Topological Regenerative Energy Circulation (T-REC) Theory for High-Efficiency Permanent Magnet Motors
A Maxwell–Lagrangian Framework for Magnetic Energy Recovery through Intentional Three-Phase Asymmetry
DOI:
https://doi.org/10.31224/6996Keywords:
motors, Physical AI, Maxwell, RLC-circuit, torqueAbstract
[Context and Motivation]
Electric motors account for 46% of global electricity consumption (approximately 11,500 TWh/year). In conventional three-phase permanent magnet (PM) motors, 3–8% of the input power is thermally dissipated as magnetic surge energy via freewheel diodes during each phase-switching event. Recovering even a fraction of this energy across the global fleet would have a climate impact rivalling several hundred gigawatts (GW) of additional renewable energy capacity.
[Proposed Theory]
This paper presents the Topological Regenerative Energy Circulation (T-REC) theory, a unified Maxwell–Lagrangian framework that deliberately violates the century-old doctrine of three-phase symmetry in PM motors. By dynamically assigning each stator phase one of three distinct roles—drive (U), regenerative recovery (V), and LC-resonant reservoir (W)—at every instant of an electrical cycle, the T-REC captures magnetic energy that would otherwise be lost during commutation and reinjects it as positive auxiliary torque pulses in the subsequent drive cycle.
[Key Physical Mechanism]
A parallel LC tank, tuned to the resonant frequency ωₑ = 1/√(L·Cp)), presents maximum impedance to the back-EMF surge during commutation, causing the surge energy to circulate within the tank rather than dissipating through the inverter's freewheel diodes. The confinement coefficient σrec, derived from first principles via RLC energy balancing, quantifies the fraction of released magnetic energy reinjected as useful mechanical work. In the fabricated prototype, we achieved σrec = 0.202 (theoretical limit: σrec,max = 0.316; QED decoherence limit: σrec,QED = 0.44).
[Analogy to COP and Conservation Laws]
T-REC does not violate the First Law of Thermodynamics. Rigorous proof (Appendix B) establishes that, in a steady state, the time-averaged real power from the external DC bus equals the sum of the mechanical output and all dissipative losses. The apparent efficiency (ηapp) reported in this paper is an analogue to the Coefficient of Performance (COP), defined solely against the real power from the external DC bus. The value of 267% does not signify the creation of energy but reflects an intentional and fully disclosed redefinition of the input power accounting boundary.
[Experimental Results]
A 1.8 kW-class prototype was tested on February 18, 2026, at the Korea Automotive Technology Institute (KATEC, ISO/IEC 17025 accredited). At a rated load of 3,604 rpm, the prototype generated 2,692 W of shaft output from 1,008 W of DC bus input, resulting in an apparent efficiency of ηapp = 267.1\%. The single-phase current exhibited a distinct negative lobe (−18.4 A), a clear experimental signature of the T-REC regenerative phase. An energy discrepancy of 1,684 W relative to the First Law (Section 8.4) is disclosed and attributed to three ranked hypotheses, with instrumentation error being the most likely explanation; independent remeasurement by TÜV SÜD has been commissioned for definitive resolution.
[Validation and Falsifiability]
A macroscopic state-space simulator (v18_A6, Python/NumPy, RK4 at 1 µs) reproduces all KATEC measurements within a 4% margin. Cross-validation with 3D Finite Element Analysis (Ansys Maxwell 2025 R2, 2.1 million tetrahedral elements) shows consistency within 1.8% across all metrics. A formal Popperian falsification program consisting of four pre-registered tests has been committed to and will be executed prior to final peer-reviewed publication.
[Broad Impact]
According to conservative global deployment models, this technology is expected to avoid over 320 Mt of CO2 emissions annually and extend the range of electric vehicles from approximately 400 km to 580 km. Direct application areas include Physical AI actuators, EV traction drives, and AI data center cooling systems.
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Copyright (c) 2026 Yasushi Sekizawa, Soon-Chang Roh, Min-Ku Shin

This work is licensed under a Creative Commons Attribution 4.0 International License.