Comparative analysis of forward-facing models vs backward-facing models in powertrain component sizing

Powertrain size optimisation based on vehicle class and usage profile is advantageous for reducing emissions. Backward-facing powertrain models, which incorporate scalable powertrain components, have often been used for this purpose. However, due to their quasi-static nature, backward-facing models give very limited information about the limits of the system and drivability of the vehicle. This makes it difficult for control system development and implementation in hardware-in-the-loop (HIL) test systems. This paper investigates the viability of using forward-facing models in the context of powertrain component sizing optimisation. The vehicle model used in this investigation features a conventional powertrain with an internal combustion engine, clutch, manual transmission, and final drive. Simulations that were carried out have indicated that there is minimal effect on the optimal cost with regards to variations in the driver model sensitivity. This opens up the possibility of using forward-facing models for the purpose of powertrain component sizing

Comparing of single reduction and CVT based transmissions on battery electric vehicle

With the deterioration of the air pollution, growing public concerns over the exhaustion of global fossil energy and the explosive growth of passenger vehicles, the improvement and popularity of electric vehicles (EVs) have increased in market share. The primary goal of EV powertrain design is achieving the same performance, e.g. launching and driving range, as that of Internal Combustion Engine vehicles. To realize this target, a novel propulsion system is proposed in this paper. A comparison of driving performance and energy saving are completed among single reduction, continuously variable transmission (CVT) and proposed system on EVs. The simulation results show that the optimized motor propulsion system has a significant improvement on battery energy saving, range extension and vehicle cost

Influence of a CVT on the fuel consumption of a parallel medium-duty electric hybrid truck

Hybrid electric vehicles are being developed to reduce the pollutant emissions and the fossil-fuel consumption of transportation. Innovative technologies are inserted to improve the performance of hybrid vehicles, including trucks and buses. Thereby, trends towards gear shifting automation motivate the research on replacing a discrete conventional Automated Manual Transmission (AMT) with a Continuously Variable Transmission (CVT). Theoretically, such a transmission enables better operation points of the thermal engine, and therefore a reduction of its fuel consumption and emissions. However, the conventional (hydraulic actuated) CVT efficiency during quasi-stationary operation is typically lower than the efficiency of a classical discrete gearbox, which leads to higher fuel consumption. This paper is focused on the study of the interests of a CVT for a medium-duty Hybrid Electric Truck (HET). The complete model and control of CVT-based and AMT-based HET are described in a unified way using Energetic Macroscopic Representation (EMR). These models are transformed to backward-models to be computed by the Dynamic Programming Method (DPM). Such a method leads to define the (off-line) optimal energy management strategies for a fair comparison of both hybrid trucks. For the studied driving cycle, the hybridization allows a fuel saving of 10% with an AMT and 3% with a CVT. The fuel consumption is higher for the CVT-based HET in comparison with the AMT-based HET due to the lowest efficiency of the CVT (85%) compared to the AMT (around 92%). However, future (on-demand) CVTs with an increased efficiency could be a solution of interest to reduce the fuel consumption of such applications. The developed method can be used to test these new CVTs, other vehicles or other driving cycles

Optimization of shift schedule for hybrid electric vehicle with automated manual transmission

Currently, most hybrid electric vehicles (HEVs) equipped with automated mechanical transmission (AMT) are implemented with the conventional two-parameter gear shift schedule based on engineering experience. However, this approach cannot take full advantage of hybrid drives. In other words, the powertrain of an HEV is not able to work at the best fuel-economy points during the whole driving profile. To solve this problem, an optimization method of gear shift schedule for HEVs is proposed based on Dynamic Programming (DP) and a corresponding solving algorithm is also put forward. A gear shift schedule that can be employed in real-vehicle is extracted from the obtained optimal gear shift points by DP approach and is optimized based on analysis of the engineering experience in a typical Chinese urban driving cycle. Compared with the conventional two-parameter gear shift schedule in both simulation and real vehicle experiments, the extracted gear shift schedule is proved to clearly improve the fuel economy of the HEV

Energetic Macroscopic Representation and inversion-based control of a CVT-based HEV

A Continuous Variable Transmission (CVT) is introduced in the simulation model of a Hybrid Electric Vehicle (HEV). The CVT-based vehicle simulation and its control are deduced from the Energetic Macroscopic Representation (EMR). Simulations are provided to show the interest of the CVT in term of fuel consumption

A toolbox for multi-objective optimisation of low carbon powertrain topologies

Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research

Design of CVT-based hybrid passenger cars

In this paper the hybridization of a small passenger car equipped with a Continuously Variable Transmission (CVT) is investigated. Designing a hybrid drive train is a multi-objective design problem. The main design objectives are fuel consumption, emissions, and performance. However, it is difficult to find a global optimal integral design solution due to the interdependence of design choices (parameters) regarding the drive train topology, component sizes, component technologies, and the control strategy, and the unknown sensitivity of the design objectives to the design parameters. In this work a parametric optimization procedure is presented in solving the design problem, where the main design objective is fuel consumption. The effects of parameter variation on the fuel consumption have been investigated. Furthermore, a reduced hybrid drive train model is introduced with which the effects of design parameter variation is studied very quickly and with an average error of less than 1.6%