Optimal Powertrain Design Research Articles

Modular and Scalable Powertrain for Multipurpose Light Electric Vehicles

Light electric vehicles are best suited for city and suburban settings, where top speed and long-distance travel are not the primary concerns. The literature concerning light electric vehicle powertrain design often overlooks the influence of the associated driving missions. Typically, the powertrain is initially parameterized, established, and then evaluated with an ex-post-performance assessment using driving cycles. Nevertheless, to optimize the size and performance of a vehicle according to its intended mission, it is essential to consider the driving cycles right from the outset, in the powertrain design. This paper presents the design of an electric powertrain for multipurpose light electric vehicles, focusing on the motor, battery, and charging requirements. The powertrain design optimization is realized from the first stages by considering the vehicle’s driving missions and operational patterns for multipurpose usage (transporting people or goods) in European urban environments. The proposed powertrain is modular and scalable in terms of the energy capacity of the battery as well as in the electric motor shaft power and torque. Having such a possibility gives one the flexibility to use the powertrain in different combinations for different vehicle categories, from L7 quadricycles to light M1 vehicles.

Marine vessel powertrain design optimization: Multiperiod modeling considering retrofits and alternative fuels

Over the coming decades, maritime transportation will transition from fossil hydrocarbon fuels to hydrogen, ammonia, and synthetic hydrocarbon fuels produced using renewable electricity as the primary energy source. In this context, a shipowner needs to identify a cost-efficient plan for the adoption of alternative fuels and onboard energy conversion system retrofits. This paper presents a multiperiod decision model for the selection of energy system components under increasingly stringent CO2 emissions regulations and cost forecasts over a multidecade planning horizon. The model considers the choice of newbuild architecture, timing of retrofits, component sizes, and allocation of fuels to converters with the objective of minimizing total cost of ownership (TCO). The decision problem is formulated as a discrete time multiperiod mixed-integer linear program. The application of the model is numerically illustrated for a Baltic Sea roll-on/roll-off ferry. The main findings are: (i) modifying the energy system with retrofits obtains 43% lower TCO compared to fuel switching alone; (ii) batteries contribute to 23% lower TCO; (iii) optimal component installation period can be shorter than their maximum lifetime; (iv) running an engine with hydrogen is favored over fuel cells and (v) hybrid propulsion is the key future-proofing design choice for short sea vessels.

Unified Mode-Based Description of Arbitrary Hybrid and Electric Powertrain Topologies

To find the optimal powertrain design for hybrid and electric vehicles in terms of energy efficiency and performance, automatised optimisation tools are often applied. These require a mathematical model of the powertrain configuration, which is usually done on the level of components, such as planetary gear sets (PGSs), clutches and brakes. This, however, leads to a huge design space with lots of functional redundancy. In fact, each functional powertrain mode can be realised by various component combinations. In this paper, a novel mode-based modelling technique for arbitrary hybrid and electric powertrain configurations with up to one internal combustion engine (ICE) and two electrical machines (EMs) is proposed. This allows to easily analyse multi-mode hybrid and electric powertrains on a purely functional basis. Additionally, it reduces mode describing parameters to the minimum amount required. First, an existing graphical representation of hybrid powertrains with one ICE and one EM is analysed and formalised. Based on that, virtual powertrain nodes are defined. These are used to compose all hybrid and electric powertrain modes. Five of them are presented in detail and plotted within a three-dimensional ratio plot. Using a first case study, it is shown how the ratio plot can be used to analyse arbitrary multi-mode powertrains. Within a second case study, it is demonstrated how optimal parameter sets for single-mode powertrain configurations can be identified using the derived model.

