Hybrid Powertrain Architectures Research Articles

Optimization algorithm for minimizing railway energy consumption in hybrid powertrain architectures: A direct method approach using a novel two-dimensional efficiency map approximation

Optimization algorithm for minimizing railway energy consumption in hybrid powertrain architectures: A direct method approach using a novel two-dimensional efficiency map approximation

A Coupling Architecture for Remotely Validating Powertrain Assemblies

<div>Among the myriad of potential hybrid powertrain architectures, selecting the optimal for an application is a daunting task. Whenever available, computer models greatly assist in it. However, some aspects, such as pollutant emissions, are difficult to model, leaving no other option than to test. Validating plausible options before building the powertrain prototype has the potential of accelerating the vehicle development even more, doing so without shipping components around the world. This work concerns the design of a system to virtually couple—that is, avoiding physical contact—geographically distant test rigs in order to evaluate the components of a powertrain. In the past, methods have been attempted, either with or without assistance of mathematical models of the coupled components (observers). Existing methods are accurate only when the dynamics of the systems to couple are slow in relation to the communication delay. Also, existing methods seem to overlook the implications of operating a distributed system without a common time frame. In order to overcome the inherent latency arising from long-range communication, the proposed design combines two features: The exploitation of synchronized clocks for the simultaneous introduction of setpoint commands and the use of observers generated through machine learning algorithms. This novel design is subsequently tested in two scenarios: A simple one, involving the virtual coupling of two parts of an elementary device formed by three rotating inertias, and a more complex one, the coupling between an internal combustion engine and an electric motor/generator as representative of a series or parallel hybrid powertrain. Although the results are heavily influenced by the quality of the data-generated observers, the architecture improves the fidelity of the coupling by nearly an order of magnitude compared to the alternative of directly transmitting the signals. It also opens a niche application that leverages the accuracy of low-fidelity models.</div>

Framework for fault-tolerant speed tracking control of gasoline engines using the first principle-based engine model

ABSTRACT Optimal engine torque management, a fundamental objective, depends predominantly on engine speed tracking performance. It ensures to attain desired speed profile in the presence of uncertainties, disturbances and malfunctions. On the other hand, certain requirements such as emissions control, fuel efficiency and drivability are degraded in case of poor speed tracking. Furthermore, constraints on engine speed tracking performance are even more stringent for hybrid power-train architecture as crankshaft speed and engine torque are the basic variables for coordinated control. Speed tracking is also considered essential for gear shift control of the automatic transmission. In this research work, a framework for fault-tolerant speed tracking of the gasoline engine is proposed using the First Principle-based Engine Model (FPEM). A high-fidelity direct relationship between fuel injection input and engine speed is derived by the transformation of FPEM. Fault is induced in the fuel injection subsystem to generate the torque imbalance. Using the proposed framework, a second-order sliding mode-based control technique is applied to track desired speed profile by mitigating the faults in the fuel injection subsystem. Reference data acquired from the engine test rig is used to demonstrate the offline validity and fault tolerance capabilities of the proposed framework in MATLAB/Simulink.

Guest Editorial Special Issue on Novel Hybrid and Electric Powertrain Architectures

Hybrid and all-electric vehicles have gained significant market share and are expected to see rapid development over the next few years. Traction force in vehicles can be generated by the powertrain unit in different configurations. Several automotive companies are investing in electrification and aiming for a paradigm shift in powertrain. Simultaneously, universities and research institutes are trying to contribute to this process. However, more consistency and unification are needed between the industry and academia for better results.

Fuel economy improvement and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures with lithium battery cell experimental data

This paper intends to provide design selections of hybrid powertrain architectures in 48 V mild hybrid electric vehicles. Based on the location of the electric machine in the driveline, the hybrid powertrain architectures can be categorized into five groups, P0, P1, P2, P3, and P4. This paper uses simulation software to investigate the fuel economy improvements and emission reduction of 48 V mild hybrid electric vehicles with P0, P1, and P2 architectures. A baseline conventional and a 12 V start/stop vehicle models based on the production vehicle are built for comparison. The 48 V battery pack model is based on experimental data including open-circuit voltage and internal resistance of a 20 Ah lithium polymer battery cell. Four standard driving cycles are used to assess the fuel economy and emissions of the vehicle models. With features of engine idle elimination, electric power assist, and regenerative braking, the 48 V P0 and P1 respectively gains average 13.5% and 15.5% simulated fuel economy compared to baseline vehicle. The 48 V P2 enables feature of electric launch/driving and improves the fuel economy by average 18.5% better than baseline vehicle. The 48 V mild hybrid system seems to be one of the promising techniques to meet future fuel economy standards and emission regulations.

