H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Electronics and Electrical Engineering D-index 30 Citations 5,175 97 World Ranking 3630 National Ranking 1395

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Electrical engineering
  • Mathematical optimization

His primary areas of study are Battery, Automotive engineering, Electric vehicle, Energy management and Power management. His Battery study combines topics from a wide range of disciplines, such as Electronic engineering, Control theory and Voltage. His research investigates the link between Automotive engineering and topics such as Electrical engineering that cross with problems in Battery degradation, Power capacity, Range anxiety and Powertrain.

His Electric vehicle research includes elements of Simulation and Electricity. His studies in Electricity integrate themes in fields like Stochastic programming, Driving cycle and Hybrid vehicle. Scott J. Moura works mostly in the field of Power management, limiting it down to concerns involving Stochastic control and, occasionally, Stand-alone power system and Algorithm design.

His most cited work include:

  • A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles (436 citations)
  • Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity (283 citations)
  • Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (250 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Battery, Control theory, Mathematical optimization, Electric vehicle and Automotive engineering. His Battery research is multidisciplinary, relying on both Electronic engineering and Optimal control. The study incorporates disciplines such as Control engineering, Partial differential equation and Voltage in addition to Control theory.

His Mathematical optimization research is multidisciplinary, incorporating elements of Power, Electric power system, Dynamical systems theory, Discrete choice and Control. His Electric vehicle research includes themes of Flow network, Simulation, Electricity and Operations research. His research investigates the connection between Automotive engineering and topics such as Power management that intersect with problems in Stochastic control, Fuel efficiency and Hybrid vehicle.

He most often published in these fields:

  • Battery (28.40%)
  • Control theory (28.40%)
  • Mathematical optimization (27.81%)

What were the highlights of his more recent work (between 2019-2021)?

  • Mathematical optimization (27.81%)
  • Battery (28.40%)
  • Electric vehicle (21.30%)

In recent papers he was focusing on the following fields of study:

Scott J. Moura mainly investigates Mathematical optimization, Battery, Electric vehicle, Control theory and State of charge. The various areas that Scott J. Moura examines in his Mathematical optimization study include Reduction and Reinforcement learning. His biological study spans a wide range of topics, including State and Optimal control.

His work deals with themes such as Operations research and Discrete choice, which intersect with Electric vehicle. Scott J. Moura regularly ties together related areas like Partial differential equation in his Control theory studies. His biological study spans a wide range of topics, including Convergence, Ordinary differential equation and Nonlinear system.

Between 2019 and 2021, his most popular works were:

  • Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles (16 citations)
  • Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas (12 citations)
  • Optimal Eco-Driving Control of Connected and Autonomous Vehicles Through Signalized Intersections (10 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Mathematical optimization
  • Electrical engineering

Scott J. Moura mostly deals with Battery, Mathematical optimization, Optimal control, State of charge and Control. Scott J. Moura combines subjects such as Key and Lithium with his study of Battery. In Mathematical optimization, Scott J. Moura works on issues like Reduction, which are connected to Approximation algorithm and State.

His Optimal control research is multidisciplinary, incorporating perspectives in Recurrent neural network, Change control and Artificial intelligence. His studies deal with areas such as Convergence, Reliability engineering, Cascade and Nonlinear system as well as State of charge. His work in Nonlinear system tackles topics such as Electric current which are related to areas like Control theory.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

S J Moura;H K Fathy;D S Callaway;J L Stein.
IEEE Transactions on Control Systems and Technology (2011)

638 Citations

Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity

Saeid Bashash;Scott J. Moura;Joel C. Forman;Hosam K. Fathy.
Journal of Power Sources (2011)

382 Citations

Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

Chao Sun;Xiaosong Hu;Scott J. Moura;Fengchun Sun.
IEEE Transactions on Control Systems and Technology (2015)

325 Citations

Blockchains for decentralized optimization of energy resources in microgrid networks

Eric Munsing;Jonathan Mather;Scott Moura.
2017 IEEE Conference on Control Technology and Applications (CCTA) (2017)

254 Citations

Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array

Xiaohua Wu;Xiaosong Hu;Scott Moura;Xiaofeng Yin.
Journal of Power Sources (2016)

242 Citations

Genetic identification and fisher identifiability analysis of the Doyle–Fuller–Newman model from experimental cycling of a LiFePO4 cell

Joel C. Forman;Scott J. Moura;Jeffrey L. Stein;Hosam K. Fathy.
Journal of Power Sources (2012)

222 Citations

Quantifying EV battery end-of-life through analysis of travel needs with vehicle powertrain models

Samveg Saxena;Caroline Le Floch;Jason MacDonald;Scott Moura.
Journal of Power Sources (2015)

215 Citations

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Chao Sun;Scott Jason Moura;Xiaosong Hu;J. Karl Hedrick.
IEEE Transactions on Control Systems and Technology (2015)

210 Citations

Adaptive Partial Differential Equation Observer for Battery State-of-Charge/State-of-Health Estimation Via an Electrochemical Model

Scott J. Moura;Nalin A. Chaturvedi;Miroslav Krstić.
Journal of Dynamic Systems Measurement and Control-transactions of The Asme (2014)

206 Citations

Integrated Optimization of Battery Sizing, Charging, and Power Management in Plug-In Hybrid Electric Vehicles

Xiaosong Hu;Scott J. Moura;Nikolce Murgovski;Bo Egardt.
IEEE Transactions on Control Systems and Technology (2016)

184 Citations

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