Due to the strengthening of air-quality regulations, researchers have been investigating methods to improve excavator energy efficiency. Many researchers primarily conducted simulation studies employing mathematical models to analyze the energy consumption of excavator systems, which is necessary to examine the fuel efficiency improvement margin and the improvement effect. However, to effectively study the improvement of excavator efficiency, the real-time energy consumption characteristics must be examined through simulations and analyses of actual equipment-based energy consumption. Accordingly, this study establishes an energy flow-down model for the entire excavator system based on actual equipment tests. A measurement system is built to measure the required data, thereby establishing an experimental methodology for modeling each component. This paper presents an excavator system energy flow-down methodology that integrates the energy flow-down model, measurement system, and experimental methodology. This methodology was applied to dig and dump operations, and the energy consumption characteristics were analyzed. An analysis of the operating modes indicates that 59.8% of the total fuel energy was consumed in the engine system, 17% in the hydraulic system, and 23.2% in the hydraulic actuation systems. The methodology can be used to help analysis of the fuel efficiency improvement margin under various conditions.

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

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Because of emissions of exhaust gases, global warming is proceeding, and air pollution has increased. Thus, many countries are manufacturing eco-friendly vehicles, including electric vehicles. However, the range of electric vehicles is less than the range of internal combustion engine vehicles, so electric vehicle production is being disrupted. Thus, it is necessary to analyze the energy flow of electric vehicles. Therefore, to analyze energy flow of electric vehicles, this study suggested an energy flow structure first, then modeled the energy flow of the vehicle, dividing it into battery, inverter and motor, reduction gear and differential, and wheel parts. This study selected a test vehicle, drove in urban driving conditions and measured data. Then, this study calculated energy flow using MATLAB/SIMULINK in real time, and calculated and analyzed energy loss of each of the vehicle’s parts using the calculated data.