Haoyang Hu

A Bi ₂ Te ₃ -based thermoelectric device with high thermoelectric performance and flexibility for human body waste heat recovery and temperature management.

The thermal-electric conversion efficiency is a crucial metric for evaluating the performance of a thermoelectric generator (TEG). However, accurate measurement of this efficiency remains a significant challenge due to various factors that impact heat flow measurements. We have observed that temperature fluctuations during temperature control are the primary factor contributing to measurement errors in heat flow under vacuum conditions. To address this issue, we have developed a time-dependent theoretical model for the thermal-electric coupling of a TEG measurement system based on Fourier’s theory of heat conduction. This model allows us to investigate the effects of both temperature fluctuation and structural parameters on the measurement error of TEG performance. Furthermore, we have proposed an error correction scheme for TEG performance based on our theoretical and experimental findings. These insights provide a theoretical framework and technical guidance for more precise measurements of TEG performance.

In this study, we have designed and developed an implantable thermoelectric generator (TEG) module tailored for large-scale flue gas waste heat recovery. We also have established a test stand to simulate diverse operational conditions, and systematically examined the influence of different operating conditions, including flue gas temperature, flue gas velocity, and cooling water temperature, on the electrical performance of the TEG module. When the flue gas temperature is 139 °C, the flue gas flow rate is 3.4 m/s, and the cooling water temperature is 20 °C, the TEG module operates at its peak performance. It achieves an open-circuit voltage of 856.3 V and an output power of 150.58 W. Furthermore, the TEG module demonstrates a notable power generation capacity of 3.86 kW/m³ and a waste heat recovery capacity of 135.85 kW/m³. The results prove the TEG module as an effective solution for large-scale flue gas waste heat recovery in industrial settings, contributing to sustainable energy practices. This study supports the application of thermoelectric power generation in the industrial sector, offering significant potential for advancements in energy efficiency.

Thermoelectric technology is an effective strategy to convert low–grade waste heat to electrical energy directly. Thermoelectric generators (TEGs) have been extensively studied in various waste heat scenarios, such as vehicle exhaust, metal casting processes and more. However, industrial pipelines also possess high levels of heat and wide distribution, yet there is limited research on TEGs for use in these pipes. The challenge in designing a TEG lies in the heat collector, which is complicated by the distinct structural differences between pipe and plate–shaped TEMs. Ultimately, we propose an arch bridge–shaped heat collector for the pipe to recover wasted thermal energy. The effects of some key factors, such as topology of TEMs, heat source temperature, cooling water temperature and velocity, on the generating performance are studied. The TEG achieved a temperature difference of 65.98 °C across the two ends of the TEM, resulting in an output power of 17.89 W at an open–circuit voltage of 133.35 V. This provides evidence that the designed heat collector is a feasible solution for recovering waste heat from pipes using TEG technology. This work provides reliable experimental data and efficient design for the application of TEGs in industrial pipes.

Thermoelectric generator (TEG) has important applications in automotive exhaust waste heat recovery. The Back propagation neural network (BP) can predict the electrical generating performance of TEG efficiently and accurately due to its advantage of being good at handing nonlinear data. However, BP algorithm is easy to fall into local optimum, and its training data usually have deviation since the data are obtained through the simulation software. Both of the problems will reduce the prediction accuracy. In order to further improve the prediction accuracy of BP algorithm, we use the genetic algorithm (GA) to optimize BP neural network by selection, crossover, and mutation operation. Meanwhile, we create a TEG for the heat waste recovery of automotive exhaust and test 84 groups of experimental data set to train the GA−BP prediction model to avoid the deviation caused by the simulation software. The results show that the prediction accuracy of the GA−BP model is better than that of the BP model. For the predicted values of output power and output voltage, the mean absolute percentage error (MAPE) increased to 2.83% and 2.28%, respectively, and the mean square error (MSE) is much smaller than the value before optimization, and the correlation coefficient (R²) of the network model is greater than 0.99.

PbCl₂ doping effectively improves the ZT value of n-type SnSe_0.95 by boosting the carrier concentration and suppressing the lattice thermal conductivity.