Powertrain System Research Articles

Investigation on the wet compression performance of a co-rotating scroll hydrogen recirculation pump for fuel cell engine systems

The scroll-type hydrogen recirculation pump is critical for fuel cell vehicle powertrain systems, yet its performance under multiphase conditions remains poorly understood. This study proposes a co-rotating scroll hydrogen pump with array scrolls and investigates its aerodynamic performance, including volumetric efficiency, isentropic efficiency, and power consumption, under varying inlet humidity through 3D transient numerical simulations. Additionally, the effects of operational and geometric parameters (rotational speed, radial clearance, and axial clearance) on the wet compression performance of the pump at the maximum inlet humidity are carried out. Results show that inlet humidity primarily affects the compression and discharge processes of the pump. Increasing inlet humidity suppresses tangential leakage, improving volumetric efficiency, while reducing isentropic efficiency. With inlet humidity increasing from 0% to 100%, the volumetric and isentropic efficiencies change by + 1.84% and −2.3%, respectively, with a power consumption increase of approximately 8.9 W. Higher rotational speeds significantly decrease the isentropic efficiency, while axial clearance has a more pronounced impact on both efficiencies compared to radial clearance. Specifically, increasing rotational speed from 5000 r/min to 15,000 r/min enhances volumetric efficiency by 9.27% and reduces isentropic efficiency by 6.72%. Similarly, increasing radial clearance from 20 μm to 40 μm reduces volumetric efficiency by 5.51% and isentropic efficiency by 0.87%. Raising axial clearance from 20 μm to 40 μm results in a 17.86% drop in volumetric efficiency and 10.7% in isentropic efficiency. These findings offer design insights for high-efficiency co-rotating scroll hydrogen pumps.

Systematic hyperparameter selection in Machine Learning-based engine control to minimize calibration effort

For automotive powertrain control systems, the calibration effort is exploding due to growing system complexity and increasingly strict legal requirements for greenhouse gas and real-world pollutant emissions. These powertrain systems are characterized by their highly dynamic operation, so transient performance is key. Currently applied control methods require tuning of an increasing number of look-up tables and of parameters in the applied models. Especially for transient control this state-of-the-art calibration process is unsystematic and requires a large development effort. Also, embedding models in a controller can set challenging requirements to production control hardware. In this work, we assess the potential of Machine Learning to dramatically reduce the calibration effort in transient air path control development. This is not only done for the existing benchmark controller, but also for a new preview controller. In order to efficiently realize preview, a strategy is proposed where the existing reference signal is shifted in time. These reference signals are then modeled as a function of engine torque demand using a Long Short-Term Memory (LSTM) neural network, which can capture the dynamic input–output relationship. A multi-objective optimization problem is defined to systematically select hyperparameters that optimize the trade-off between model accuracy, system performance, calibration effort and computational requirements. This problem is solved using an exhaustive search approach. The control system performance is validated over a transient driving cycle. For the LSTM-based controllers, the proposed calibration approach achieves a significant reduction of 71% in the control calibration effort compared to the benchmark process. The expert effort and turbocharger experiments used in calibrating transient compensation maps in physics-based feedforward controller are replaced by little simulation time and parametrization effort in ML-based controller, which requires significantly less expert effort and system knowledge compared to benchmark process. The best trade-off between multi-objective cost terms is achieved with one layer and 32 cells LSTM neural network for both non-preview and preview control. For non-preview control, a comparable control system performance is achieved with the LSTM-based controller, while 5% reduction in cumulative NOx emissions and similar fuel consumption is achieved with preview controller.

Analysis of passenger car powertrain system measurements in road conditions

The paper is focused on presenting a methodology for measuring power and torque based on diagnostic equipment available in most diagnostic workshops, such as OBD interfaces or the CAN Bus on-board data transmission network, under real-world road conditions. The publication presents an algorithm for calculating the powertrain’s torque and power based on measurements of changes in vehicle speed or acceleration recording during a two-phase road test. The results presented, based on the method described, apply to both the internal combustion and electric vehicle. Common powertrain operating parameters, such as maximum power, maximum torque and the powertrain’s flexibility parameters described in the literature are proposed for the final evaluation of the vehicle’s traction system.

