Jun Lu — Research Analyst Report
Analysis Mode: fast | Analysis Time: 2026-03-16T00:14:36
Rating: Starring Star (57.2/100)
Basic Metrics
| Metric | Value |
|---|---|
| Institution | Unknown |
| h-index | 27 |
| Total Citations | 2,852 |
| Recent 5 Years Citations | 186 |
| Total Papers | 207 |
| Top Conference Papers | 13 |
| Publication Period | 1989 – 2023 |
| Semantic Scholar | 5223928 |
Research Trajectory
Jun Lu’s academic trajectory shows a clear two-stage evolution. The first stage (1989–2013) focused on numerical calculations of electromagnetic fields, especially the Harmonic Balance Finite-Element Method (HB-FEM) and the design of high-frequency planar transformers, establishing a solid methodological foundation in nonlinear magnetic field analysis. A key turning point occurred around 2013–2015: the boom in the global electric vehicle industry and policy advances in smart grids made his accumulated work in high-frequency magnetic devices and wireless charging highly industrially valuable. This shifted his research focus toward EV wireless charging, microgrid energy management, and energy storage system optimization. The second stage (2014–2023) reached its peak with a highly cited review paper published in 2018 (366 citations), establishing his representative status in the field of EV wireless charging. Notably, papers after 2022—such as those on superconducting materials, Riemann geometry, quantum networks, and insect-computer hybrid speakers—show severe author data contamination; these should be considered the work of different researchers and not included in the evaluation of his own academic trajectory.
Breakthrough Works
1. Analysis of the DC Bias Phenomenon by the Harmonic Balance Finite-Element Method (2011)
Description: Systematically applied the Harmonic Balance Finite-Element Method (HB-FEM) to the analysis of transformer DC bias phenomena, establishing a complete mathematical model that can accurately calculate harmonic distributions in nonlinear saturated magnetic fields.
Why it couldn’t be done before: Magnetic saturation caused by DC bias is a highly nonlinear problem. Traditional time-domain finite element methods require enormous computational resources and are difficult to converge; the early HB-FEM theory was immature, lacking fixed-point iterative algorithms to handle hysteresis non-linearity, and computer performance was not sufficient to support three-dimensional harmonic simultaneous solution.
Impact: Provided an authoritative simulation tool for DC bias protection and design of power system transformers. Published in IEEE Transactions on Power Delivery, it received 51 citations and became a benchmark document in this subfield.
2. Characterizations of High Frequency Planar Transformer With a Novel Comb-Shaped Shield (2011)
Description: Proposed a high-frequency planar transformer with a comb-shaped shield structure, systematically characterizing its electromagnetic shielding efficiency and eddy current loss characteristics, providing a new structural solution for on-chip power supplies and wireless charging couplers.
Why it couldn’t be done before: At high frequencies, skin effect and proximity effects are complexly intertwined, and traditional analytical models fail. Fine three-dimensional finite element simulation combined with experimental verification was required, but such precise simulation tools and microprocessing manufacturing techniques were not widespread in the early 2000s.
Impact: Received 45 citations, driving the evolution of planar transformers toward higher frequencies and higher power density, and establishing a design paradigm for integrated magnetic devices in subsequent LLC resonant converters.
3. Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks (2014)
Description: For residential low-voltage distribution networks, constructed an ARIMAX and neural network hybrid short-term load forecast model, identifying key exogenous variables that affect demand, and achieving accurate prediction of total electricity consumption and peak demand the next day.
Why it couldn’t be done before: End-user behavior at the end points of low-voltage distribution networks is highly random, and traditional statistical models lack sufficient generalization ability. Deep learning was not yet widespread, early neural networks faced overfitting and training difficulties; only with the large-scale deployment of smart meters and the first access to high-resolution historical electricity data, data-driven modeling became possible.
Impact: Received 80 citations, becoming an important benchmark work in distribution network demand forecasting, directly triggering research on prediction modules in subsequent microgrid energy management systems.
4. Review of static and dynamic wireless electric vehicle charging system (2018)
Description: Systematically reviewed static and dynamic wireless electric vehicle charging technologies, covering induction coupling principles, coil design, power electronics topologies, interoperability standards, and safety, and proposed a technical roadmap.
Why it couldn’t be done before: The publication relied on a large accumulation of original research on wireless charging technology moving from laboratory to industrialization between 2012–2018; the maturity of the Qi standard and the release of SAE J2954 drafts made standardization discussions possible; Jun Lu’s ten-year accumulation in planar transformers, induction couplers, and EV charging systems enabled him to conduct authoritative synthesis.
Impact: Received 366 citations, being the most influential work in the paper list, establishing Jun Lu as a representative scholar in EV wireless charging, and being cited by many subsequent studies as a review entry.
5. Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform (2020)
Description: Used the best-basis stationary wavelet packet transform for load signal decomposition, combined with a feedforward neural network tuned by the Harris-Hawk Optimization algorithm (HHO), to construct a dedicated short-term load forecasting framework for microgrid energy management.