Toward more electric powertrains in aircraft: Technical challenges and advancements

The main purpose of this article is to provide an instructive review of the technological challenges hindering the road toward more electric powertrains in aircraft. Hybrid, all-electric, and turboelectric powertrain architectures are discussed as possible fuel consumption and weight reduction solutions. Among these architectures, the short-term implementation of hybrid and all-electric architectures is limited, particularly for large-capacity aircraft due to the low energy/power density levels achievable by state-of-the-art electrical energy storage systems. Conversely, turboelectric architectures with advanced distributed propulsion and boundary layer ingestion are set to lead the efforts toward more electric powertrains. At the center of this transition, power converters and high-power density electric machines, i.e., electric motors and generators, and their corresponding thermal management systems are analyzed as the key devices enabling the more electric powertrain. Moreover, to further increase the fuel efficiency and power density of the aircraft, the benefits and challenges of implementing higher voltage powertrains are described. Lastly, based on the findings collected in this article, the projected roadmap toward more electric aircraft powertrains is presented. Herein, the individual targets for each technology, i.e., batteries, electric machines, and power converters, and how they translate to future aircraft prototypes are illustrated.

Powertrain Design Optimization for a Range-Extended Electric Pickup and Delivery Truck

<div>The ongoing electrification and data-intelligence trends in logistics industries enable efficient powertrain design and operation. In this work, the commercial package delivery vehicle powertrain design space is revisited with a specific combination of optimization and control techniques that promise accurate results with relatively fast computational time. The specific application that is explored here is a Class 6 pickup and delivery truck. A statistical learning approach is used to refine the search for the most optimal designs. Five hybrid powertrain architectures, namely, two-speed e-axle, three-speed and four-speed automatic transmission (AT) with electric motor (EM), direct-drive, and dual-motor options are explored, and a set of Pareto-optimal designs are found for a specific driving mission that represents the variations in a hypothetical operational scenario. The modeling and optimization processes are performed on the MATLAB<sup>™</sup>-Simulink platform. A cross-architecture performance and cost comparison is performed, which shows that two-speed e-axle is the optimal architecture for the selected application.</div>

Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle

Hybrid hydraulic technology has the advantages of high-power density and low price and shows good adaptability in construction machinery. A complex hybrid powertrain architecture requires optimization and management of power demand distribution and an accurate response to desired power distribution of the power source subsystems in order to achieve target performances in terms of fuel consumption, drivability, component lifetime, and exhaust emissions. For hybrid hydraulic vehicles (HHVs) that are used in construction machinery, the challenge is to design an appropriate control scheme to actually achieve fuel economy improvement taking into consideration the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. To improve fuel economy and adaptability of various driving conditions to online energy management and to enhance the response performance of an engine to a desired torque, a hierarchical model predictive control (MPC) scheme is presented in this paper using the example of a spray-painting construction vehicle. The upper layer is a stochastic MPC (SMPC) based energy management control strategy (EMS) and the lower layer is an MPC-based tracking controller with disturbance estimator of the diesel engine. In the SMPC-EMS of the upper-layer management, a Markov model is built using driving condition data of the actual construction vehicle to predict future torque demands over a finite receding horizon to deal with the randomness of the driving conditions. A multistage stochastic optimization problem is formulated, and a scenario-based enumeration approach is used to solve the stochastic optimization problem for online implementation. In the lower-layer tracking controller, a disturbance estimator is designed to handle the uncertainty of the engine, and the MPC is introduced to ensure the tracking performance of the output torque of the engine for the distributed torque from the upper-layer SMPC-EMS, and therefore really achieve high efficiency of the diesel engine. The proposed strategy is evaluated using both simulation MATLAB/Simulink and the experimental test platform through a comparison with several existing strategies in two real driving conditions. The results demonstrate that the proposed strategy (SMPC+MPC) improves miles per gallon an average by 7.3% and 5.9% as compared with the control strategy (RB+PID) consisting of a rule-based (RB) management strategy and proportional-integral-derivative (PID) controller of the engine in simulation and experiment, respectively.

