Powertrain Configurations Research Articles

Impact of Driving Timing and Road Types on the Operating Regions of Electric Two-wheeler Powertrain Components under Indian Road Conditions

Among the different classes of electric vehicles (EVs), electric two-wheelers (E2Ws) are securing more enticement towards the commutator for regular usage due to their higher suitability in the heterogeneous traffic conditions of India. However, the driving range of the E2Ws is the major concern in increasing its penetration rate in road transportation. To eradicate the range anxiety of commutators by redesigning the current E2W powertrain configuration, this study strives to insights into the operational limits of powertrain elements such as the battery and motor in E2W under multiple operational circumstances such as driving style, pavement, and ambient conditions. Regarding this, the E2W with data acquisition system (DAC) connected with an onboard diagnosis (OBD) port is utilized to acquire the data related to powertrain components and vehicles for four driving trips (trips 1–4) under three major road segments (Rural, expressway, and urban) of India. The operating and computed performance metrics, comprising battery voltage and current, state-of-charge (SOC), energy consumption, motor current, speed, efficiency, and so on, are then compared throughout driving trips and road segments. The mean velocity of E2W for trips 1 to 4 is 31.91, 33.68, 39.03, and 35.58 km/h, respectively. The results disclosed that total energy consumption and energy consumption per km. are higher during trip 3 due to higher average vehicle speed. Also, it has a higher depth-of-discharge (DOD) of 83.91 % than other driving trips. Further, a higher rate of SOC drop, energy consumption and battery temperature rise are found at highway road segments.

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

A comprehensive design pipeline for FCEV powertrain configuration considering the evolutionary development process and extreme economic performance

Due to stringent emission regulations, the zero-emission hydrogen fuel cell electric vehicles (FCEVs) are gaining significant attention, but their slow power response requires careful selection of auxiliary energy sources and power systems to optimize energy efficiency. This paper presents a comprehensive design pipeline for the FCEV powertrain, encompassing methods for powertrain configuration design, feasible domain design for component parameters, and parameter matching. Initially, a powertrain configuration design method based on evolutionary game theory is introduced, forecasting trends in energy-efficient powertrain configurations using a dynamic cost function that adjusts based on the proportions of different configurations. Next, a feasible domain design method is developed to establish fixed constraints on component parameters, ensuring a fair balance between performance needs and overall costs, including development and lifecycle expenses associated with the powertrain configuration. Subsequently, dynamic programming (DP) is used for parameter matching, assessing the utmost energy-saving potential. The cost function in this method incorporates the degradation cost of powertrain components, underscoring the economic performance of the target FCEV over its lifespan. Finally, simulation assessments indicate that the proposed design pipeline effectively identifies the optimal powertrain configuration for FCEVs, endowing each possible configuration with exceptional economic efficiency across different component parameters.

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

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

System configuration, control development, and in-field validation of a hybrid electric wheel loader featuring electrically-boosted engine

This paper proposes an innovative next-generation wheel loader derived from a production series electric wheel loader by John Deere. The proposed wheel loader features a series hybrid electric powertrain with an energy storage system and an electrically-boosted turbocharged diesel engine. A detailed system design inclusive of powertrain configuration and control development including a novel heuristics-based vehicle power management is presented. A simulation model was created to evaluate the potential fuel savings of the proposed wheel loader, revealing a fuel saving potential of over 10% when compared to the baseline configuration. Subsequent in-field testing of an actual demo wheel loader verified its ability to achieve over 10% fuel savings, thereby confirming the simulation outcomes, demonstrating the promise of the proposed hybrid powertrain, and validating the efficacy of the control system developed in this research.

Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.

Rapid Decision-Making Tool for Electric Powertrain Sizing for Motorcycles during New Product Development

As part of the intergovernmental and public interventions to reduce carbon dioxide emissions, there are no existing regulations to ban the sale of petrol motorcycles (PM), but it is expected that motorcycle regulations will follow car regulations with several years of delay. There is an emerging trend in motorcycle uptake, which will lead to new development projects with existing brands, and new brands, and will clearly increase the need for development tools that satisfies design challenges specific to electric motorcycles (EM) and electric powertrains. There is significant importance in motorcycle design to quantify the vehicle-level performance indicators and specifications, which are not limited to total vehicle mass, range, acceleration performance, and top speed. Those performance indicators should be quantified for different powertrain configurations and component selections to identify the most suitable configuration for the specific motorcycle development. In this paper, an innovative powertrain sizing approach is proposed to provide solutions for EMs against the design challenges specific to electric motorcycles. The innovative approach is to apply the practice of design space exploration (DSE) in resilient system design (RSD) to EM development. As a proof of concept, a case study of battery sizing is presented, in which a powertrain sizing tool is used to identify battery pack sizing requirements using requirement-based design (RBD), sensitivity analysis and DSE. The case study shows that the RBD approach allows EM product developers to identify a single solution, while DSE clearly demonstrates the trade-off between different configurations, taking multiple design variables into account. The tool prioritises high accessibility and high confidence with limited information at the early phases of electric motorcycle powertrain component sizing and selection.

