Performance evaluation of an automotive thermoelectric generator with inserted fins or dimpled-surface hot heat exchanger

Wang, Yiping; Li, Shuai; Xie, Xu; Deng, Yadong; Liu, Xun; Su, Chuqi

doi:10.1016/j.apenergy.2018.02.176

Cited by 125 publications

(45 citation statements)

References 33 publications

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“…The use of thermal spreaders, as well as practical constraints in terms of the location where the ATEG can be effectively mounted in the engine, may reduce the available temperature anticipated on the hot side [12]. A strategy intended to maintain the uniformity of the working conditions for each one of the TEMs that forms an ATEG consists of dividing the whole system into identical subassemblies that are mounted in parallel along the exhaust line [13]. Wang et al [13] applied this methodology to build an ATEG with four plate heat exchangers, each one with 60 TEMs.…”

Section: Thermoelectric Generatorsmentioning

confidence: 99%

“…A strategy intended to maintain the uniformity of the working conditions for each one of the TEMs that forms an ATEG consists of dividing the whole system into identical subassemblies that are mounted in parallel along the exhaust line [13]. Wang et al [13] applied this methodology to build an ATEG with four plate heat exchangers, each one with 60 TEMs. They reported that the inner structure that provided the best heat transfer with the lowest pressure drop, was the one that used dimples instead of fins.…”

Section: Thermoelectric Generatorsmentioning

confidence: 99%

“…We monitored the following information: engine torque, engine speed (rpm), exhaust mass flow rate, exhaust gas temperature at both ATEG inlet and outlet, water cooling volumetric flow rate, water cooling temperatures at both ATEG inlet and outlet, ambient temperature, ATEG output voltage, and output current (see Figure 2). The HSHE was connected to the circular cross-sectional area of the exhaust pipes (50 mm in diameter) by expansion and contraction elements (#4 in Figure 1a) very similar to those employed in other types of ATEGs whose designs are based on TEMs located on two parallel plates (e.g., References [6,13,15]). The overall dimensions of the ATEG were 440 × 200 × 20 mm (length × width × height) and a total weight of 7 kg.…”

Section: Experimental Set-upmentioning

confidence: 99%

“…That said, in fuel consumption analysis, it is critical to correctly determine the energy losses through the ATEG. Indeed, the increase in the upstream pressure once the ATEG is installed in the exhaust (or, equivalently, the head losses experienced by the exhaust gas when moving through the ATEG) becomes one of the main ATEG design parameters [6,10,13]. These head losses can be directly related to the back pressure, which is defined as the pressure difference between the ATEG inlet (upstream, high pressure) and the ATEG outlet (downstream, low pressure).…”

Section: Model Validationmentioning

confidence: 99%

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Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

Comamala

Cózar

Massaguer³

et al. 2018

Energies

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The need for more sustainable mobility promoted research into the use of waste heat to reduce emissions and fuel consumption. As such, thermoelectric generation is a promising technique thanks to its robustness and simplicity. Automotive thermoelectric generators (ATEGs) are installed in the tailpipe and convert heat directly into electricity. Previous works on ATEGs mainly focused on extracting the maximum amount of electrical power. However, the back pressure caused by the ATEG heavily influences fuel consumption. Here, an ATEG numerical model was first validated with experimental data and then applied to investigate the effects that modifying the main ATEG design parameters had on both fuel economy and output power. The cooling flow rate and the geometrical dimensions of the heat exchanger on the hot side and the cold side of the ATEG were varied. The design that produced the maximum output power differed from that which maximized fuel economy. Back pressure was the most limiting factor in attaining fuel savings. Back pressure values lower than 5 mbar led to a < 0.2% increase in fuel consumption. In the ATEG design analyzed here, the generation of electrical output power reduced fuel consumption by a maximum of 0.5%.

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Section: Thermoelectric Generatorsmentioning

confidence: 99%

Section: Thermoelectric Generatorsmentioning

confidence: 99%

Section: Experimental Set-upmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

See 2 more Smart Citations

Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

Comamala

Cózar

Massaguer³

et al. 2018

Energies

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show abstract

“…The thermoelectric generators convert the thermal energy into the electricity using the Seebeck effect of semiconductor materials [2]. The automobile vehicles are the heavy consumers of the fossil fuel and approximately 30%-40% of heat is lost as the vehicle exhaust [3]. The significant research works have been done on the applications of the thermoelectric generators in the automobile vehicle for waste heat recovery.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

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Optimal Design and Performance Analysis of Thermoelectric Power Generation Device Based on Multi‐Objective Genetic Algorithm

Chen

Dou

et al. 2021

Advcd Theory and Sims

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The combined use of flue gas waste heat resources and thermoelectric generators (TEGs) is considered to be a relatively reliable method to generate electricity. The focus of this study is on the optimization and improvement of the hot‐end heat collection pipe. This paper aims to increase the temperature difference between hot and cold ends of TEG and enhance uniformity of temperature distribution, thereby improving the output power of the TEG system. To balance the temperature difference between the cold and hot ends of the TEG, the pressure drop from the inlet to the outlet and the mass of TEG hot end pipes, a finite element simulation model is constructed. Meanwhile, a multi‐objective genetic algorithm is applied to optimize the structure of four dimensions: fin bottom length, fin height, fin thickness, and outlet diameter. Three optimization objectives, namely, average temperature difference, total pressure drop from inlet to outlet, and pipeline mass are globally optimized to determine the best size, based on which the accuracy of the simulation model is verified by conducting experiments.

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Performance evaluation of an automotive thermoelectric generator with inserted fins or dimpled-surface hot heat exchanger

Cited by 125 publications

References 33 publications

Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

Effects of Design Parameters on Fuel Economy and Output Power in an Automotive Thermoelectric Generator

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

Optimal Design and Performance Analysis of Thermoelectric Power Generation Device Based on Multi‐Objective Genetic Algorithm

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