Powertrain Configurations Research Articles

Analysis of experimental results with an active pre-chamber ultra-lean burn SI engine

To ensure that private cars can continue to be used in the future, they must become significantly more efficient and at the same time emit considerably less pollutants. In addition to pure electric drives, further optimized gasoline engines in hybrid powertrain configurations still offer major potentials in this respect. A major step toward increasing efficiency can be achieved by extremely lean burn combustion. If, in addition to low fuel consumption, this operation should also simultaneously reduce NOx raw emissions, lean-burn operation with relative air/fuel ratios of λ≥ 2 must be enabled in wide ranges of the engine operation map. Within the scope of this publication, results of experimental investigations with a lean burn pre-chamber ignition system applied to a small gasoline engine with 75 mm bore and 90.5 mm stroke are presented. In this context, the effects of the pre-chamber design on emissions and fuel consumption are examined. By comparing different pre-chamber enrichments with natural gas and conventional RON98 gasoline, it can be shown that with the direct liquid injection of gasoline into the pre-chamber similar good thermodynamic results as with natural gas can be achieved with the advantage of easier integration of a single fuel system. Due to its significantly improved lean burn capability with relative air/fuel rations of up to λ = 3, combined with low specific indicated NOx raw emissions of less than 0.1 g/kWh, the presented lean-burn combustion system offers excellent conditions for further efficiency improvements of electrified powertrains. WLTP cycle simulations based on measured engine maps for the developed combustion process resulted in a fuel consumption reduction of up to 10%. At the same time, NOx raw emissions below the Euro 6d limit of 60 mg/km can be achieved.

Energy Management System Design for Good Delivery Electric Trike Equipped with Different Powertrain Configurations

This paper demonstrates the design of an electric trike’s energy management system for a goods delivery service via various possible component configurations. A model of the energy management system was first developed based on general engineering vehicles’ equations using Matlab software. Various component configurations, such as the usage of two battery types (lithium iron phosphate (LFP) and lithium nickel cobalt aluminum oxide (NCA)), implementation of three braking strategies (full mechanical, parallel, and series strategies), the presence of a range extender (RE), and various masses of range extenders were simulated by using the model. The driving cycle of the e-trike as input data in the simulation was obtained by driving the vehicle around Bandung City. Speed, distance, and elevation were obtained by using GPS-based software. The simulation results showed that the most efficient and effective component configuration was to use the serial regenerative braking strategy with no RE equipped. This configuration achieved an efficiency of 18.07 km/kWh. However, for a longer route, the usage of a 20-kg RE was required to prevent the state of charge drop below 30%. The NCA with serial regenerative braking and 20-kg RE had an efficiency of 17.47 km/kWh for the complete route.

Design and construction of an experimental test bench for the study of a mechanical system used for coupling power sources

This study investigates the experimental test bench specially created fortestingaplanetary mechanism with two degrees of freedom in the configuration of hybrid electric vehicle powertrain. For the experimental study, it was necessary to build a test bench that consists of a metal frame on which other mechanical and electrical components are positioned: the mechanical coupling system, the thermal engine, a three-phase electric motor and an alternator. The test bench can simulate road load, during which time it is measured with the help ofa torque sensor the mechanical moment developed by the electric motor used in this configuration.

Optimization-Based Fuzzy Energy Management Strategy for PEM Fuel Cell/Battery/Supercapacitor Hybrid Construction Excavator

Fuel cell hybrid electric construction equipment (FCHECE) is known as a promising solution to achieve the goal of energy saving and environment protection. Energy management strategy is a key technology of FCHECE, which splits the energy flow between power sources. This paper presents a novel optimal energy management strategy for a hybrid electric-powered hydraulic excavator system to enhance power performance, power sources lifespan, and fuel economy. As for the proposed powertrain configuration, fuel cell serves as a primary energy source, and supercapacitor and battery are considered as energy storages. The integration of supercapacitor and battery in fuel cell vehicle has advantages of improving power performance and storing the regenerative energy for future usage. An energy management strategy based on fuzzy logic control and a rule-based algorithm is proposed to effectively distribute the power between the three sources and reuse the regenerative energy. Furthermore, the parameters of the fuzzy logic system are optimized using the combination of a backtracking search algorithm which provides a good direction to the global optimal region and sequential dynamic programming as a local search method to fine-tune the optimal solution in order to reduce the hydrogen consumption and prolong the lifetime of the power sources. Simulation results show that the proposed energy management strategy enhances the vehicle performance, improves fuel economy of the FCHECE by 10.919%, increase battery and supercapacitor charge-sustaining capability as well as efficiency of the fuel cell system.