Corrections to “Comprehensive Analysis of Fuel Cell Electric Vehicles: Challenges, Powertrain Configurations, and Energy Management Systems”

Corrections to “Comprehensive Analysis of Fuel Cell Electric Vehicles: Challenges, Powertrain Configurations, and Energy Management Systems”

Comprehensive Analysis of Fuel Cell Electric Vehicles: Challenges, Powertrain Configurations, and Energy Management Systems

Comprehensive Analysis of Fuel Cell Electric Vehicles: Challenges, Powertrain Configurations, and Energy Management Systems

Flight-Validated Electric Powertrain Efficiency Models for Small UASs

Minimizing electric losses is critical to the success of battery-powered small unmanned aerial systems (SUASs) that weigh less than 25 kgf (55 lb). Losses increase energy and battery weight requirements which hinder the vehicle’s range and endurance. However, engineers do not have appropriate models to estimate the losses of a motor, motor controller, or battery. The aerospace literature often assumes an ideal electrical efficiency or describes modeling approaches that are more suitable for controls engineers. The electrical literature describes detailed design tools that target the motor designer. We developed SUAS powertrain models targeted for vehicle designers and systems engineers. The analytical models predict each component’s losses using high-level specifications readily published in SUAS component datasheets. We validated the models against parametric experimental studies involving novel powertrain flight data from a specially instrumented quadcopter. Given propeller torque and speed, our integrated models predicted a quadcopter’s battery voltage within 5% of experimental data for a 5+ min mission despite motor and controller efficiency errors up to 10%. The models can reduce development costs and timelines for different stakeholders. Users can evaluate notional or existing powertrain configurations over entire missions without testing any physical hardware.

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.

Energy Management Strategies for Series-Parallel Hybrid Electric Vehicles Considering Fuel Efficiency and Degradation of Lithium-Ion Batteries

<div>Lithium-ion batteries are the most crucial component of hybrid electric vehicles (HEVs) with respect to cost and performance. In this article, a new energy management strategy (EMS) is developed that improves fuel efficiency (FE) and suppresses the degradation of the battery. A hybridized two-layer algorithm that combines multi-objective nonlinear model predictive control (NMPC) with a rule-based (RB) algorithm is proposed as a new EMS that is called RB-NMPC. The RB-NMPC is designed to optimize the torque split between the engine and electric motors while maintaining the maximum and minimum constraints of each component. The proposed EMS is incorporated into control-oriented vehicle models, and their performances are analyzed for different driving cycles by comparing with RB, dynamic programming (DP), and NMPC. In addition, the RB-NMPC algorithm is applied for two different powertrain configurations of HEV, P0P2 and P1P2 configurations for both an Urban Dynamometer Driving Schedule (UDDS) and a Highway Fuel Economy Test (HWFET). For P0P2, the results show that RB-NMPC outperforms other methods for UDDS with an FE that is 4.7% higher than that of RB and is the closest to that of DP, which is an optimal standard that is limited for real-time application due to its complexity among others. The capacity loss of the battery using RB-NMPC is 19.1% less than that using DP when applied to the UDDS. The FE of P1P2 is higher than that of P0P2, but the similar capacity fade is comparable. RB-NMPC shows the lowest capacity loss for both P0P2 and P1P2 configurations. Parallel comparisons are performed for the HWFET. For the HWFET, the FEs of P0P2 and P1P2 are similar. However, the capacity fades by RB-NMPC are 16.3% and 67.0% reduced compared to that by DP for P0P2 and P1P2, respectively. Finally, to verify the effectiveness of the RB-NMPC in reducing battery aging, the currents from DP and RB-NMPC EMSs are applied to pouch-type lithium-ion batteries and tested for multiple UDDSs using a battery test station. The results demonstrate that the RB-NMPC can effectively reduce battery aging.</div>

Universal Bridgeless Nonisolated Battery Charger With Wide-Output Voltage Range

The existing onboard battery chargers (OBCs) are incompatible to adopt for different electric vehicle (EV) powertrain configurations owing to different battery pack voltages and input power supplies. This letter proposes a new universal battery charger to charge EV batteries whose output voltage is ranging from 48 to 450 V. A dual-loop distinct current controller with a seamless transition for boost and buck operations is designed, which regulates the output voltage and maintains the unity power factor at the grid side. Furthermore, an active harmonic minimization method is incorporated with the designed controller to attenuate the prominent third-harmonic in the reference current and eradicate subharmonic oscillations. The performance of the proposed OBC is validated through the MATLAB simulations and experiments on a 1.5-kW laboratory prototype with an universal ac power supply and compared with existing OBCs.

