Powertrain System Research Articles

Research on Electric Vehicle Powertrain Systems Based on Digital Twin Technology

As a critical component of electric vehicles, the powertrain has a significant impact on the overall performance of vehicles. In addressing the challenge of lengthy testing cycles, this study develops a para model of the powertrain, utilizing digital twin (DT) technology, thereby establishing a framework for simulation testing of multi-controller intermodulation. We establish functional definition coverage testing by designing specific functional requirement use cases, and we validate the failure mechanism via fault injection use cases. The results indicate that the DT testing platform can effectively simulate the operational interactions among various controllers within the powertrain system. In comparison to traditional field testing, the digital twin-based testing methodology offers enhanced operational efficiency and allows for the examination of testing conditions that are impractical to implement in real vehicles, particularly in the context of fault injection testing, thus facilitating the early detection of potential safety risks within the system. The advancement of this technical solution holds significant practical implications for the future mass production and development of electric vehicles.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

A review of the application development and key technologies of rotary engines under the background of carbon neutrality

Rotary engines possess advantages such as light weight, high power density, and strong fuel adaptability, which are essential for powertrain systems. They hold great potential in the field of power machinery. However, the development of rotary engines has been constrained by increasingly stringent emission regulations. Despite this, numerous scholars and institutions continue to optimize rotary engines, leading to a proliferation of related research. It is necessary to synthesize these findings to guide future promotion and application efforts. This review examines the current applications and development status of rotary engines globally. It summarizes the development of key technologies such as sealing techniques, rotor configurations, and ignition devices, which have achieved lower energy consumption and emissions. The suitability of rotary engines for automotive and unmanned aerial vehicle applications has also been reported. To further reduce carbon emissions, researchers have explored zero-carbon fuel rotary engines. Hydrogen-fueled rotary engines have demonstrated higher combustion efficiency. By integrating EGR, load control strategies, and ignition system layouts, they have addressed issues such as NOx emissions, knock, and leakage. The development of ammonia-hydrogen hybrid fuels is also a potential effective solution. Continuous innovative research indicates that, with their inherent advantages and the integration of alternative fuels and advanced ignition technologies, rotary engines have the potential to meet future energy and environmental requirements. This positions them as strong contenders in future diversified power systems.

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

Capacity configuration of fuel cell hybrid vehicles using enhanced multi-objective particle swarm optimization with competitive mechanism

A well-designed hybrid powertrain is crucial for ensuring the safe, efficient, and durable operation of fuel cell hybrid vehicles. This paper introduces a modular design approach for powertrains, utilizing a Competitive mechanism-based Multi-Objective Particle Swarm Optimization (CMOPSO) algorithm integrated with nested dynamic programming. In this approach, the upper layer employs the CMOPSO algorithm to design the powertrain system, while the lower layer optimizes power coordination for each proposed design. This two-layer optimization framework considers factors such as vehicle economy and durability. Under WLTP conditions, the capacity configuration results are a fuel cell with a rated power of 22 kW, 100 batteries in series, and 7 batteries in parallel. Furthermore, the modular approach outperforms three other algorithms in terms of solution count, diversity, and overall performance metrics. The study also highlights that the vehicle’s power demand characteristics, influenced by different driving cycles, significantly affect capacity configuration results. Sensitivity analysis reveals that both the total operating cost and manufacturing cost of the vehicle are most sensitive to variations in the fuel cell rated power.

Battery-electric powertrain system design for the HorizonUAM multirotor air taxi concept

AbstractThe work presented herein has been conducted within the DLR internal research project HorizonUAM, which encompasses research within numerous areas related to urban air mobility. One of the project goals was to develop a safe and certifiable onboard system concept. This paper aims to present the conceptual propulsion system architecture design for an all-electric battery-powered multirotor electric Vertical Takeoff and Landing (eVTOL) vehicle. Therefore, a conceptual design method was developed that provides a structured approach for designing the safe multirotor propulsion architecture. Based on the concept of operation the powertrain system was initially predefined, iteratively refined based on the safety assessment and validated through component sizing and simulations. The analysis was conducted within three system groups that were developed in parallel: the drivetrain, the energy supply and the thermal management system. The design process indicated that a pure quadcopter propulsion system can merely be designed reasonably for meeting the European Union Aviation Safety Agency (EASA) reliability specifications. By adding two push propellers and implementing numerous safety as well as passivation measures the reliability specifications defined by EASA could finally be fulfilled. The subsequent system simulations also verified that the system architecture is capable of meeting the requirements of the vehicle concept of operations. However, further work is required to extend the safety analysis to additional system components as the thermal management system or the battery management system and to reduce propulsion system weight.

Ambient Temperature Effects on Energy Consumption and CO2 Emissions of a Plug-in Hybrid Electric Vehicle

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively.