A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles

The energy management strategy (EMS) and control algorithm of a hybrid electric vehicle (HEV) directly determine its energy efficiency, control effect, and system reliability. For a certain configuration of an HEV powertrain, the challenge is to develop an efficient EMS and an appropriate control algorithm to satisfy a variety of development objectives while not reducing vehicle performance. In this research, a comprehensive, multi-level classification for HEVs is introduced in detail from the aspects of the degree of hybridization (DoH), the position of the motor, the components and configurations of the powertrain, and whether or not the HEV is charged by external power. The principle and research status of EMSs for each type of HEV are summarized and reviewed. Additionally, the EMSs and control algorithms of HEVs are compared and analyzed from the perspectives of characteristics, applications, real-time abilities, and historical development. Finally, some discussions about potential directions and challenges for future research on the energy management systems of HEVs are presented. This review is expected to bring contribution to the development of efficient, intelligent, and advanced EMSs for future HEV energy management systems.

Current and Voltage Harmonics in the Powertrain of Electric Vehicles

Switching electronics, such as the drive inverter, generate significant voltage and current harmonics with frequencies as low as a few kilohertz which are propagated throughout the complete electric powertrain and all connected components. With amplitudes up to several volts and dozens of amperes, such harmonics can cause significant problems and lead to performance limitations or even component failures. In order to maintain the quality of the dc-link voltage and guarantee the sound interaction of all components, power electronics must therefore be equipped with filter capacitors damping these harmonics. However, the filter stages account for a large part of the overall component cost and volume. Usually, the required dc-link filters are designed independently for each component on its own without taking into account the configuration of the complete powertrain. This, however, might result in undersizing of filters and possible system failures or contrary to that to oversized and expensive components. In this study, an analytic approach for the computation of the electric harmonics generated by the drive unit of a hybrid fuel cell vehicle will be derived and validated using measurement data. Using this model, the significance of the electric harmonics for the design of the powertrain as well as the sizing process of the filter capacitors will be analyzed.

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>

Innovative power train configurations for aircraft auxiliary power units focused on reducing carbon footprint

Auxiliary power units constitute a non-negligible source of pollution not only in flight conditions but also at airports and their surrounding areas. This work compiles a technical feasibility assessment focused on the analysis of innovative alternatives to the traditional aircraft gas turbine based auxiliary power unit arrangement. The proposed innovations involve both new power train configurations and the use of different fuels, with the purpose of reducing both the primary energy consumption and the environmental impact of these systems. The core innovation proposed here consists in the use of a supercharged rotary engine as an alternative configuration to traditional auxiliary power units in aircraft. The rotary engine can achieve higher thermal efficiencies than gas turbines, which corresponds to a lower specific fuel consumption and, thus, a lower carbon footprint. Additionally, the proposed alternatives will involve hybrid configurations, providing thermal energy generation with electrical storage devices. Finally, the assessed arrangements will be based on the “more electric aircraft” concept, which implies a simplification of the aircraft non-propulsive power system design and allows the addition of batteries to the generating unit. In order to perform this assessment, different thermal models and simulations have been developed, allowing the quantification of the improvements related to the alternative proposed configuration.

Evaluating emissions and sensitivity of economic gains for series plug-in hybrid electric vehicle powertrains for transit bus applications

From the design space explored for series architecture plug-in hybrid electric vehicle transit buses by the authors, one powertrain and control design is selected to provide maximum benefit to investment ratio. Sensitivity analysis is performed for this powertrain configuration. Vehicle parameters (including vehicle mass, coefficient of drag, coefficient of rolling resistance), usage parameters (drivecycle, annual vehicle miles traveled, number of recharges in a day, recharge current, and battery temperature), and economic parameters (fuel price, motor price, and battery price) are varied to understand their effect on the number of required battery replacements, net present value, payback period, and fuel consumption reduction. It is shown that battery temperature has the most significant impact, particularly on the number of battery replacements and net present value and, as such, must be well controlled in practice. It is shown that to maintain the battery at 20°C, for ambient temperatures between −5°C and 45°C, 0.8–1.8% excess fuel is required across all drivecycles for the considered plug-in hybrid electric vehicle transit bus powertrain configuration. In addition, the well-to-wheel emissions of criteria pollutants resulting from the usage of this plug-in hybrid electric vehicle transit bus in Indiana and California are calculated and compared with the conventional transit bus, using the GREET (Greenhouse Gases, Regulated Emissions and Energy Use in Transportation) Model. With a single over night charge, the plug-in hybrid electric vehicle transit bus operating in either Indiana or California produces 50% less CO2 and other greenhouse gases as compared to a conventional transit bus.