Why it couldn’t be done before: The HHO algorithm was introduced in 2019—a relatively new meta-heuristic optimization tool at that time; hybrid prediction frameworks combining deep learning and intelligent optimization algorithms required sufficient computational resources and standardized microgrid operation datasets, both of which were insufficient before 2019.
Impact: Received 105 citations, being the most influential original work in the intersection of forecasting and energy management by the author, establishing a three-stage prediction framework of “signal decomposition + meta-heuristic optimization + neural network” in the microgrid field.
Research Directions
- Harmonic Balance Finite-Element Method (HB-FEM) and nonlinear electromagnetic field analysis
- High-frequency planar transformer and integrated magnetic device design
- Electric vehicle wireless charging technologies (static and dynamic)
- Microgrid energy management, energy storage optimization and control
- Short-term load forecasting and wind power prediction (data-driven methods)
- Smart grid and Vehicle-to-Grid (V2G) systems
Methodological Evolution
Jun Lu’s methodology has undergone three paradigm shifts. The first stage (1989–2012) centered on mathematical analysis: the Harmonic Balance method transformed nonlinear differential equations into complex algebraic equations, combined with finite element discretization, forming the unique computational electromagnetics toolchain of HB-FEM; the evolution marked by upgrading from simple harmonic truncation to fixed-point iterative and neural network-assisted hysteresis modeling. The second stage (2013–2018) shifted toward system-level modeling and control: research scope expanded from magnetic devices to power electronics converter topologies and microgrid hierarchical control architectures; methodology extended from numerical simulation (FEM) to experimental platform verification and hardware-in-the-loop testing, and MEMS radio frequency switch and wireless charging coupler design also incorporated microprocessing perspectives. The third stage (2019–2023) fully introduced a data-driven paradigm: the combination of wavelet packet decomposition, meta-heuristic optimization (HHO, GWO, SSA) and deep neural networks (LSTM) reflects the major trend toward migration of energy system research to machine learning methods. The author flexibly applied signal processing experience to time-series prediction problems, maintaining methodological continuity.
Domain Impact
Jun Lu’s core contributions to the field are reflected at two levels: at the basic method level, his HB-FEM series works (1989–2017) provided mature computational tools for DC bias and high-frequency nonlinear magnetic field analysis of power transformers. Related results were published in top journals such as IEEE Transactions on Magnetics and IEEE Transactions on Power Delivery, offering continuous academic reference value; at the application level, the 2018 EV wireless charging review (366 citations) became a must-read review for new researchers in this field due to its systematicness and timeliness, objectively playing a role in setting research agendas. Overall, Jun Lu is a mid-tier scholar oriented toward engineering applications: with an h-index of 27 and approximately 2852 total citations, he has stable academic influence in the intersection of power electronics and smart grids. However, he has only 13 top conference papers, and recent 5-year citations (186 times) are relatively limited, indicating that his peak period has passed, and current influence mainly relies on long-tail citations from historical accumulation.
Highly Cited Papers (Top 20)| # | Year | Citation | Title |
|—|——|——|——| | 1 | 2018 | 366 | Review of static and dynamic wireless electric vehicle charging systems | | 2 | 2020 | 105 | Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform | | 3 | 2014 | 80 | Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks | | 4 | 2023 | 78 | Optimized Forecasting Model to Improve the Accuracy of Very Short-Term Wind Power Prediction | | 5 | 2017 | 75 | Aggregated applications and benefits of energy storage systems with application-specific control methods: A review | | 6 | 2018 | 75 | A hybrid AC/DC microgrid control system based on a virtual synchronous generator for smooth transient performances | | 7 | 2015 | 69 | Development of a three-phase battery energy storage scheduling and operation system for low voltage distribution networks | | 8 | 2016 | 64 | Coordinated control of three-phase AC and DC type EV–ESSs for efficient hybrid microgrid operations | | 9 | 2014 | 58 | Forecasting low voltage distribution network demand profiles using a pattern recognition based expert system | | 10 | 2020 | 54 | A Multifunctional Single-Phase EV On-Board Charger With a New V2V Charging Assistance Capability | | 11 | 2018 | 52 | A unified multi-functional on-board EV charger for power-quality control in household networks | | 12 | 2011 | 51 | Analysis of the DC Bias Phenomenon by the Harmonic Balance Finite-Element Method | | 13 | 2020 | 49 | Multi-objective energy storage capacity optimisation considering Microgrid generation uncertainties | | 14 | 2020 | 48 | Resiliency analysis of electric distribution networks: A new approach based on modularity concept | | 15 | 2011 | 45 | Characterizations of High Frequency Planar Transformer With a Novel Comb-Shaped Shield | | 16 | 2016 | 44 | Hierarchical controls selection based on PV penetrations for voltage rise mitigation in a LV distribution network | | 17 | 2018 | 42 | A Need-Based Distributed Coordination Strategy for EV Storages in a Commercial Hybrid AC/DC Microgrid With an Improved Interlinking Converter Control Topology | | 18 | 2017 | 39 | Improved Neutral Current Compensation With a Four-Leg PV Smart VSI in a LV Residential Network | | 19 | 2021 | 38 | Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach | | 20 | 2018 | 35 | Hybrid AC/DC Microgrid testing facility for energy management in commercial buildings |