Powertrain Test Research Articles

Composite Control Law for Nonlinear Systems With Mismatched Disturbances for a Ball-Ramp Dual-Clutch Transmission

The dual-clutch transmission (DCT) was developed to increase the transmission efficiency and the shift performance. However, in a DCT, due to uncertainty related to the actuator, the tie-up phenomenon can arise, in which two clutches engage together or the clutch torque control performance deteriorates. Especially, the actuator uncertainty increased when using a special mechanism to increase controllability and efficiency. Among them, the ball-ramp DCT (BR-DCT), which uses a self-energizing mechanism, can reduce the consumption of actuator energy while also reducing the tie-up effect. However, the nonlinearity of the actuator must be considered, such as friction between parts and change in friction coefficient. Among the methods by which this type of uncertainty is estimated, the nonlinear disturbance observer is most effective when used to estimate the unmodeled nonlinearity of an actuator and time-varying uncertainty. In order to execute disturbance rejection control to improve the performance of the shift controller using the estimated uncertainty, it is necessary to configure disturbance compensation input. However, because the BR-DCT powertrain includes mismatched disturbances and nonlinearity of the actuator, it is difficult to apply the existing methods on the composite control law to shift controller. Therefore, in this study, we propose a composite control law to guarantee integrated stability in nonlinear systems such as a BR-DCT powertrain. The proposed method was verified through a powertrain test bench equipped with a BR-DCT. Finally, the proposed composite control law was able to converge both the tracking error and the disturbance estimation error.

Design of Ball-Ramp dual clutch transmission to reduce uncertainties in clutch actuator and tie-up effect

Dual-clutch transmissions (DCT) have high efficiency, low drop in tractive force during shifting, and fast shifting is possible. However, it is infeasible to measure clutch torque due to cost and durability problems, and difficult to control clutch torque due to change in friction coefficient. Also, the energy consumed by the actuator for maintaining engagement is large. To address these limitations, a Ball-Ramp DCT (BR-DCT) was developed based on the self-energizing principle. The BR-DCT can reduce energy consumption of the clutch actuator. In addition, it is possible to reduce the uncertainties in actuator by using only measurable values to calculate the actuator input; and, when a tie-up occurs in which two clutches are engaged at the same time, the magnitude of negative torque can be reduced. However, design constraints for implementing last two features were not addressed in previous studies. In this paper, we propose the two design constraints to reduce the uncertainties in actuator and reduce the tie-up effect. For verification, a BR-DCT prototype and powertrain test bench were manufactured with the design constraints. As a result of the shift experiment, it was verified that accuracy of the actuator model can be improved, and the tie-up effect can be reduced.

Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains

Battery electric vehicles (BEVs) are currently enjoying rising sales figures. However, BEVs still have problems with customer acceptance, partly due to limited driving ranges. To improve the situation, this paper introduces a novel approach utilising temperature-dependent efficiencies using an economic model predictive control approach (MPC) in combination with an active grille shutter in order to accelerate the heating of the permanent magnet synchronous machine. The measurements of temperature-dependent component efficiencies on a powertrain test bench are presented and analysed in detail in the speed/torque range. Thermal models based on the lumped parameter thermal network approach were developed and validated as part of the system-level validation against a US06 wind tunnel measurement. After the build-up and implementation of the MPC, various simulations were conducted. For the investigations, three driving cycles were considered at component start temperatures of 20–80 °C. The results show that using the MPC with the grille shutter can save 0.69–2.02% energy at the HV level compared to the rule-based control with a shutter, of which up to 1.02% is due to temperature-dependent efficiencies. Comparing the MPC with the grille shutter to a vehicle without a shutter, savings of 2.8–4.2% were achieved, while up to 1.67% was achieved due to temperature effects in the powertrain.