Comparison of four-wheel-drive hybrid powertrain configurations

The stringent fuel economy and emission standards enforce sport sedan and sport utility vehicle employ four-wheel-drive (4WD) hybrid powertrain to achieve better fuel economy and acceleration performance simultaneously. This paper reviews and conducts a comparative study of four 4WD hybrid configuration types: series, series-parallel, power-split and compound-split configurations. To make a fair comparison of fuel economy and acceleration performance, an integrated hierarchical optimization framework is presented, which solves the parameters sizing and energy management problem simultaneously. The pareto-front optimal design of each configuration is derived. According to detailed energy analysis, a novel multi-mode configuration is proposed by adding clutches between planetary gear nodes of compound-split configuration. By combining multiple modes, the proposed configuration achieves better fuel-saving and acceleration performance than initial configuration. Moreover, this comparative study takes into consideration the cost, control complexity and off-road performance. The results show that the power-split one adopting three motor/generators (MGs) shows superior fuel economy and acceleration performance than others, while the proposed multi-mode configuration maintains a competing performance with less MGs. In addition, the series-parallel configuration has great comprehensive potential.

On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems

Electric vehicles (EVs) are characterized by a significant variety of possible powertrain configurations, ranging from one to four electric machines, which can have an on-board or in-wheel layout. Multiple models of production EVs have recently been introduced on the market, with 4-wheel-drive (4WD) architectures based on a central motor within each axle, connected to the wheels through a gearbox, a differential, and half-shafts. In parallel, an important body of research and industrial demonstrations have covered the topic of 2-speed transmission systems for EVs, with the target of enhancing longitudinal acceleration and gradeability performance, while increasing the operating efficiency of the electric powertrain. Although several recent studies compare different electric powertrain architectures, to the best of the authors’ knowledge the literature misses a comparison between 2-wheel-drive (2WD) and 4WD configurations for the same EV, from the viewpoint of drivability and energy consumption. This paper targets this gap, by assessing 2WD and 4WD powertrain layouts with central motors, for a case study light passenger car for urban mobility, including consideration of the effect of single- and 2-speed transmission systems. An optimization routine is used to calculate the energy-efficient gear state and/or torque distribution for each considered configuration. For the specific EV, the results highlight the favourable trade-off of the single-speed 4WD layout, capable of reducing the energy consumption during driving cycles by approximately 9% with respect to the conventional 2WD layout with single-speed transmission, while providing satisfactory drivability and good gradeability, especially in low tire–road friction conditions.

An uncertainty analysis of the energy intensity of 37 materials used in automobile manufacturing: Statistical methods and recommendations

Life Cycle Inventories must meet the data quality requirements set out by the goal and scope of a Life Cycle Assessment. The literature on the timing of updating inventory data to meet the data quality requirements is almost non-existent. In this study, data were taken from publicly available reports (corporate, peer-reviewed or governmental), congresses, proceedings, technical papers, international databases and LCAs. Monte Carlo simulations were used to test for variance; regression analysis was used to determine correlation between time and energy intensity. Different conventional automobile power train configurations were examined along with lightweight versions. Monte Carlo simulations were programmed for each automobile power train configuration. Least absolute residuals methods were used to confirm whether the variance seen in simulations was due to time or to other factors such as uncertainty or scattered data. The Monte Carlo simulations identified the variation by material and quantified their significance. Least absolute regression analysis identified and confirmed which materials have a clearly delineated temporal relationship: rubber, polyethylene terephthalate, polypropylene, lead, copper, steel, recycled steel, aluminum and recycled aluminum. Consequently, recommendations are given for the time intervals for necessary data updates which, depending on the material in question, are between two and five years.

Multidisciplinary design optimization for hybrid electric vehicles: component sizing and multi-fidelity frontal crashworthiness

