GA−BP Prediction Model for Automobile Exhaust Waste Heat Recovery Using Thermoelectric Generator

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.

[1]  Anmin Yin,et al.  Grain size characterization of TA1 with GA-BP neural network using laser ultrasonics , 2023, Optik.

[2]  Lulu Zhang,et al.  Recent advances in modeling and simulation of thermoelectric power generation , 2022, Energy Conversion and Management.

[3]  Zhihua Wang,et al.  The investigation into the failure criteria of concrete based on the BP neural network , 2022, Engineering Fracture Mechanics.

[4]  Belqasem Aljafari,et al.  Performance optimization of a photovoltaic-segmented thermoelectric generator operating under Nigeria weather conditions using Bayesian regularized artificial neural networks , 2022, Sustainable Energy, Grids and Networks.

[5]  J. Joshi,et al.  Predictive analysis of gas holdup in bubble column using machine learning methods , 2022, Chemical Engineering Research and Design.

[6]  Yuxiao Zhu,et al.  Artificial neural network enabled accurate geometrical design and optimisation of thermoelectric generator , 2022, Applied Energy.

[7]  Bo Wu,et al.  Process optimization of high-speed dry milling UD-CF/PEEK laminates using GA-BP neural network , 2021 .

[8]  Kaifa Wang,et al.  Fast and Accurate Performance Prediction and Optimization of Thermoelectric Generators with Deep Neural Networks , 2021, Advanced Materials Technologies.

[9]  Tae Young Kim,et al.  Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks , 2021, Energies.

[10]  H. Oztop,et al.  Thermoelectric generation in bifurcating channels and efficient modeling by using hybrid CFD and artificial neural networks , 2021, Renewable Energy.

[11]  Albert Massaguer,et al.  Faster and more accurate simulations of thermoelectric generators through the prediction of the optimum load resistance for maximum power and efficiency points , 2021, Energy.

[12]  Luiz Wrobel,et al.  Thermoelectric generator (TEG) technologies and applications , 2021, International Journal of Thermofluids.

[13]  R. Sen,et al.  Multi-fold enhancement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast: an artificial neural network-genetic algorithm approach , 2020 .

[14]  Wei Hsin Chen,et al.  Geometry design for maximizing output power of segmented skutterudite thermoelectric generator by evolutionary computation , 2020 .

[15]  Liming Liu,et al.  Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network , 2020 .

[16]  Taeyoung Kim,et al.  Application of compact thermoelectric generator to hybrid electric vehicle engine operating under real vehicle operating conditions , 2019 .

[17]  Somchai Wongwises,et al.  Performance prediction of hybrid thermoelectric generator with high accuracy using artificial neural networks , 2019, Sustainable Energy Technologies and Assessments.

[18]  Roop L. Mahajan,et al.  Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator , 2018, Energies.

[19]  Lan Xiao,et al.  Theoretical analysis on a segmented annular thermoelectric generator , 2018, Energy.

[20]  Appadurai Anitha Angeline,et al.  POWER GENERATION FROM COMBUSTED “SYNGAS” USING HYBRID THERMOELECTRIC GENERATOR AND FORECASTING THE PERFORMANCE WITH ANN TECHNIQUE , 2018, Journal of Thermal Engineering.

[21]  C. Su,et al.  Energy Efficient Thermoelectric Generator-Powered Localized Air-Conditioning System Applied in a Heavy-Duty Vehicle , 2018 .

[22]  Wei Liu,et al.  Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm , 2018 .

[23]  Wai Lok Woo,et al.  Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters , 2017 .

[24]  Chen Chen,et al.  Effects of heat enhancement for exhaust heat exchanger on the performance of thermoelectric generator , 2015 .

[25]  Gequn Shu,et al.  Effect of vehicle driving conditions on the performance of thermoelectric generator , 2015 .

[26]  Fardin Ahmadizar,et al.  Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm , 2015, Eng. Appl. Artif. Intell..

[27]  Gequn Shu,et al.  Investigation and design optimization of exhaust-based thermoelectric generator system for internal combustion engine , 2014 .

[28]  Doug Crane,et al.  TEG On-Vehicle Performance and Model Validation and What It Means for Further TEG Development , 2013, Journal of Electronic Materials.

[29]  Rui Quan,et al.  A Novel Optimization Method for the Electric Topology of Thermoelectric Modules Used in an Automobile Exhaust Thermoelectric Generator , 2013, Journal of Electronic Materials.

[30]  B. Ciylan,et al.  Determination of Output Parameters of a Thermoelectric Module using Artificial Neural Networks , 2011 .

[31]  Yulong Zhao,et al.  A comprehensive hybrid transient CFD-thermal resistance model for automobile thermoelectric generators , 2023, International Journal of Heat and Mass Transfer.

[32]  Canjun Yang,et al.  Hydrothermal fluid ejector for enhanced heat transfer of a thermoelectric power generator on the seafloor , 2021, Sustainable Energy & Fuels.

[33]  Su Chuqi,et al.  Arrangement of TEG Device and Thermoelectric Module , 2010 .