Investigation of methods for measuring fuel economy and emissions of heavy-duty hybrid-electric vehicles (HEVs)

Hybrid-electric vehicles can achieve low fuel consumption and emission by optimal combination of electrical energy and internal combustion engine power. There are four main test method of energy consumption and emission for heavy-duty HEVs, including traditional engine bench test, powertrain test, HILS and chassis dynamometer test. The tradition engine operating conditions are distinguished from that in HEVs, which can’t reflect the performance of HEVs. Powertrain test can achieve braking energy regeneration, but need bulky installation on testbed. HILS test solves the problem that the engine test cycle is different from the actual operating condition on road and is cost-effective. However, it requires a lot of effort to monitor the HILS simulation input parameters and carry out vehicle validation. Chassis dynamometer test method can better reflect the real-road driving condition. But it is nearly impossible to test all different HEVs types on chassis dynamometer and the test cost is very expensive. By comparing different heavy-duty HEVs energy consumption and emission test methods, the advantages and disadvantages are identified, which provides guidance for the formulation of the next-stage standards of heavy-duty HEV, and promotes the healthy and orderly development of the HEV industry.

Construction and experimental research of multifunctional hybrid powertrain test bench

With the development of hybrid electric vehicles, the demand for test benches is becoming more and more vigorous. Based on the traditional engine test bench, this paper builds a multifunctional hybrid powertrain test bench by matching appropriate equipment such as battery simulators, power analyzers, fuel consumption meters, and air flow meters. The test bench has the characteristics of modularization, flexibility, and multi-function. It can be used for testing single engine, single generator and hybrid power system. Finally, the range extender produced by a domestic manufacturer was used as the test object, and the endurance test was carried out on the modified test bench. The test further verified the reliability and feasibility of the multifunctional hybrid powertrain test bench.

Hierarchical Power Allocation Method Based on Online Extremum Seeking Algorithm for Dual-PEMFC/Battery Hybrid Locomotive

This paper presents a hierarchical power allocation method (HPAM) for dual-stack fuel cell (FC) hybrid locomotive powertrain. In a high-power FC application such as rail transportation, the use of multi-stack system is able to provide the primary and stable demanded power. The battery is adopted as the energy storage source (ESS) to fulfill the transient power demand fluctuations. Besides, considering that the performance of the stack will change with the operating conditions, this study also searches an online identification method based on forgetting factor recursive least square (FFRLS) algorithm to update the system parameters of the dual-PEMFC system in real time. In addition, sequential quadratic programming (SQP) scheme is employed in this study to search for the optimal solution of nonlinear constraint problems. Furthermore, it is also to ensure that the proton exchange membrane fuel cell (PEMFC) systems can operate as much as possible in the high efficiency range, and the final battery state of charge (SOC) can be close to the initial SOC. Moreover, this study also adopts the equivalent consumption minimization strategy (ECMS) as benchmark to foreground the superiority of the presented method. A reduced-scale hybrid locomotive powertrain test bench is developed to demonstrate the practicability of the developed HPAM. It can be seen from the experimental results that, the proposed HPAM can optimize PEMFC systems efficiency and performances, and reduce system fuel consumption.

Enhanced efficiency prediction of an electrified off-highway vehicle transmission utilizing machine learning methods

The increasingly noticeable effects of climate change require action to reduce greenhouse gas emissions. In the transport sector, the increase in electromobility is an important tool for reducing CO2 emissions. In order to meet the essential requirements, a particularly detailed system knowledge, for example on energy efficiency in the powertrain, is needed for the development and improvement of electric vehicles.The present work contributes to this by mapping the efficiency of a planetary gearbox for an electric vehicle. The mapping is done by experimental modelling with machine learning methods based on a standard efficiency description. The data necessary for the neural network training is generated by efficiency experiments on the powertrain test rig. For suitable results the data was pre-processed by applying relevant low-pass filters. A comparison of the Bayesian-Regularization algorithm, the Levenberg-Marquardt algorithm and the scaled-conjugate-gradient algorithm for training exhibited the strengths and weaknesses of the individual algorithms for the efficiency mapping. By comparing each algorithms performance metrics, the one matching the requirements best is chosen for the efficiency mapping.The result is a continuous function that determines the efficiency based on speed- and torque-inputs. Based on a key performance indicator, statistical validation by evaluating the standard deviations is used to ensure the quality of the results. In this paper the suitable algorithm for the given use case was determined. It can be applied for further research due to the significant results shown.