The electrification of road vehicle powertrains has recently gained growing interest worldwide as an effective solution to comply tightening regulations on CO2 emissions. In hybrid electric vehicles (HEVs), multiple power components, such as internal combustion engine, electric motor/generators, and battery system, are included in the powertrain to improve vehicle performances, thus involving an increase of the powertrain envelope and of the overall vehicle mass. Larger vehicle mass affects the design of the structural framework, which expands its envelope to meet safety requirements. The expansions of both powertrain systems and structural framework are indeed constrained by the limited vehicle widthtrack, which demands for tradeoff studies at early vehicle design phases to achieve a feasible solution while optimizing performances related to different design disciplines. These particularly include proper sizing of power components to achieve optimal fuel economy capabilities and accurate design of structural components to satisfy crashworthiness criteria while minimizing the associated structural mass. In this framework, the achievement of a globally optimal solution requires a cooperative development process that addresses the design problem accounting for all these disciplinary contributions in an integrated manner. This paper proposes a multidisciplinary design optimization (MDO) framework for the preliminary design of a power-split HEV powertrain accounting for crashworthiness requirements. A multidisciplinary feasible optimization architecture is particularly illustrated that includes a sequential scheme to evaluate the disciplines and a direct search method as the global system optimizer. Different sizes are swept for the internal combustion engine, the electric motors, and the planetary gear ratios. A multi-fidelity approach is considered for the crashworthiness analysis, to assess the feasibility of powertrain configurations. Results show that, including crashworthiness analysis in the MDO formulation of the design problem, the identified optimal design differs from the outcome of a powertrain-only based optimization process. Particularly, benefits in terms of global solution feasibility and associated computational cost are achieved. The presented methodology allows to efficiently integrate powertrain analyses and crashworthiness constraints and it is suited for the early design of HEVs.

Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios

In this analysis, life cycle environmental burdens and total costs of ownership (TCO) of current (2017) and future (2040) passenger cars with different powertrain configurations are compared. For all vehicle configurations, probability distributions are defined for all performance parameters. Using these, a Monte Carlo based global sensitivity analysis is performed to determine the input parameters that contribute most to overall variability of results. To capture the systematic effects of the energy transition, future electricity scenarios are deeply integrated into the ecoinvent life cycle assessment background database. With this integration, not only the way how future electric vehicles are charged is captured, but also how future vehicles and batteries are produced. If electricity has a life cycle carbon content similar to or better than a modern natural gas combined cycle powerplant, full powertrain electrification makes sense from a climate point of view, and in many cases also provides reductions in TCO. In general, vehicles with smaller batteries and longer lifetime distances have the best cost and climate performance. If a very large driving range is required or clean electricity is not available, hybrid powertrain and compressed natural gas vehicles are good options in terms of both costs and climate change impacts. Alternative powertrains containing large batteries or fuel cells are the most sensitive to changes in the future electricity system as their life cycles are more electricity intensive. The benefits of these alternative drivetrains are strongly linked to the success of the energy transition: the more the electricity sector is decarbonized, the greater the benefit of electrifying passenger vehicles.

Minimum-Fuel Energy Management of a Hybrid Electric Vehicle via Iterative Linear Programming

This paper presents models and optimization algorithms to compute the fuel-optimal energy management strategies for a parallel hybrid electric powertrain on a given driving cycle. Specifically, we first identify a mixed-integer model of the system, including the engine on/off signal and the gear-shift commands. Thereafter, by carefully relaxing the fuel-optimal control problem to a linear program, we devise an iterative algorithm to rapidly compute the minimum-fuel energy management strategies including the optimal gear-shift trajectory. We validate our approach by comparing its solution with the globally optimal one obtained solving the mixed-integer linear program and with the one resulting from the implementation of the optimal strategies in a high-fidelity nonlinear simulator. We showcase the effectiveness of the presented algorithm by assessing the impact of different powertrain configurations and electric motor size on the achievable fuel consumption. Our numerical results show that the proposed algorithm can assess fuel-optimal control strategies with low computational burden, and that powertrain design choices significantly affect the achievable fuel consumption of the vehicle.

Torsional vibration analysis of a planetary gear type antiresonant vibration isolator using transfer matrix method, Part II: Application to automotive powertrain

In this research, an analytical modeling and analysis approach called the transfer matrix method is applied to study a planetary gear type dynamic antiresonant vibration isolator (PG-DAVI) for complex rotational systems. In this part 2 of series papers, the transfer matrix method is applied to an automotive powertrain system with an embedded PG-DAVI to characterize torsional vibration attenuation. First, the internal configuration of an automotive powertrain system is described as a baseline system, and its modal characteristics and harmonic responses are calculated. Then, two additional powertrain systems modified with a PG-DAVI and a classical dynamic absorber are presented. Finally, the effectiveness of vibration suppression in the powertrain system using a PG-DAVI is investigated by comparing the modal characteristics, harmonic responses, and time responses of the three powertrain configurations using the transfer matrix method as well as conventional analytical and numerical methods.