Electric vehicles’ powertrain systems architectures design complexity

Strict emission regulations and energy scarcity have ushered in a new era of automotive technology. Utilizing electric power as a second source of energy or an alternative to fossil fuel energy has been the center of attention for decades. Implementing electric energy in vehicles’ powertrain systems requires new system architecture and rigorous methods for decision-making in a multi-disciplinary design procedure. Accordingly, the challenge is to define the design requirements and the economic feasibility of the final product.

Dynamic modeling and optimization for powertrain shake characteristics of electric powertrain system with hydraulic engine mounts

Poor designed powertrain mounting system can lead to severe resonance when road excitation frequency is consistent with the natural frequency of the powertrain mounting system, which results in vertical vibration of the vehicle. In addition, double-layer vibration isolation and hydraulic engine mount (HEM) are widely used for vibration attenuation of electric powertrain. This paper focuses on the vertical vibration of electric vehicles under road excitation. An analytic model of powertrain shake characteristics considering the amplitude-frequency dependent nonlinearity of HEMs is established. Furthermore, the proposed nonlinear model is experimentally validated through rigid body modal test of the powertrain. Numerical simulation is carried out to investigate the influence of HEM parameters on powertrain shake characteristics. Simulation results demonstrate that the effective pumping area, upper chamber stiffness and inertia track effective cross-sectional area, and length of HEM have obvious impact on powertrain shake characteristics. Under the premise of ensuring vibration isolation performance under other conditions, optimization of the above HEM parameters can reduce peak value of car body vibration under road excitation and thus improve ride comfort.

Optimal rule extraction-based real-time energy management strategy for series-parallel hybrid electric vehicles

Energy management strategy plays an important role in improving the fuel economy of hybrid electric vehicles. In this paper, an energy management strategy optimized by dynamic programming for a series–parallel hybrid electric vehicle is proposed. The key components of the powertrain system are modeled, and the model accuracy is corrected with the experimental data. To construct the real-time energy management strategy, the dynamic programming algorithm is employed first to find the optimal control benchmark, and then the control sequence with optimal fuel economy is obtained to analyze and build the mapping relationship between control variables and driving conditions. To fulfill the requirement of real-world application, the influence of the engine warm-up process on fuel consumption is also investigated. Finally, according to the optimal control datasets, the optimal operating mode distribution of clutches and the optimal power of the engine and motor are extracted to generate the real-time strategy. Benefiting from the optimal control sequence of dynamic programming, this study explores optimal operating characteristics of powertrain components towards fuel saving and enables the strategy conversion from global optimal to real-time application, which provides opportunities to achieve approximate optimal fuel economy of hybrid electric vehicles in real-time.

Control strategy optimization of hybrid electric vehicle for fuel saving based on energy flow experiment and simulation

Energy flow analysis is one of the most effective methods for assessing and improving vehicle efficiency. It can provide the most effective support for the development and optimization of a hybrid electric vehicle (HEV) control strategy for improving the performance and energy saving of HEVs. In this study, an energy flow experiment of a series–parallel HEV is conducted in a climate chamber under the Worldwide Light-duty Test Cycle to address high fuel consumption. Then, GT-Suite and MATLAB/Simulink co-simulation models for the tested vehicle are built and calibrated. The problems of the original HEV control strategy are addressed using the analysis of vehicle energy flow distributions, operating conditions, and energy efficiency of key parts. Then, two optimized vehicle control strategies (termed Opt1 and Opt2) are employed to test vehicle fuel savings using a calibrated co-simulation model. The specific optimized vehicle control strategies primarily include canceling the series and parallel charging in the braking state, adding the engine direct-drive mode, and reducing the proportion of the parallel mode. The simulated results show that the 100 km fuel consumptions of Opt1 and Opt2 are reduced by 7.1% and 7.5%, respectively, compared with the original vehicle. The proportion of energy that drives the vehicle directly through the engines of Opt1 and Opt2 is increased from 45.7% to 70.2% and 61.6%, respectively. The total vehicle energy losses for Opt1 and Opt2 decrease from 29.0% to 22.5% and 22.9%, respectively. Although the engine of the original vehicle exhibits high thermal efficiency, the efficiency of the entire powertrain system is the lowest, resulting in the highest fuel consumption.

