What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Artificial intelligence
- Control theory
His primary scientific interests are in State of charge, Battery, Control theory, Energy management and Voltage.
His State of charge research is multidisciplinary, incorporating perspectives in Kalman filter, Electronic engineering, Online model and Robustness.
His Battery research integrates issues from Probabilistic logic, Artificial intelligence, Simulation and Lithium.
His biological study spans a wide range of topics, including Electric vehicle and Algorithm.
His studies deal with areas such as Control engineering, Artificial neural network and Dynamic programming as well as Energy management.
His work on Equivalent circuit as part of general Voltage study is frequently linked to Schedule, bridging the gap between disciplines.
His most cited work include:
- Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach (534 citations)
- State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model (445 citations)
- A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles (308 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Energy management, Electric vehicle, Battery and Automotive engineering.
Control theory is often connected to Benchmark in his work.
His research integrates issues of Model predictive control, Dynamic programming, Control engineering, Fuel efficiency and Driving cycle in his study of Energy management.
He combines subjects such as Powertrain, Control and Energy consumption with his study of Electric vehicle.
His Battery research incorporates themes from Kalman filter and Voltage.
The various areas that Hongwen He examines in his State of charge study include Equivalent circuit, Electronic engineering, Simulation and State.
He most often published in these fields:
- Control theory (39.37%)
- Energy management (30.32%)
- Electric vehicle (29.41%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (39.37%)
- Energy management (30.32%)
- Automotive engineering (27.60%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Control theory, Energy management, Automotive engineering, Electric vehicle and Battery.
His work deals with themes such as State of charge, Driving cycle and Short circuit, which intersect with Control theory.
His State of charge study incorporates themes from Recursive least squares filter, Lithium-ion battery, Extended Kalman filter and Equivalent circuit.
The Extended Kalman filter study combines topics in areas such as Battery management systems and Voltage.
His Energy management study combines topics in areas such as Model predictive control, Artificial neural network, Dynamic programming, Markov chain and Fuel efficiency.
The study incorporates disciplines such as Supercapacitor, Hybrid power, Service life and Lossless compression in addition to Battery.
Between 2019 and 2021, his most popular works were:
- Noise-Immune Model Identification and State-of-Charge Estimation for Lithium-Ion Battery Using Bilinear Parameterization (35 citations)
- A noise-tolerant model parameterization method for lithium-ion battery management system (30 citations)
- Hydrogen circulation system model predictive control for polymer electrolyte membrane fuel cell-based electric vehicle application (12 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Artificial intelligence
- Control theory
His scientific interests lie mostly in Electric vehicle, Control theory, Automotive engineering, Battery and Model predictive control.
His Control theory study combines topics from a wide range of disciplines, such as State of charge, Electric motor and Energy consumption.
His State of charge study incorporates themes from Genetic algorithm, Dynamic programming and Hybrid vehicle.
His studies in Automotive engineering integrate themes in fields like Artificial neural network, Energy recovery and Control theory.
His study in Battery is interdisciplinary in nature, drawing from both Deep learning, Artificial intelligence, State and Trajectory.
His Model predictive control study also includes fields such as
- Proton exchange membrane fuel cell together with Flow control valve and Electrolyte,
- Hardware-in-the-loop simulation that intertwine with fields like Energy management, Electricity generation, Hydrogen consumption and Hybrid power.
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