Electronic Differential Control of Independent Wheel Drive NEV using Drive Current Balancing Strategy

This paper presents the design and real-time implementation of an electronic differential speed control system (EDSC) for a neighborhood electric vehicle (NEV) with a decentralized power train configuration. The EDSC supervises the desired speed variation of the drive motors of the vehicle at various turning trajectories. The core focus of this design is to reduce the system complexity, computation delay and design expenses with an aid of a newly proposed drive current balancing algorithm (DCBA). The embedded DCBA based EDSC allocates the necessary torque to each wheel solely depending on the motor current variables. Thus, it eliminates the necessities of typical feedback variables-steering and speed to control the EDSC. The developed system performance is being investigated in situ real time on-board experiment. Results in the context of response time, design simplicity and performance reveal the effectiveness of the proposed framework.

Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses

Powertrain configuration plays an important role in the performance of plug-in hybrid electric buses. Current designs are the compromise between energy efficiency, dynamic ability, shifting smoothness and manufactural cost. To balance the above requirements, this research proposes a novel dual motor powertrain for plug-in hybrid electric buses. The efficiency improvement is compared to the conventional plug-in parallel hybrid electric buses with a single motor powertrain. Parameter designs of system components guarantee two configurations equivalently. To maximize the benefits of the proposed powertrain, this paper introduces an energy management strategy which coordinates enumeration method and dynamic programming to build the optimal maps of powertrain operation. The enumeration method determines the working points of power sources and gear states in all possible modes according to vehicle speed and power. The dynamic programming then selects the most suitable mode with the consideration of gear shifting and mode change in the optimal maps. Simulation results show that the dual motors work in peak efficiency region much more frequently than the single motor in different conditions. Therefore, the total energy cost of dual motor powertrain for entire driving cycles decreases significantly in comparison with the single motor powertrain, 6.5% in the LA92 and 6.7% in the Urban Dynamometer Driving Schedule.

Comparing Fuel Consumption and Emission Levels of Hybrid Powertrain Configurations and a Conventional Powertrain in Varied Drive Cycles and Degree of Hybridization

Hybrid electric powertrains in automotive applications aim to improve emissions and fuel economy with respect to conventional internal combustion engine vehicles. Variety of design scenarios need to be addressed in designing a hybrid electric vehicle to achieve desired design objectives such as fuel consumption and exhaust gas emissions. The work in this paper presents an analysis of the design objectives for an automobile powertrain with respect to different design scenarios, i. e. target drive cycle and degree of hybridization. Toward these ends, four powertrain configuration models (i. e. internal combustion engine, series, parallel and complex hybrid powertrain configurations) of a small vehicle (motorized three wheeler) are developed using Model Advisor software and simulated with varied drive cycles and degrees of hybridization. Firstly, the impact of vehicle power control strategy and operational characteristics of the different powertrain configurations are investigated with respect to exhaust gas emissions and fuel consumption. Secondly, the drive cycles are scaled according to kinetic intensity and the relationship between fuel consumption and drive cycles is assessed. Thirdly, three fuel consumption models are developed so that fuel consumption values for a real-world drive cycle may be predicted in regard to each powertrain configuration. The results show that when compared with a conventional powertrain fuel consumption is lower in hybrid vehicles. This work led to the surprisingly result showing higher CO emission levels with hybrid vehicles. Furthermore, fuel consumption of all four powertrains showed a strong correlation with kinetic intensity values of selected drive cycles. It was found that with varied drive cycles the average fuel advantage for each was: series 23 %, parallel 21 %, and complex hybrids 33 %, compared to an IC engine powertrain. The study reveals that performance of hybrid configurations vary significantly with drive cycle and degree of hybridization. The paper also suggests future areas of study.

Optimal energy management and shift scheduling control of a parallel plug-in hybrid electric vehicle

This paper deals with design of a control strategy for a parallel powertrain configuration of a plug-in hybrid electric vehicle (PHEV). The control strategy is aimed at minimising fuel consumption and number of gear shift events for a wide range of driving cycles, while keeping the battery state-of-charge within allowable range. A control-oriented backward-looking model of PHEV powertrain is used as a design basis. The control strategy combines a rule-based controller with an equivalent consumption minimisation strategy (ECMS). The ECMS uses both transmission gear ratio and engine torque as control variables, thus eliminating a need for designing a separate gear shift scheduling strategy and exploiting a full potential of powertrain efficiency improvement. The overall control strategy is designed for different characteristic operating regimes including charge depleting, charge sustaining, and blended regimes. The strategy is verified by computer simulations against globally optimal benchmark obtained by using the dynamic programming-based optimisation, whose results are also used for fine tuning of controller parameters.