Thermoelectric generators (TEG) convert the thermal energy into electrical energy and are under investigation as a power supply for medical implants. To improve the performance of TEG, the design optimization process through finite element model simulation is preferred by biomedical engineers. This paper aims to provide an efficient method of speeding up the design optimization process of TEG. A three‐dimensional realistic human torso model incorporating the TEG is investigated, where the internal heat transfer in human tissue is characterized by Pennes bioheat equation. In addition, convection, radiation, and evaporation effects at the skin surface are applied to identify the heat transfer effects between the human body and the environment. To speed up finite element simulation of the large‐scale human torso model, projection‐based model order reduction (MOR) is applied for generation of a compact but highly accurate model. Parametric MOR (pMOR) further enables generating a parameter‐independent compact model. For an efficient design optimization of TEG, this compact human torso model is applied within a thermal submodeling approach. Its temperature distribution results are back‐projected and used as boundary conditions for the TEG submodel. The achieved speed‐up in simulation time, demonstrated in this work, clearly indicates that the design optimization process of TEG is more efficient with the combination of MOR and submodeling techniques.

In this paper, we present a macroscale multiresonant vibration-based energy harvester. The device features frequency tunability through magnetostatic actuation on the resonator. The magnetic tuning scheme uses external magnets on linear stages. The system-level model demonstrates autonomous adaptation of resonance frequency to the dominant ambient frequencies. The harvester is designed such that its two fundamental modes appear in the range of (50,100) Hz which is a typical frequency range for vibrations found in industrial applications. The dual-frequency characteristics of the proposed design together with the frequency agility result in an increased operative harvesting frequency range. In order to allow a time-efficient simulation of the model, a reduced order model has been derived from a finite element model. A tuning control algorithm based on maximum-voltage tracking has been implemented in the model. The device was characterized experimentally to deliver a power output of 500 µW at an excitation level of 0.5 g at the respected frequencies of 63.3 and 76.4 Hz. In a design optimization effort, an improved geometry has been derived. It yields more close resonance frequencies and optimized performance.

This research focuses on the design of a miniaturized thermoelectric generator (TEG) for electrically active implants. Its design optimization is performed using the finite element method. A simplified TEG model is obtained by replacing the thermocouple array with a single representative thermopile, which considers the number and fill factor of the thermocouples as parameters. Instead of rebuilding the geometry of a detailed model with multiple thermocouples, the simplified model adapts the material properties of its representative thermopile, facilitating design optimization. We extend the model by integrating the simplified TEG together with a housing inside a human tissue model for thermoelectric analysis. For computation efficiency and applicability of model order reduction (MOR), a thermal model is derived from the thermoelectric one, with the Peltier effect being considered through an effective thermal conductivity. Through parametric MOR, two parametric reduced‐order models are generated from the full‐scale thermoelectric and thermal model, respectively. Furthermore, we demonstrate the design optimization of TEG both in full‐scale and reduced‐order model for maximal power output and sufficient voltage output.

Energy harvesting, a solution to provide a lifetime power supply to wireless sensor nodes, has attracted widespread attention in the last two decades. An energy harvester collects ambient energy, e.g., solar, thermal, or vibration energy, and transforms it into electrical energy. In this work, we work on an electromagnetic energy harvester model, which is composed of four magnets oscillating along a coil. Such a device converts the vibrational energy into electrical energy. We reproduce the electromagnetic energy harvester model in finite element-based software. In order to include this model in a system-level simulation, the methodology of extracting a look-up table-based equivalent circuit model is presented. Such an equivalent circuit model enables the interaction of the electromagnetic energy harvester model with both electrical and mechanical compact models at the system-level. Furthermore, the matrix interpolation-based and algebraic parameterization-based parametric model order reduction methods are suggested for speeding up the generation of the equivalent circuit model and the design optimization process with respect to magnet dimensions. The efficiencies of these two methods are investigated and compared.