Driveline System Effects on Powertrain Mounting Optimization for Vibration Isolation under Actual Vehicle Conditions

<div>Vehicle vibration is the key consideration in the early stage of vehicle development. The most dynamic system in a vehicle is the powertrain system, which is a source of various frequency vibration inputs to the vehicle. Mostly for powertrain mounting system design, only the uncoupled powertrain system is considered. However, in real situations, other subsystems are also attached to the powertrain unit. Thereby, assuming only the powertrain unit ignores the dynamic interactions among the powertrain and other systems. To address this shortcoming, a coupled powertrain and driveline mounting system problem is formulated and examined. This 16 DOF problem is constructed around a case of a front engine-based powertrain unit attached to the driveline system, which as an assembly resting on other systems such as chassis, suspensions, axles, and tires. First, the effect of a driveline on torque roll axis and other rigid body modes decoupling is examined analytically in terms of eigensolutions and frequency responses. It is observed from the analysis that when the optimized uncoupled powertrain system is introduced in real vehicle conditions, the vibration isolation level of the powertrain mountings gets degraded. Then, a new improved approach of considering coupled powertrain and driveline systems in the initial design phase itself is proposed. The mounting system parameters such as mount location, mount orientation angle, and stiffness rate are optimized and redesigned for the proposed system. The results of the redesigned system show that the decoupling of the rigid body mode parameters is improved and consequently powertrain vibration performance is also improved in static and dynamic conditions of the vehicle. Overall, the findings of this study suggest that considering the driveline along with the powertrain as a coupled system at the early phase of the mounting system design itself improves the vibration performance of the vehicle during real-life situations.</div>

An imitation learning-based energy management strategy for electric vehicles considering battery aging

—The benefits of an electrified powertrain system, including increased energy efficiency and decreased emission, have made electrification of powertrain systems a top priority for the automobile industry. There has been a significant advancement in studying state-of-the-art battery technology in electric vehicle applications. However, the performance and longevity of electric vehicles may suffer due to battery degradation during vehicle usage. Additionally, there is a need for additional research on energy saving and battery degradation in hybrid energy storage systems for electric vehicles equipped with batteries and supercapacitors. This paper proposes an imitation Q-learning-based energy management system designed to improve energy efficiency and reduce battery degradation for the battery and supercapacitor electric vehicle. A battery electric vehicle is also studied for comparison purposes. To test the efficacy of the proposed method, experiments are conducted using a motor-generator set and a dSPACE SCALEXIO system. The comparisons indicate that the battery degradation is reduced by 26.36% and energy efficiency is increased by 3.83% through the imitation Q-learning energy management strategy.

Real-Time USB Networking and Device I/O

Multicore PC-class embedded systems present an opportunity to consolidate separate microcontrollers as software-defined functions. For instance, an automotive system with more than 100 electronic control units (ECUs) could be replaced with one or, at most, several multicore PCs running software tasks for chassis, body, powertrain, infotainment, and advanced driver assistance system (ADAS) services. However, a key challenge is how to handle real-time device input and output (I/O) and host-level networking as part of sensor data processing and control. A traditional microcontroller would commonly feature one or more Controller Area Network (CAN) buses for real-time I/O. CAN buses are usually absent in PCs, which instead feature higher bandwidth Universal Serial Bus (USB) interfaces. This article shows how to achieve real-time device I/O and host-to-host communication over USB, using suitably written device drivers and a time-aware POSIX-like “tuned pipe” abstraction. This allows developers to establish task pipelines spanning one or more hosts, with end-to-end latency and throughput guarantees for sensor data processing, control, and actuation.