Novel dynamic conditioning unit for the reproduction of real driving conditions on a test bed

The importance of thermal management has increased with the advent of electrification and fuel cell. The dynamic response required from the thermal circuit has also increased and cannot be reproduced by today’s conditioning systems. This contribution describes a novel, considerably more powerful conditioning concept with three main characteristics: The new conditioning concept is validated via simulation as well as by an application on a powertrain test bed. The system proved to be extremely robust in its function due to the consideration of all influencing parameters and, in addition, it is easy to use: no time-consuming configuration work is required prior to installation, nor when changing the test bed or the unit under test. The foundation for this is the model-based control that is based on a direct relationship between the temperature of the medium and the setpoint value. For the first time, temperature traces previously measured under real operation conditions can be reproduced and even temperature gradients of up to 60 K/s can be followed. The dynamic conditioning unit described here will be extended by a transient, real-time capable simulation model to a thermal emulator. This will enable future thermal management functions to be developed and optimized in ThermoLabs.

A Combined Markov Chain and Reinforcement Learning Approach for Powertrain-Specific Driving Cycle Generation

<div class="section abstract"><div class="htmlview paragraph">Driving cycles are valuable tools for emissions calibration at engine and powertrain test beds. While generic velocity profiles were sufficient in the past, legislative changes and increasing complexity of powertrain and exhaust aftertreatment systems require a new approach: Realistically transient cycles - which include critical driving maneuvers and can be tailored to a specific powertrain configuration - are needed to optimize the emission behavior of the said powertrain.</div><div class="htmlview paragraph">For the generation of realistic velocity profiles, the Markov chain approach has been widely used and described in literature. However, this approach, so far, has only been used to generate cycles that are statistically representative of a large database of real driving trips, which is typically not available during the early stages of development of a new powertrain.</div><div class="htmlview paragraph">The work at hand combines a Markov chain approach for driving cycle generation with Q-learning - a reinforcement learning algorithm - to generate driving cycles that meet user-defined criteria and include critical driving maneuvers reflecting individual powertrain development targets.</div><div class="htmlview paragraph">Using simplified models of the powertrain, vehicle and driver and an iterative process, the generated driving cycles are evaluated regarding the fulfillment of the set criteria. The learning algorithm then adapts the Markov chain transition probability matrix to increase the occurrence of the desired critical driving maneuvers and reduce the share of undesired stochastic elements.</div><div class="htmlview paragraph">Exemplary results show that the proposed method is indeed able to generate driving cycles with certain realistic properties that include the desired maneuvers with the expected statistical variance.</div></div>

Impacts of Driving Conditions on EV Battery Pack Life Cycle

The aging of lithium-ion batteries (LIBs) is a crucial issue and must be investigated. The aging rate of LIBs depends not only on the material and electrochemical performance but also on the working conditions. In order to assess the impact of vehicle driving conditions, including the driving cycle, ambient temperature, charging mode, and trip distance on the battery life cycle, this paper first establishes an electric vehicle (EV) energy flow model to solve the operating parameters of the battery pack while working. Then, a powertrain test is carried out to verify the simulation model. Based on the simulated data under different conditions, the battery capacity fade process is estimated by using a semi-empirical aging model. The mileage (Ф) traveled by the vehicle before the end of life (EOL) of the battery pack is then calculated and taken as the evaluation index. The results indicate that the Ф is higher when the vehicle drives the Japanese chassis dynamometer test cycle JC08 than in the New European Driving Cycle (NEDC) and the Federal Test Procedure (FTP-75). The Ф will be dramatically reduced at both low and high ambient temperatures. Fast charging can increase the Ф at low ambient temperatures, whereas long trip driving can always increase Ф to varying degrees.