A Comparative Study on Coaxial Parallel and Coaxial Parallel-serial Hybrid Architectures for Plug-in Hybrid Electric Buses

This paper presents a comparative study on coaxial parallel hybrid (PH) and coaxial parallel-serial hybrid (PSH) powertrain architectures for plug-in hybrid electric buses (PHEB) in the perspective of economical performance. To do so, parameters of main components, including the engine power, the electric motor power, the capacity of battery are selected based on the same dynamic specifications of a PHEB. Then the PH electric bus (PHB) and the PSH electric bus (PSHB) are modeled in Matlab/Simulink, respectively. The dynamic programming (DP) algorithm and the nonlinear model predictive control (NMPC) based energy management controllers are designed to evaluate the fuel consumption and the energy cost of batteries. The simulation results reveal that the electric motors in PSHB possess about 10% higher equivalent efficiency and the PSHB has the superiority of energy saving effect over PHB.

Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine

The great development of engine technologies can help to improve the engine characteristics and performance: a better thermal efficiency and an extending fuel economy area, which will subsequently decrease the fuel consumption and thus influence the overall architecture of the vehicle. In this paper, an investigation is carried out to assess the influence of the high-efficiency engine on the transmission gear numbers. First, according to the relevant studies and the integration of the advanced engine technology, a future engine fuel consumption map is obtained, based on which, the preliminary simulations are applied to explore the best match between the transmission and the proposed future engine from the perspective of fuel consumption. The simulation results indicate that the transmission with four gears is the best option to match the future engine while maintaining good fuel economy and meeting the driving demands. Then, based on this conclusion, a new hybrid powertrain architecture, which includes four gears for the engine, is introduced and analyzed in detail, with the advantage of seamless gear shift due to the compensation torque of the motor. Finally, to further examine the fuel economy and gear shift quality of the proposed powertrain, the dynamic model is established and the simulation results demonstrate that the new powertrain architecture shows a good fuel consumption performance and the gear shift process can be achieved without power interruption.

Balancing driveability, NVH, and fuel consumption on automotive powertrains using integrated simulation techniques

The engine downsizing approach can be considered as state-of-the-art automotive propulsion system. However, future trends are also indicating a trend to a further reduction of the number of engine architectures, normally accompanied with a reduction of the number of cylinders in internal combustion engines, including prospective use of alternative fuels such as compressed natural gas or the downsized engine embedded in a powertrain with electrically driven propulsion systems. Hitherto, different simulation approaches are employed to evaluate the trade-off between noise, vibration and harshness, driveability, and fuel consumption. This paper presents the coupling of two simulation tools, each one an established tool in its discipline, namely a 1D-gas-dynamics tool for a downsized internal combustion engine including a dedicated boost-pressure control approach, and a multibody dynamics tool to simulate the powertrain and vehicle dynamics, using the functional mock-up interface. Using this approach as an industry first application, a significant reduction of development time while avoiding duplication of powertrain subsystem modeling, is achieved. This enables a transient simulation and supports a fast analysis of different powertrain architectures. It, furthermore, allows a detailed look at the impact of powertrain subsystems like the architecture of the internal combustion engine, flywheel, and driveline design on relevant vehicle attributes. Moreover, the transient simulation approach allows the investigation of lag effects caused by the air-path characteristics of the internal combustion engine, or transient effects brought up with the advent of hybrid powertrain architectures. The application case of this tool chain presented in this paper revealed that engine architecture reduction should be carried out carefully in order to not compromise driveability of the engine later on. This could bear the risk of sacrificing fuel economy due to a higher lugging limit, often linked to a reduced firing order of the engine, caused by a reduction of the number of cylinders. The paper finally discusses prospective solutions in terms of conventional mechanical solutions and added electrified propulsion including potential limitations to overcome this issue.

