Measurement and evaluation of produced energy by thermoelectric generator in vehicle
Introduction
Replacing traditional power generators with renewable technologies and recovering the wasted energies are the most efficient strategies to reduce fossil fuels consumption. Recovering wasted heat from different resources improves energy efficiency and resolves the global warming problem [1], [2]. In order to achieve the energy recovery from thermal losses, thermoelectric generator (TEG) technology is utilized recently. As vehicles are one of the important pollution sources, the heat recovery application on vehicles has been investigated to reduce their consumption [3], [4], [5], [6], [7], [8], [9], [10]. Experts in BMW company have focused on the recovery of wasted heat from exhaust gas and engine radiator that reduces the fuel consumption about 8–12.5% [11]. A novel hybrid energy system is investigated with the presence of TEG for air conditioning in commercial vehicles [12]. The results show that the power consumption is decreased about 45% by the presented strategy. The impact of vehicle driving conditions on the operation of TEG has been studied and a numerical model of TEG for recovery the heat loss of exhaust is developed [13]. Reference [14] investigates performance and energy optimization of a TEG for a hybrid vehicle. The paper considers different aspects such as proposing a net power model and improving engine efficiency.
Moreover, applications and characteristics of TEG have been studied in many aspects such as determining TEG characteristics [15], [16], [17]. In [15], measurements are focused on TEG operation for various loads [15]. Indeed, the operation curve of TEG for different conditions is extracted and analyzed. The output power of TEG and the effects of temperature variations on the power have been investigated using controllable cold and hot sources [16]. The main drawback of the presented system is its expensive setup. Important parameters of TEG including Seebeck coefficient, electrical conductivity and Hall Effect are determined for temperature among 100–600 °K [17]. The constructed system is able to determine the mentioned parameters for each TEG with simple structure. Reference [18] proposes an experimental model to obtain the operation curve (i.e., V-I curve) of TEG. The V-I curve is achieved by measuring short circuit current, open circuit voltage and maximum output power [18]. The impacts of thermal substrate resistance on the TEG’s characteristic have been studied by [19]. Reference [20] utilizes TEG as a self-powered sensor by using generated current for power itself and transmit the information.
In order to analyze operation of TEG, finite element method (FEM) is utilized in the previous studies. To model the produced electrical power from thermal losses in vehicles, coupling: Navier-Stokes, heat transfer and turbulence equations is used [21]. Time dependent analysis with FEM for a case study is time consuming. Different methods such as geometry simplifying and reducing the calculation details are considered to overcome the problem. In the FEM-based studies, environmental conditions are not considered as an important parameter [22]. Due to the mentioned points, proposing an accurate and adequate method seems necessary. Hence, the online data measurement is the best way which considers all the effective features directly.
In this paper, a practical measurement system is prepared for registering the temperature on hot parts of vehicle such as exhaust and brake discs. The proposed system measures and registers temperature instantaneously for different conditions with considering all effective variables (such as cross wind, vehicle speed and number of brakes). The obtained results are used to calculate expected values of produced electrical power by TEG. In order to reduce the amount of data registering in various conditions, adaptive neuro fuzzy inference system (ANFIS) is utilized as an intelligent predictive method. This method uses measured temperatures in different conditions to construct the fuzzy-neural network and then predicts produced power in new situations. To achieve the output power in the measured temperatures, an experimental setup consisting of a controllable heat source is used. Moreover, due to lack of Seebeck coefficient value of the utilized TEG, a real strategy is implemented to calculate the mentioned parameter. It is noteworthy that the paper considers a real data measurement method with an accurate system for different conditions. In this regard, the key points and main advantages of the presented work are:
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Proposing an online measurement system with low cost, simple structure and components availability.
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Considering all of the environmental effects on the temperature measurement.
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Acceptable reliability and sufficient options of the measurement system to meet various measurement demands.
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Using ANFIS as an intelligent method to predict more conditions.
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Comparing the practical results with theoretical ones by utilizing an experimental prototype.
Section snippets
Theory of power calculation and prediction method
The paper text is divided to theory, measurements and calculation parts that help better understanding of the used procedure. The first part considers theory of power calculation and the used prediction method. Power calculation theory highlights which parameters have to be measured. Thus, it is explained before measurement strategies. The second subsection of theory is about ANFIS which has specific equations. The other parts contain investigation of temperature measurement, produced power
Structure of measurement system
A setup is prepared to measure temperature of vehicle’s hot places that is exhibited in Fig. 2. This system consists of a 10 kΩ resistor, an ATmega8 microcontroller, USB connector, input and output sockets, wires and a DS18B20 sensor. To save the obtained data during experiments, a connection is designed between the measurement system and a computer. It is noteworthy that sensor wires length should be fewer than two meters to get accurate data with less noise. Due to high temperature of
Calculating the produced power
To find generated power of TEG (SP1848) in the measured temperatures, an experimental setup is prepared. The TEG module and the constructed prototype in the laboratory are illustrated in Fig. 8, Fig. 9, respectively. As seen in Fig. 9, this system is divided into two parts. Heater, TEG and cooling system are located in outside of the setup as well controller, converter and other electrical components are placed inside a box. The DC/AC converter is used for converting DC output to feed AC loads.
Power prediction based on measurements
Prediction of produced power based on measurements is possible by utilizing ANFIS. The practical measurements in different conditions have been utilized to assemble a prediction algorithm based on ANFIS. The ANFIS algorithm predicts expected values of power for the most probable conditions based on a normal distribution function with variable variances. Then, the algorithm is used to calculate the average power of TEG network including TEG on exhaust and brake discs.
Expected values and their applications
In order to have an accurate investigation, beside the measurements, predicting the results by the proposed algorithm is done and considered as expected values. In this regard, the results including expected values of current, voltage, power and energy for average speeds of 55 km/h and 40 km/h with 10% and 20% variances are obtained for SP1848 and reported in Table 3, Table 4. It should be noted that other probable conditions are summarized in Table 5. The vehicle speed and working time period
Comparing experiments with calculations
In order to validate the obtained results in the laboratory and calculated data, some results such as voltage, current and power are compared. Hence, the final results are presented in Table 6. As expected, experimental and theoretical results have differences in some cases. For instance, in 91 °C, the output power difference is about 0.12 W. This difference for current and voltage are about 0.079 A and 0.346 V, respectively. On the other side, in 116.9 °C, output power difference is increased
Conclusion
Calculating thermoelectric power for vehicle applications needs temperature measurement in different conditions that depends on wind speed, air turbulence and extracted gas from engine. A reliable analysis requires an accurate setup with high speed sampling feature. Hence, a measurement system is constructed with an accurate sensor to measure temperature instantaneously. Furthermore, an experimental prototype is utilized to determine the TEG power in real conditions. By adjusting the heat
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