Comparison of Parameterization Methods for Real-Time Battery Simulation Used in Mechatronic Powertrain Test Benches

This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery’s behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.

Powertrain Test Beds for Transmission Calibration of Commercial Vehicles

Powertrain Test Beds for Transmission Calibration of Commercial Vehicles

Development of RDE/ISC test methodology in light of Euro 6d/VI emissions limits

This paper discusses the fact that vehicle and powertrain test methods have long been guided by type approval requirements – with a focus ot the recent/current example of real driving emissions (RDE) and in-service conformity (ISC) test requirements. The implcations current and future – of these testing requirement, which force the use of portable emissions measurement system (PEMS) are discussed. In this context, BOSMAL Automotive Research and Development Institute’s PEMS systems are presented, and the systems’ attributes and versatility are explored. Considerations for testing a very wide range of vehicle, engine and fuel types are mentioned. Non-legislative applications of PEMS systems are briefly explored; finally, it is shown that the emissions laboratory and its chassis dyno remain indispensable when conducting work on light duty exhaust emissions, even in the era of RDE.

Distributed Electric Powertrain Test Bench With Dynamic Load Controlled by Neuron PI Speed-Tracking Method

Based on the real-time model and platform, a dynamic hardware-in-the-loop (HIL) testing method for the distributed powertrain of electric vehicle (EV) is proposed. Compared with static point testing, the dynamic HIL test can provide a more realistic working environment for the EV’s distributed electric powertrain (DEP) early development. The test data of electric motor’s efficiency and maneuver performance under a dynamic work condition are more authentic and meaningful. Meanwhile, the driver-vehicle-road real-time (DVRRT) model is set up to emulate the actual condition. The speed-tracking control method with proportional–integral (PI) gains tuned by the neuron network algorithm is used to generate the distributed real-time loads. Maximum adhesion limitation is added once the slipping is detected in the real-time model. Simulation and experiment of the test bench are done. The generated distributed load is compared with both the theoretical one and the simulated one in the Carsim software platform. Two comparisons show the similar results. The load accuracy is high, but there is a short time delay. The mechanical work measured by the experiment test bench is highly consistent (97.5%) with the theoretical value. As a result, the proposed test bench and its control method can be used for DEP efficiency test.

Creeping control strategy for Direct Shift Gearbox based on the investigation of temperature variation of the wet multi-plate clutch

Proposing an appropriate control strategy is an effective and practical way to address the overheat problems of the wet multi-plate clutches in Direct Shift Gearbox under the long-time creeping condition. To do so, the temperature variation of the wet multi-plate clutch is investigated first by establishing a thermal resistance model for the gearbox cooling system. To calculate the generated heat flux and predict the clutch temperature precisely, the friction torque model is optimized by introducing an improved friction coefficient, which is related to the pressure, the relative speed and the temperature, before being demonstrated experimentally using a full scale powertrain test. After that, the verified heat transfer model and the reasonable friction torque model are employed by the vehicle powertrain model to construct a comprehensive co-simulation model for the Direct Shift Gearbox vehicle, capable of simulating the dynamic responses and predicting the temperature variations of two clutches. A creeping control strategy is then proposed and, to evaluate the vehicle performance, the safety temperature (250°C) is particularly adopted as an important metric. From the variations in torque and speed obtained from the simulation results, the vehicle can track the desired speed (1.5 km/h) satisfactorily, with only 3% fluctuation, and have good longitudinal dynamic performance (8.5 m/s3). But above all, during the entire 174 s creeping process, the temperature of two clutches is always under the safety value (250°C), which demonstrates the effectiveness of the proposed control strategy in avoiding the thermal failures of clutches.