Comparison of direct‐drive Vernier wind generators for potential use at ≥10 MW power level

AbstractThis paper investigates the benefits and challenges of the multi‐MW direct‐drive offshore wind Vernier generators. It is worth noting that the comparison of generator topologies presented in ref. [1] was for the same power level and therefore a reduced machine volume for the surface‐mounted permanent magnet Vernier (SPM‐V) generators. This would mainly impact the capital expenditure (CAPEX) cost between the different investigated machines. Whereas in this paper, the performance is compared for the same machine volume that allows the Vernier generators to produce a much higher energy yield than a conventional SPM generator. As the extra energy yield would be beneficial over the lifetime of the turbine, this will have a bigger impact than the CAPEX cost savings with a reduced machine volume. In addition, a novel Vernier machine with magnets on both the stator and rotor has been proposed to further improve the energy yield. In addition to the basic electromagnetic performance, the levelised cost of energy (LCOE) of the three generator topologies, that is, the conventional SPM, SPM‐V and the proposed Vernier machines, has been compared. The direct‐drive powertrain systems with SPM‐V and the proposed Vernier generators can achieve LCOE of 12.3% and 24% lower than that of the conventional SPM generators, indicating their huge potential as an alternative to reduce the overall cost of energy.

Research on Failure Characteristics of Electric Logistics Vehicle Powertrain Gearbox Based on Current Signal

As a core component of the powertrain system of Electric Logistics Vehicles (ELVs), the gearbox is crucial for ensuring the reliability and stability of ELV operations. Traditional fault diagnosis methods for gearboxes primarily rely on the analysis of vibration signals during operation. This paper presents research on diagnosing gear tooth wear faults in ELV powertrains using motor current signals. Firstly, an experimental test platform was constructed based on the structural principle of the powertrain of ELV models. Subsequently, a pure electric light truck powertrain gearbox with tooth wear was tested. Time–frequency domain analysis, amplitude analysis, ANOVA analysis, kurtosis analysis, and zero−crossing points analysis were used to analyze the U−phase current of the motor connected to the gearbox to study the characteristics of the phase current of the drive motor after tooth wear. The results indicate that while the time–frequency domain characteristics of the U−phase currents are not significantly altered by tooth wear faults, the amplitude, variance, and kurtosis of the current increase with the severity of the wear. Conversely, the number of zero−crossing points decreases. These findings provide valuable insights into new methodologies for diagnosing faults in ELV powertrain systems, potentially enhancing the efficiency and effectiveness of troubleshooting processes.

Nonlinear modeling and torsional vibration analysis of heavy-duty vehicle powertrain system during acceleration

Nonlinear modeling and torsional vibration analysis of heavy-duty vehicle powertrain system during acceleration

Comparative study of real-time A-ECMS and rule-based energy management strategies in long haul heavy-duty PHEVs

Road transport is a significant contributor to Green House Gas (GHG) emissions in the European Union (EU) and is responsible for approximately 25% of total GHG emissions in the EU13. The European Commission has proposed stricter CO2 emissions targets for heavy-duty vehicles that require manufacturers to achieve a reduction of 90% in average fleet emissions from new vehicles by 2040 compared to a 2019 baseline. To meet these ambitious targets, there is an urgent need to explore alternative pathways away from conventional fossil fuel-based powertrain systems like electrified powertrains and carbon–neutral fuels. One promising option is the plug-in hybrid electric powertrain concept, which combines the advantages of both power sources, enabling improved fuel efficiency and reduced CO2 emissions. However, the potential of such a plug-in hybrid powertrain needs to be evaluated for heavy-duty trucks.This study focuses on the development of control strategies for the Energy Management System (EMS) of the above powertrain concept. First, a control strategy for the non-predictive EMS’s start/stop functionality and optimization of the torque split between the combustion engine and electric machine is developed and calibrated for efficient engine operation depending on the battery state of charge. In addition, a predictive EMS’s control strategy is developed that uses horizon information of the driving route for optimal utilization of electrical energy within the prediction horizon, thereby further enhancing fuel consumption reduction. The predictive EMS uses Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with Pontryagin’s Minimization Principle (PMP) for online equivalence factor adaptation, providing local optimal solutions in real-time. The study concludes with the energy savings achieved through the implementation of these strategies using real-world driving cycles in the heavy-duty transport sector.