Joint Optimization of Configuration, Component Sizing, and Energy Management for Input-Split Hybrid Powertrains

Configuration design, component sizing, and energy management are intertwined. However, the full potential of hybrid powertrains has not yet been thoroughly investigated due to the multi-dimensional design space. Therefore, in this paper, a joint optimization method combining particle swarm optimization (PSO) and convex programming (CP) is proposed for the first time. In the outer loop, the PSO algorithm searches for the optimal design for the powertrain configuration. In contrast, the component size and power management are simultaneously optimized by the CP algorithm in the inner loop. A combined driving cycle is designed to integrate both acceleration and energy-saving performance, where an extreme acceleration driving process is intended to act as the acceleration constraint. The results show that, compared to Prius 1st, the acceleration and total cost-saving of the optimal 1-PG (planetary gear) powertrain are improved by 13.09% and 11.08%, respectively. In particular, the acceleration and total cost-saving of the optimal 2-PG powertrain (MG2-RSG) are upgraded by 34.23% and 15.18%, respectively. Finally, the effectiveness and efficiency of the proposed method are verified by comparing it with dynamic programming (DP) and multi-objective evolutionary algorithm based on the decomposition (MOEA/D) method.

Performance simulation of hydrogen-electric transit bus running in a mountain environment

As is well known, the transport sector accounts for a third of all final energy consumption in the EU and is responsible for more than a quarter of total greenhouse gas emissions, making it one of the main contributors to climate change. Reducing the negative effects of transport is a strategic objective of the EU, which has put in place a series of actions to promote cleaner and more efficient transport modes, using more sustainable technologies, fuels, and infrastructure. The development of more sustainable and zero-emission collective transport solutions implies the use of novel propulsion systems capable of using energy carriers produced from renewable energy sources. This article deals with the preliminary design and performance analysis of a hydrogen hybrid passenger bus to be used in a mountainous area characterized by mainly curvilinear routes with high gradients both uphill and downhill, within an experimental context of technological-energy innovation. The study is part of a wider project (LIFE3H) co-funded by the European Union, which, among other objectives, intends to lay the bases for the development of a Hydrogen Valley (integrated hydrogen production, storage, and use site), through public hydrogen transport and refuelling stations in the mountain area of “Altopiano delle Rocche” in Abruzzo (Italy). The studied vehicle power-train configuration is based on an electric motor fed by a hybrid power unit consisting of a hydrogen fuel cell functionally coupled with an electrochemical battery. A minibus configuration run over a round trip path according to the real traffic conditions is simulated and main vehicle powertrain components are sized. Finally, hydrogen consumption for traction is estimated.

Half-Power Prediction and Its Application on the Energy Management Strategy for Fuel Cell City Bus

The fuel cell hybrid powertrain is a potential power supply system for fuel cell vehicles. The underlying problem is that the fuel cell vehicles encounter exhaustive hydrogen consumption. To effectively manage hydrogen consumption, the aim is to propose fuel cell city bus power and control system. The underlying idea is to determine the target power of fuel cell through simulation study on fuel cell and battery energy management strategy and road test verifications. A half-power prediction energy management strategy is implemented to predict the target power of the fuel cell in the current time step based on the demand power of the vehicle and the state of charge (SOC) of the battery in the previous time steps. This offers better understanding of the correlation between fuel cell power and vehicle drive cycle for enabling effective power supply management. The research results show that the half-power prediction energy management strategy effectively reduces the hydrogen consumption of the vehicle by 7.1% and the number of battery cycle by 6.0%, compared to the stepped management strategy of battery SOC. When applied to a 12-m fuel cell city bus—F12, specially designed and manufactured for the Winter Olympic Games in 2022—the fuel economy of 3.7 kg/100 km is achieved in urban road conditions. This study lays a foundation for providing the powertrain configuration and energy management strategy of fuel cell city bus.