Design of the electric vehicle powertrain systems based on μ-analysis

Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value [Formula: see text] is proposed. To solve the problem of robustness under parameter uncertainty, the traditional [Formula: see text]-synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of [Formula: see text]-analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value [Formula: see text] as a performance constraint. The result shows that the [Formula: see text]-analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the [Formula: see text]-analysis can suppress the oscillation better than the [Formula: see text]-synthesis.

Automation program for optimum design of electric vehicle powertrain systems based on artificial neural network

Many studies have been conducted on various powertrain systems, such as multi-motor, multi-speed, or both, to enhance the energy efficiency and dynamic performance of electric vehicles (EVs). This study developed an automated design program to obtain the optimal design of EVs for various powertrain systems. The program consists of an EV simulation and artificial neural network (ANN) modeling and optimization tools. The EV simulation tool employs an integrated EV model that can analyze the efficiency and performance of various powertrain systems in a single environment. The ANN modeling and optimization tool first constructs an ANN model, and then performs optimization using the ANN model to address excessive computational efforts arising from the multi-objective genetic algorithm. This study verified the developed program by conducting analysis and optimization of five powertrain systems with the same EV specifications. A multi-objective optimization problem was formulated by considering the design variables as the torque distribution between the motors and gear shifting patterns and ratios of the transmission, and the objectives as the energy consumption and acceleration time. A comparison of the optimization results among the five powertrain systems quantitatively showed the positive effects of the multi-motor and multi-speed powertrain systems. Furthermore, the ANN-based multi-objective optimization process allowed for the efficient determination of the optimum design solutions for the proposed EV powertrain systems. Consequently, these results demonstrated the effectiveness of the automation program in rapid decision-making on EV powertrain system configurations, satisfying each designer’s requirements.

Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data

Instantaneous fuel consumption estimation of fleet vehicles provides essential tools for fleet operation optimization and intelligent fleet management. This study aims to develop practical and accurate models to estimate instantaneous fuel consumption based on on-board diagnostics (OBD) data. Fuel consumption data is measured by a high-precision fuel flow meter. Two machine learning algorithms of Random Forest (RF) and Artificial Neural Networks (ANN) are trained with real-world urban and highway driving data of four fleet vehicles with different types and powertrain systems. In addition, the cold-start period of the vehicle operation is included to cover the fuel consumption penalty in the warm-up period. The validation results show that the RF method is more accurate than the ANN method, and both of the machine learning models have a better accuracy compared to the existing fuel consumption calculation methods based on the engine control unit (ECU) parameters.

A multidisciplinary detection system for cyber attacks on Powertrain Cyber Physical Systems

With the introduction of external connectivity to modern vehicles, both academic and industry security practitioners addressed the issues related to external communication interfaces exposing internal networks that are used to control the different vehicle’s systems. Despite these issues are considered having little-to-no impact on the functioning of the vehicle itself due to their demand of physical connection, with the advent of cellular connectivity proof-of-concept and demonstrations of remote takeover of the vehicle have been showcased, raising awareness about the risks related to the introduction of external connectivity on the vehicle system. All these remote attacks demonstrated by security researchers exploited several vulnerabilities to gain remote access to the Controller Area Network (CAN), one of the most deployed in-vehicle communication protocol. In these works, the authors demonstrate how to exploit the drive-by-wire features of modern vehicles to jeopardize people inside and outside the vehicle. To address this issue, several researchers designed algorithms to detect the injection of malicious CAN messages. However, these solutions are focused on the only CAN communication, without considering all the other subsystems that can be used by an attacker to hijack the vehicular system. Due to their high complexity, modern vehicle can be seen as Cyber–Physical Systems, in which the physical system (i.e. the mechanics of the vehicle) are controlled via software hosted on microcontrollers that communicate with each other via different communication protocols. Hence, to properly address the cybersecurity of a modern vehicle it is necessary to consider all the different aspects of the system, without focusing on a single one. In this work we investigate the capabilities of a multidisciplinary approach for anomaly detection based for securing the powertrain system of a generic vehicle. The experimental analysis presented in this paper is based on the model of a generic internal combustion engine and a speed controller, communicating via CAN. Four different detection methods from both control theory and computer security are tested against cyber attacks carried out from different contexts. Our threat model is composed by different attack scenarios representative of known cyber-attacks targeting modern vehicles. The experimental evaluation presented in this work highlights the strengths and weaknesses of each detection method against different attack scenarios carried out on the different components of our system. To the best of our knowledge, this is the first paper addressing the cyber security of the connected vehicle with a multidisciplinary approach.

