Annette von Jouanne, Ph.D.
Professor Baylor University
Media
Biography
Areas of Expertise
Accomplishments
Best Paper of the Year Award
2021
Energies Journal 2019
Prize Paper Award
2023
ECCE 2022
Top 2% most cited researcher in higher ed history in the world
Stanford Database
Education
Southern Illinois University Carbondale
B.S.
Electrical Engineering
1990
Southern Illinois University Carbondale
M.S.
Electrical Engineering/Power Systems
1992
Texas A&M University
Ph.D.
Electrical Engineering/Power Electronics
1995
Media Appearances
Professor partners with U.S. Navy, researches all-electric ship
Baylor Lariat online
2022-03-30
Dr. Annette von Jouanne, professor of electrical and computer engineering, partnered with the U.S. Navy on the development of electric ships and how sustainable energy relates to transportation.
Von Jouanne said that her life is driven by her Christian faith and that she sees energy as a means of helping people, especially in a sustainable way that provides for our current needs without compromising the needs of future generations.
Research Grants
Insulation Life Prediction for Silicon Carbide (SiC) Motor-Drive Systems
Navy (ONR)
2023–2025
Microscale Onboard Integrated Condition Assessment
STTR (DOD)
2024-2025
Optimized Four-leg Inverter for Advanced SiC Motor Drive Applications with CM Voltage Elimination, Zero Damaging EDM Bearing Currents and Full Torque Capabilities
Navy (ONR)
2024–2026
Articles
Comprehensive Modeling of SiC Inverter Driven Form Wound Motor Coil for Insights on Coil Insulation Stress
Energies2025
This paper comprehensively presents an approach for modeling form wound coils of a motor driven by an inverter, with focus on the electric stresses on the coil insulation. A 10 kV SiC testbed for medium voltage form wound coils was developed to support and validate the modeling techniques discussed. A finite element analysis (FEA) model of the motor coil is presented using COMSOL 6.1. The FEA model was used to determine parameters for an electrical model based on the multi-conductor transmission line theory. The linking of these models allows for a rapid analysis of the electrical stresses the insulation can be exposed to. An experimental method for model validation using the empirical transfer function estimation (ETFE) approach to find the impedance response of the testbed for comparison to the proposed electrical model is presented and employed.
Review of Electrochemical Systems for Grid Scale Power Generation and Conversion: Low-and High-Temperature Fuel Cells and Electrolysis Processes
Energies2025
his review paper presents an overview of fuel cell electrochemical systems that can be used for clean large-scale power generation and energy storage as global energy concerns regarding emissions and greenhouse gases escalate. The fundamental thermochemical and operational principles of fuel cell power generation and electrolyzer technologies are discussed with a focus on high-temperature solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs) that are best suited for grid scale energy generation. SOFCs and SOECs share similar promising characteristics and have the potential to revolutionize energy conversion and storage due to improved energy efficiency and reduced carbon emissions. Electrochemical and thermodynamic foundations are presented while exploring energy conversion mechanisms, electric parameters, and efficiency in comparison with conventional power generation systems.
A Review of Stator Insulation State-of-Health Monitoring Methods
Energies2025
Tracking the state of the health of electrical insulation in high-power electric machines has always been a topic of great interest due to the high cost of downtime associated with unexpected failures. Over the years, there have been continuous efforts to develop and improve upon methods for testing and categorizing the health and expected lifetime of stator insulation. Methods such as partial discharge, surge, and dissipation factor testing are common examples. With the increasing use of high-specific-power electric machines in new applications such as traction and wind power generation, coupled with the increasing use of wide-bandgap semiconductor device-based inverters, some traditional methods for insulation health tracking may need adjustments or be combined with newer methods to remain accurate and useful.
A review of offshore renewable energy for advancing the clean energy transition
Energies2025
Offshore renewable energy resources are abundant and widely available worldwide, offering significant contributions to the clean energy net-zero carbon emission targets. This paper reviews strong and emerging offshore renewable energy sources, including wind (fixed bottom and floating), hydrokinetic wave and tidal energy, floating solar photovoltaics (FPVs) and hybrid energy systems. A literature review of recent sources yields a timely comprehensive comparison of the levelized cost of electricity (LCOE), technology readiness levels (TRLs), capacity factors (CFs) and global generation installed and potential, where offshore wind is recognized as being the strongest contributor to the clean energy transition and thus receives the most attention. Offshore wind grid integration, converter technologies, criticality, resiliency and energy storage integration are presented, in addition to challenges and research directions.
A Review of AI Applications in Lithium-Ion Batteries: From State-of-Health Estimations to Prognostics
Energies2026
Battery management systems (BMSs) are integral components of electric vehicles (EVs), as they ensure the safe and efficient operation of lithium-ion batteries. State of health (SoH) estimation is one of the core functions of BMSs, providing an assessment of the current condition of a battery, while prognostics aim to predict remaining useful life (RUL) as a function of the battery’s condition. An accurate SoH estimation allows proactive maintenance to prolong battery lifespan. Traditional SoH estimation methods can be broadly divided into experiment-based and model-based approaches. Experiment-based approaches rely on direct physical measurements, while model-driven approaches use physics-based or data-driven models. Although experiment-based methods can offer high accuracy, they are often impractical and costly for real-time applications. With recent advances in artificial intelligence (AI), deep learning models have emerged as powerful alternatives for SoH prediction.