Wide Bandgap Devices in Electric Vehicle Converters: A Performance Survey

This paper introduces a unique quantified study about using low-losses fast-switching wide bandgap (WBG) devices, i.e., gallium nitride (GaN) and silicon carbide (SiC), over traditional Silicon (Si) devices in the switching of dc/dc converters, focusing on electric vehicles' (EVs) machine drive and battery charger. A detailed model of the power train of a Nissan Leaf was developed in PSIM software, with WBG semiconductors' capability. The model was simulated one time using GaN semiconductors and another time using SiC devices. Simulation results are quantified and a comparison between different semiconductors in terms of total losses and efficiency is presented. The developed PSIM model can also be extended to other EVs like Chevy Volt. A proof of concept prototype for a Nissan Leaf dc/dc converter was built in the laboratory and results were collected. Componentwise experimental results are presented and their correlation with simulation findings is demonstrated. In addition, experimental results of the overall power train test bench are found to be matched with the simulation results on a system level as well.

Key Issues in Design and Manufacture of Magnetic-Geared Dual-Rotor Motor for Hybrid Vehicles

This paper presents a practical design guide for the magnetic-geared dual-rotor motor (MGDRM), which is one of the candidates of the power split device in hybrid electric vehicles. The spoke-type permanent magnet rotor and simplified complementary structure are first used to form a new MGDRM, which exhibits less flux leakage, less cost, and less torque ripple. Based on this structure, an FEA aided key dimensions design technique, considering the height of the outer rotor, is developed with the goal of generating maximum torque for a given motor volume. The method also takes the core saturation into consideration. Moreover, to enhance the manufacturability of the complimentary MGDRM, a unique technique is proposed to implement the dual rotor structure with simple components. A prototype of 16 kW is designed and manufactured. Experiments are carried out on a hybrid powertrain test bench, verifying the proposed design and manufacture technique.

Development of hybrid electric vehicle powertrain test system based on virtue instrument

Hybrid powertrain has become the standard configuration of some automobile models. The test system of hybrid vehicle powertrain was developed based on virtual instrument, using electric dynamometer to simulate the work of engines, to test the motor and control unit of the powertrain. The test conditions include starting, acceleration, and deceleration. The results show that the test system can simulate the working conditions of the hybrid electric vehicle powertrain under various conditions.

Multivariable control of a test bed for differential gears

Common problems related to the control of power train test beds are the coupling of the two typical controlled variables rotational speed and testing torque as well as resonant torque oscillations. This work presents a simplified non-linear mathematical model of a test bed for differential gears including the unit under test suitable for controller design. Based on this system model, a new control concept with input–output decoupling and feedback linearisation is developed to overcome both previously mentioned problems. Simulation studies using the proposed control structure as well as a conventional control concept for power train test beds show the superiority of the new controller.Finally the proposed controller was implemented on real-time processing hardware and tested on a commercial test bed for differential gears to prove the performance of the new control concept in practice.

Powertrain dynamics and control of a two speed dual clutch transmission for electric vehicles

The purpose of this paper is to demonstrate the application of torque based powertrain control for multi-speed power shifting capable electric vehicles. To do so simulation and experimental studies of the shift transient behaviour of dual clutch transmission equipped electric vehicle powertrains is undertaken. To that end a series of power-on and power-off shift control strategies are then developed for both up and down gear shifts, taking note of the friction load requirements to maintain positive driving load for power-on shifting. A mathematical model of an electric vehicle powertrain is developed including a DC equivalent circuit model for the electric machine and multi-body dynamic model of the powertrain system is then developed and integrated with a hydraulic clutch control system model. Integral control of the powertrain is then performed through simulations on the develop powertrain system model for each of the four shift cases. These simulation results are then replicated on a full scale powertrain test rig. To evaluate the performance of results shift duration and vehicle jerk are used as metrics to demonstrate that the presented strategies are effective for shift control in electric vehicles. Qualitative comparison of both theoretical and experimental results demonstrates reasonable agreement between simulated and experimental outcomes.