Topology Generation for Hybrid Electric Vehicle Architecture Design

Existing hybrid powertrain architectures, i.e., the connections from engine and motors to the vehicle output shaft, are designed for particular vehicle applications, e.g., passenger cars or city buses, to achieve good fuel economy. For effective electrification of new applications (e.g., heavy-duty trucks or racing cars), new architectures may need to be identified to accommodate the particular vehicle specifications and drive cycles. The exploration of feasible architectures is combinatorial in nature and is conventionally based on human intuition. We propose a mathematically rigorous algorithm to enumerate all feasible powertrain architectures, therefore enabling automated optimal powertrain design. The proposed method is general enough to account for single and multimode architectures as well as different number of planetary gears (PGs) and powertrain components. We demonstrate through case studies that our method can generate the complete sets of feasible designs, including the ones available in the market and in patents.

Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach

Due to limited flexibility of engine operation, it is uneasy to improve the fuel economy of parallel hybrid electric vehicles (HEVs) with a single electric machine (SEM) from control perspective only. However, from design perspective, it is likely to devise multiple operating modes on a powertrain system to achieve better fuel economy. Therefore, in this paper, a velocity coupling HEV system with a SEM has been proposed, which has eleven modes, including an electric vehicle (EV) mode, three regenerative braking modes, an engine-start mode, an electrically variable transmission (EVT) mode, a parallel HEV mode, an engine-only mode, and three charging modes. Dynamic Programming (DP), as a global optimization algorithm, is leveraged to minimize the fuel consumption of this system. For comparison purposes, the P1 and P2 HEV systems are also examined by DP using the same vehicular parameters. Comparative results indicate that the P2 HEV saves about 6.68% fuel consumption over the P1 HEV, while more improvement can be observed from the proposed velocity coupling HEV, with 13.82% fuel consumption reduction over the P1 HEV.

Current hybrid-electric powertrain architectures: Applying empirical design data to life cycle assessment and whole-life cost analysis

The recent introduction of hybrid-electric powertrain technology has disrupted the automotive industry, causing significant powertrain design divergence. As this radical powertrain innovation matures, will hybrid vehicles dominate the future automotive market and does this represent a positive shift in the environmental impact of the industry? The answer to this question is sought within this paper. It seeks to take advantage of the position that the industry has reached, replacing previous theoretical studies with the first extensive empirical models of life cycle emissions and whole-life costing. A comprehensive snapshot of today’s hybrid market is presented, with detailed descriptions of the various hybrid powertrain architectures. Design data has been gathered for 44 hybrid passenger cars currently available in the US. The empirical data is used to explore the relative life cycle greenhouse gas emissions and whole-life costing of different hybrid powertrain architectures. Potential dominant designs are identified and their emissions are shown to be reduced. However, both the emissions and economic competitiveness of different hybrid powertrains are shown to vary significantly depending on how the vehicle is used.

Modeling, Control, and Experimental Validation of a Transient Hydrostatic Dynamometer

Due to its superior power-to-weight ratio, a hydrostatic dynamometer is an ideal candidate for transient engine or powertrain testing. It can load or motor the engine to follow any desired speed and acceleration profiles for real-world applications. Given its high bandwidth, the hydrostatic dynamometer can be further used to emulate the dynamics of hybrid powertrains and, therefore, investigate the interactions between the engine and the hybrid power source in real time. This will greatly expedite the research of various hybrid powertrain architectures and control methodologies, without actually building the complete physical system. This paper presents the design, modeling, tracking control, and experimental investigation of a transient hydrostatic dynamometer. A ninth-order physics-based dynamic model for the dynamometer is formulated and then identified and validated with experimental data. To control the dynamometer to emulate the real-world engine speed/torque profiles, two different nonlinear control strategies are investigated and implemented. First, a nonlinear model-based inversion plus PID control is designed to achieve precise tracking. Then, a state feedback control via feedback linearization is designed and implemented. Experimental results demonstrate precise tracking performance with less than 5% tracking error for both transient and steady-state operations.

Diesel hybrid powertrain for passenger and light commercial vehicles

Diesel hybrid powertrain for passenger and light commercial vehicles