Configurations and Control Strategies of Hybrid Powertrain Systems

The configuration and control strategy of hybrid powertrain systems are significant for the development of hybrid electric vehicles (HEV) because they significantly affect their comprehensive performance. In this paper, the types, features, and applications of the mainstream hybrid powertrain configurations on the market in recent years are summarized and the effects of different configurations on the comprehensive performance of HEVs are compared. Moreover, the technical routes for each hybrid configuration are highlighted, as configuration optimization methods have become a technical difficulty. In addition, the technological advances in the steady-state energy management strategy and dynamic coordinated control strategy for hybrid powertrain systems are studied. The optimization of the steady-state energy management strategy mainly involves assigning the working point and working range of each power source reasonably. However, with the increase in the complexity of optimization algorithms, real-time control of HEVs still needs to be improved. The optimization of the dynamic coordinated control strategy mainly focuses on the stability and smoothness of the dynamic process involving switching and shifting the working mode. The optimization of the dynamic control process for the system remains to be further improved. It is pointed out that the configurations and strategies should be optimized jointly to obtain a comprehensive improvement in the system performance. This paper provides an informative basis and technical support for the design and optimization of a hybrid powertrain system.

Concept evaluation of a P2 MHEV SUV: application for possible EU7 boundaries

In this work, the experimental results that appeared in the recent published article “Current experimental developments in 48 V-based CI-driven SUVs in response to expected future EU7 legislation” are used to create a proper system simulation model with the simulation platform AVL CRUISE^text {TM} M. This simulation model is then used to perform a system validation in order to evaluate the configuration with a straight-four compression ignition (CI) engine and the selected exhaust aftertreatment system (EAS). The mild hybrid electric vehicle (MHEV) has an 48 V P2 architecture and an 8-gear dual-clutch transmission (DCT) as a powertrain configuration. In addition to evaluating the 48 V potential, the simulation is performed with a conventional 12 V configuration, but also including an electrically heated catalyst (EHC). As boundary conditions for the simulation, we use the different engine operating mode (EOM) calibrations from the test bed to trigger the dedicated operation modes of the internal combustion engine (ICE). For the exhaust aftertreatment system (EAS), an optimization loop is performed to obtain a layout which will be near a serial production. This includes optimizing the heat losses and reducing the thermal mass of the canning. Beside the plant models, a hybrid control unit (HCU) is used, which includes an exhaust aftertreatment system coordinator (EASC). With these functionalities, the EOMs, electrically heated catalyst (EHC), electric machine (EM) and dosing control unit (DCU) are optimized to obtain the lowest possible nitrogen oxides (NOx) with an carbon dioxide (CO_{2}) reduction potential. The targets for the emission limits are defined on the basis of the available information from the Consortium for ultra-Low Vehicle Emissions (CLOVE) and International Council on Clean Transportation (ICCT) proposals.

Comparative Analysis of Single, Double and Quad Electric Vehicle Powertrain Systems

Electric vehicles offer a promising solution to solving environmental problems related to air pollution. The typical powertrain design of an electric vehicle includes a relatively high-speed-low-torque electric motor integrated with some mechanical system to transmit the output mechanical energy to the wheels, such as the gearbox, transmission and differential. Different drivetrain configurations with single, double and quad electric motors can be used in electric vehicles. In this study, it is aimed to compare the effects of different powertrain configurations on the acceleration performance, range and energy consumption of the electric vehicle. Single, dual and quad motor electric vehicle powertrain systems were simulated with MATLAB Simulink according to the New European Driving Cycle (NEDC). The Volvo XC40 Recharge electric car was used as the reference vehicle with the same vehicle characteristics, battery type/capacity and the same total power output by changing only the electric motor powertrain configurations. It has been determined that quadruple electric motor powertrain configuration has lower energy consumption, longer range and better acceleration performance than the single and double electric motor powertrain configurations.

Study on torsional vibration characteristics and suppression of electric vehicles with dual-motor drive system

The battery electric vehicles with dual-motor coupling drive system have serious torsional vibration problems when the driving torque changes rapidly. This paper targets at reducing the negative effect of the torsional vibration with an active control strategy. The torsional vibration model of the dual-motor coupling drive system is established through the lumped mass method. A four-inertia modeling method which can reflect the powertrain configuration characteristics is proposed, by simplifying the traditional lumped mass model, and the feasibility is verified based on the error comparing of the torsional vibration characteristics between the two models. Combined Kalman filter theory, PID control theory and fuzzy control theory, a real-time self-tuning active control strategy is proposed to compensate motor torque by considering vehicle acceleration as the control input. Finally, the four-inertia model with the control strategy is simulated under such conditions as the torque abrupt change and the C-WTVC driving cycle. The simulation results indicated that the proposed active control strategy performs well in the torsional vibration suppression and the control effect is robust to the different structural parameters.