• An adaptive estimation approach based on fisher information to overcome the flat voltage plateau challenges of SOC estimation in LFP batteries

  Energy and AI · 2026-02-11 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Design Guidelines for Nonlinear Kalman Filters via Covariance Compensation

  arXiv (Cornell University) · 2026-03-24

  articleOpen accessSenior author

  Nonlinear extensions of the Kalman filter (KF), such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are indispensable for state estimation in complex dynamical systems, yet the conditions for a nonlinear KF to provide robust and accurate estimations remain poorly understood. This work proposes a theoretical framework that identifies the causes of failure and success in certain nonlinear KFs and establishes guidelines for their improvement. Central to our framework is the concept of covariance compensation: the deviation between the covariance predicted by a nonlinear KF and that of the EKF. With this definition and detailed theoretical analysis, we derive three design guidelines for nonlinear KFs: (i) invariance under orthogonal transformations, (ii) sufficient covariance compensation beyond the EKF baseline, and (iii) selection of compensation magnitude that favors underconfidence. Both theoretical analysis and empirical validation confirm that adherence to these principles significantly improves estimation accuracy, whereas fixed parameter choices commonly adopted in the literature are often suboptimal. The codes and the proofs for all the theorems in this paper are available at https://github.com/Shida-Jiang/Guidelines-for-Nonlinear-Kalman-Filters.

  PublisherOA PDF

Recent grants

• CAREER: Estimation and Control of Electrochemical-Thermal Battery Models: Theory and Experiments

  NSF · $531k · 2019–2024

• Collaborative Research: Multi-Scale, Multi-Rate Spatio-Temporal Optimal Control with Application to Airborne Wind Energy Systems

  NSF · $235k · 2017–2020

• Fast Charging Batteries via Electrochemical Model-based Control

  NSF · $295k · 2014–2017

Frequent coauthors

• Hongcai Zhang

  University of Macau

  32 shared

• Dong Zhang

  Lanzhou University of Technology

  29 shared

• Sangjae Bae

  Honda (United States)

  25 shared

• Teng Zeng

  University of California, Berkeley

  22 shared

• Saehong Park

  University of California, Berkeley

  22 shared

• Hector E. Perez

  University of California, Berkeley

  21 shared

• Hosam K. Fathy

  University of Maryland, College Park

  20 shared

• Luis D. Couto

  Flemish Institute for Technological Research

  17 shared

Labs

• Energy, Controls, & Applications Lab (eCAL)PI

Education

• PhD, Mechanical Engineering

  University of Michigan

  2011

Awards & honors

• PATH Faculty Director
• Chair of Engineering Science
• UC Berkeley PATH Awarded $10M USDOT Grant for Rural Autonomo…
• PATH Awarded USDOT SMART Grant for I-40 Corridor Project
• Congratulations to the 2025 Eno Leadership Development Confe…