H–H Configuration of Modular EV Powertrain System Based on the Dual Three-Phase BLDC Motor and Battery-Supercapacitor Power Supply System

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions.

Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.

Enabling off-highway diesel engine downsizing and performance improvement using electrically assisted turbocharging

Internal combustion engine (ICE) downsizing through various turbocharging configurations is generally known by the powertrain design community as an effective means to reduce frictional losses, increase waste heat recovery, and improve fuel efficiency while increasing engine power density. However, often is the case that turbocharging strategies, including variable geometry turbochargers, and regulated two-stage turbochargers, incur the performance tradeoff between transient response and fuel economy (pumping losses) at high engine speeds. For off-highway vehicles having particularly transient and high-powered duty cycles, efforts to improve this tradeoff and increase operational flexibility have turned to evaluating various electrified air system architectures. In this study, a 4.5 L diesel-ICE configured with an electrically driven compressor (eBooster®) is placed in series with a conventional turbocharger and integrated into a 48 V mild-hybrid powertrain architecture. The objective of this powertrain configuration is to enable engine downsizing by 34%, replacing the current 6.8 L ICE platform with the hybridized 4.5 L ICE concept. The commercial 1-D simulation software GT-SUITE is used for powertrain system development and system optimization. Development and validation of the GT-SUITE model and air system controls is concurrently supported through experimental data collection. The simulation model development includes using machine learning methods for optimizing exhaust gas dilution, injection timing, and eBooster® power to improve steady-state and transient brake-specific fuel consumption while minimizing criteria pollutant emissions. It was found that total specific fluid consumption over the standardized non-road transient engine duty cycle could be reduced by 18% over the current 6.8 L engine by using an optimized eBoosted 4.5 L engine. The hybridized 4.5 L engine concept concurrently showed sufficient transient response capability and nearly an order of magnitude reduction in duty cycle total soot production.

Real Driving Cycle Simulation of a Hybrid Bus by Means of a Co-Simulation Tool for the Prediction of Performance and Emissions

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity.

Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels

The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.

Comprehensive energy footprint benchmarking of commercial electrified powertrains

This work presents a comprehensive energy management framework applied to electrified commercial powertrains with in-depth energy footprint analysis. We use our recently proposed algorithm, validated real-world powertrain system models, and Pareto-optimal analysis to optimize fuel consumption and harmful pollutant emissions. The approach involves dynamic optimization of 13 states and 4 control levers capturing complex subsystem interactions in parallel P2 and series range-extender commercial medium-duty electrified trucks. These subsystems exhibit vehicular (eco-driving), thermal, electrical, and mechanical dynamics at different time scales, and contain kinematic and combinatorial constraints, integer- and real-valued variables, interpolated look-up tables, and data maps. A Pareto-optimal solution is found by carefully optimizing fuel and NOx emissions to understand the energy footprint. Presented results exhibit rich powertrain behavior to unearth up to 6% improvement in energy demand, 2% in fuel economy and 18% in pollutant NOx reduction when compared to Dynamic Programming baseline from a coarsely modeled powertrain system. Furthermore, internal energy flow in the powertrains is analyzed to benchmark the optimal energy consumption and realize the multi-objective trade-off. Finally, a transient study problem is presented comparing cold versus warm after-treatment start conditions. It optimizes engine switching, eco-driving, and powersplit controls revealing adequate dynamic response and complex system interactions.