Development and Performance Analysis of Commercial Vehicle Axle Shaft

Axle shafts, which are an important part of the vehicle's powertrain system, transfer the torque to the wheels and enable the vehicle to move. In this respect, the design of the axle shaft to be used in a new vehicle is of great importance for vehicle manufacturers. When a cylindrical shaft is torsionally loaded, the shear stress is highest at the surface of component and zero at the center. Therefore, these axle shafts are exposed to an induction hardening process that enables only this superficial case to have its properties changed, remaining the core zone with its material original characteristics. Current study presents the results of a project aimed at developing and evaluating the fatigue life of axle shaft that belongs to a commercial vehicle. Developments were made on the existing axle and the results were examined using experimental tests and finite element analysis method. In line with the improvements made, the developed axle shaft has 331.7% more fatigue life than the existing axle, while the cost is 24% lower. According to these results, more attention must be paid to material selection, induction hardening process, stress concentration and surface condition of axle shaft in the design. This study involves many disciplines such as design, manufacturing, analysis, testing, etc. It is very important to ensure communication between all these disciplines in the production of the product. The output obtained from one discipline becomes the input of another discipline. In the cumulative sum of all these inputs, the optimum level of parts is obtained. For this reason, this study, which processes all these disciplines together, is very valuable.

Comparative Simulation Analysis of Electric Vehicle Powertrains with Different Configurations Using AVL Cruise and MATLAB Simulink

Electric vehicles are now recognized as a crucial answer in the worldwide effort to achieve sustainable and environment-friendly transportation. As the automotive industry moves towards using electric power, it is crucial to assess and improve the powertrain configurations in electric vehicles. This study aims to meet this need by conducting an in-depth comparative analysis of single, double, and quad electric vehicle powertrain systems. The performance of these configurations is rigorously simulated by using two widely used platforms: AVL Cruise and MATLAB Simulink. This study mainly covers the analysis of energy efficiency, which is a critical factor in determining the environmental impacts and feasibility of electric vehicles. In order to conduct a thorough comparison, the energy consumption per kilometer is evaluated as a crucial performance measure. Our research primarily focuses on model validation, namely by comparing it with manufacturer data to determine the accuracy and reliability of the simulation results. The findings reveal a compelling narrative in the pursuit of sustainable transportation. The use of a dual motor setup stands out as a prominent example of energy efficiency, demonstrating remarkable outcomes in both simulation platforms. Significantly, these findings closely correspond with the manufacturer's data for the Volvo XC40, confirming the appropriateness and dependability of our simulation models. Furthermore, the quad motor configuration shows significant energy efficiency, providing helpful insight regarding its applicability and performance. The broader implications of this research go beyond powertrain configurations, including the trustworthiness of simulation models in the automotive industry. These findings improve the continuous advancement of electric vehicle design and development, indicating a more environment-friendly and energy-efficient future for the automotive industry. This study offers invaluable insights and benchmarks for the transition to environment-friendly transportation solutions, as the world advances towards sustainable mobility.

Non-dominated sorting artificial rabbit multi-objective sizing optimization for a conceptual powertrain of a 6 × 4 battery electric tractor truck

In order to tackle the increasingly severe energy crisis, improving the efficiency of the powertrain system in battery electric vehicles has become an effective approach. Based on a 6 × 4 battery electric tractor truck (BETT), the paper focuses on enhancing the dynamic and economic performance of vehicles through advancements in configuration, control strategies, and parameter design. Firstly, a novel multi-motor torque coupling powertrain system based on the parallel-axle transmission is proposed and modeled. Then, an instantaneous minimum energy consumption control strategy for the economic performance simulation and an instantaneous maximum acceleration control strategy for the dynamic performance simulation are designed. Furthermore, a conventional heuristic single-objective optimization algorithm, artificial rabbit optimization (ARO), is improved into a multi-objectives version, non-dominated sorting artificial rabbit multi-objective optimization (NSARMO), by combining the fast non-dominated sorting (FNS) and modifying the calculation method of the energy factor. Finally, an optimization framework consisting of a control layer, model layer, and optimization layer is established and applied to the parameter optimization of the proposed powertrain system. Simulation results demonstrate that the optimization results of the proposed method are superior to those optimized by commonly used non-dominated sorting genetic algorithm-II (NSGA-II) and NSARMO without the modified energy factor, achieving a better balance between dynamic performance and economic performance. Moreover, the proposed method exhibits demonstrates 40.67% and 24.67% improvement in its ability to search for optimal Pareto solutions compared to NSARMO (-)(- means the calculation method of energy factor is not modified) and NSGA-II. At last, a hardware-in-the-loop (HIL) experiment bench is established, and the experiment results indicate that a scheme optimized by the proposed NSARMO exhibits only an approximate performance in energy consumption in the HIL environment compared to the simulation environment, which signifies the effectiveness of control strategy equipped with the optimization results optimized by the proposed method.

Experimental validation of a predictive energy management strategy for agricultural fuel cell electric tractors

Efficiency and durability are critical challenges for the large-scale adoption of fuel cell powertrains in agricultural tractors due to the demanding nature of farm-related tasks. This study presents a novel predictive energy management strategy that uses forecasts of the average load of the farming cycle to achieve stationary fuel cell operation, thus limiting degradation caused by load cycling, high-power, and low-power operation. The recurrent characteristics exhibited by agricultural duty cycles allow the calculation of highly accurate average load prediction, which can be sufficient for a predictive energy management strategy to reduce degradation without hindering fuel consumption. As a result, this forecast is utilized in conjunction with a straightforward and real-time-capable energy management strategy, which foresees the operation of the fuel cell system at the level of the average load. The work is structured into two main contributions: model-based design of the energy management strategy, and experimental validation on a fuel cell powertrain testbed. Three typical agricultural duty cycles were studied to evaluate the benefits of the proposed strategy, demonstrating that energy management strategies can mitigate fuel cell degradation without compromising system efficiency. In particular, the predictive energy management strategy offers the potential to mitigate the negative impact of tractor operational idling time at the headland turns through the reduction of fuel consumption, thereby enhancing overall tractor performance. The experimental powertrain setup stands as an innovative contribution within the research landscape concerning fuel cell powertrains for tractors and validates the benefits of the proposed strategy. Furthermore, the conducted testbed analyses assume significance in highlighting the effects of the energy management strategy on the physical fuel cell operating factors and emphasizing the comprehensive benefits across the powertrain system, particularly with regard to the thermal management of the fuel cell system, where the prevention of derating is achievable through the implementation of the proposed energy management approach. Overall, the findings of this study have important implications for the development of sustainable and efficient farming procedures through the adoption of fuel cell technology in agricultural machinery.

Power Curve Determination and Electrification of Powertrain System of Harvester Crane Swinging

The work deals with the determination of the power curve for driving the swinging of the hydraulic crane of the harvester and the subsequent replacement of the hydraulic motor with an electric motor for the possibility of recuperating braking energy. For this purpose, the hydraulic circuit of the crane slewing gear was measured with flowmeters during its rotation between the angles –105° to +105°, –90° to +90° and –36° to +22°. The power was calculated from the measured values. The maximum power needed to swing the harvester crane was 9720 W. With this power, the torque reached 187.98 Nm. The average value of the power needed to swing the hydraulic crane was only 2472 W at a torque of 47.81 Nm. From these values, a synchronous electric motor with permanent magnets with a nominal speed of 2000 rpm (rotation per minute) and a power of 3246 W emerged as suitable for replacing the hydraulic motor. The use of an electric motor would, however, require a planetary gearbox with fast input speeds that would then be reduced to slow output speeds. The research results demonstrated the possibility of using an electric motor to swing the hydraulic crane during the work cycle of the harvester for the use of energy recovery from crane braking and thus the possibility of reducing fuel consumption and emissions.

Challenges and Solutions to Meet the Euro 7 NOx Emission Requirements for Diesel Light-Duty Commercial Vehicles

AbstractThe improvement of air quality requires a further reduction of pollutant emissions, especially in urban areas. The Euro 7 regulations aim at the development of a new generation of internal combustion engine vehicles capable of achieving ultra-low pollutant emissions under demanding, real-world operating conditions. They introduce new technical challenges in the holistic design of a vehicle’s powertrain and emission control system. To identify these, four real-world Euro 7 driving scenarios are investigated, covering demanding urban, highway and mountain driving situations. Technical solutions are then presented to address these challenges and ensure compliance with the Euro 7 emission requirements as set out in the latest regulation proposal of the European Commission. The study focuses on the NOx emissions of an N1 Class III light commercial vehicle with 3.5 t mass and a P2 diesel mild-hybrid powertrain. To ensure emission compliance, a Euro 6e exhaust gas aftertreatment system with enlarged catalysts is combined with NOx raw emission improvements. For low-load cold starts, a 4-kW electric heater in the exhaust system is considered in addition to a 2-l DOC and a 6-l DPF with SCR coating. For high-load cycles with high raw emissions, a 10-l underfloor SCR is considered to ensure the necessary deNOx performance.

Energy loss and efficiency investigations of a dual clutch transmission based on adaptive experiment and analytical simulation

Energy transfer improvement and energy consumption reduction are getting more crucial in the automotive sector. As one of the essential elements directly influencing energy transfer in the powertrain system, transmission losses have been widely analyzed with both simulation and experimental approaches. For a better characterization of individual losses, this paper investigates and compares most of the available analytical models for further system implementation. Regarding laboratory experiments, an adaptive sampling strategy is presented and analyzed for various operating conditions. Compared to the traditional factorial design, the applied Gaussian Process Regression (GPR) delivers both estimations and their predictive variances in the entire experiment process. Besides, new measurement candidates can be determined with the prior information from GPR model. The analytical simulation and adaptive measurement are employed to investigate a seven-speed transmission. With both approaches, a detailed discussion about total transmission losses and individual loss mechanisms are performed under various operating conditions.