Electric Motor Design Optimization:A Convex Surrogate Modeling Approach

This paper instantiates a convex electric powertrain design optimization framework, bridging the gap between high-level powertrain sizing and low-level components design. We focus on the electric motor and transmission of electric vehicles, using a scalable convex motor model based on surrogate modeling techniques. Specifically, we first select relevant motor design variables and evaluate high-fidelity samples according to a predefned sampling plan. Second, using the sample data, we identify a convex model of the motor, which predicts its losses as a function of the operating point and the design parameters. We also identify models of the remaining components of the powertrain, namely a battery and a fixed-gear transmission. Third, we frame the minimum-energy consumption design problem over a drive cycle as a second-order conic program that can be efficiently solved with optimality guarantees. Finally, we showcase our framework in a case study for a compact family car and compute the optimal motor design and transmission ratio. We validate the accuracy of our models with a high-fidelity simulation tool and calculate the drift in battery energy consumption. We show that our model can capture the optimal operating line and the error in battery energy consumption is low. Overall, our framework can provide electric motor design experts with useful starting points for further design optimization.

Influence of Omitted Variables in Consumer Choice Models on Engineering Design Optimization Solutions

Abstract This paper examines the impact of omitted variable bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into optimal design solutions and characterize properties of optimization problems that affect the magnitude of the resulting error in solutions. We then demonstrate the impact of OVB on optimal designs using a case study of automotive powertrain design optimization. In the case study, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%), which propagates up to 21% error in the optimal engine size.

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving.

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>

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the on-track performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study; especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

Parameter-adaption for a vehicle dynamics model for the evaluation of powertrain concept designs

The powertrain design of multi-motor electric vehicles directly affects not only costs, consumption and acceleration, but also the handling of a vehicle. Therefore, a holistic powertrain design optimization needs to include a vehicle dynamics model in its objective function. While the parameters for the powertrain model result from the design variables that describe the powertrain, the parameters for the vehicle dynamics model must be adapted in a feasible way to ensure comparable results. Therefore, the authors present a method on how to adaptively parametrize a double-track vehicle dynamics model for the use in powertrain design optimization. Automated design calculations for all main chassis and suspension parts are used to determine the parameters for the model. A parameter variation proves the plausibility of the approach. The results show that an adaption of the suspension and chassis parameters due to changes in the powertrain make results more comparable but do not compensate for the effects on the vehicle handling. In particular, the steady state longitudinal load distribution still has major influences on the vehicle handling.

Modeling and control of a waste heat recovery system for integrated powertrain design optimization

Abstract The growing demand for increased vehicle efficiency has led to the introduction of waste heat recovery techniques in vehicle powertrains, with the organic Rankine cycle as the most interesting technique for automotive applications. In order to integrate the design of the waste heat recovery system into the design optimization of the full powertain, a novel modeling approach is suggested for capturing the key dynamics of a waste heat recovery system with low computational effort and number of design parameters, and which is scalable with the individual components of the cycle. The model is compared with a detailed finite volume model for a given organic Rankine cycle. Secondly, the organic Rankine cycle is integrated into a parallel hybrid vehicle model, and an optimization routine is proposed for obtaining the optimal control trajectories for the powertrain over a drive cycle. The main purpose of the waste heat recovery model designed in this paper in combination with the proposed optimization algorithm is to be used for powertrain design optimization studies.

Influence of the Main Design Factors on the Optimal Fuel Cell-Based Powertrain Sizing

The design of the optimal power distribution system (PDS or powertrain) for fuel cell-based vehicles is a complex task due to PDS comprising one or more power converters, several types of secondary energy sources, a fuel cell, several control loops, and protections, among others. The optimized powertrain design tries to minimize the mass, volume, and cost, and also to improve system efficiency, fuel economy (both hydrogen and electricity), and vehicle autonomy. This paper analyzes the influence of four different factors that deeply affect the optimal powertrain design, in particular: the minimum power delivered by the fuel cell, the storage of the recovered energy from the regenerative braking periods, the battery technology, and the maximum battery state-of-charge variation. The analysis of these factors is carried out over a set of 9 different fuel cell-based architectures applied to a light vehicle, and a 10th architecture corresponding to a pure electric vehicle. This analysis provides the knowledge of how these design factors affect the mass, volume, and cost of the optimal power distribution architectures, and how they can be considered in the design.

Optimal Powertrain Design through a Virtual Development Process

The ever more stringent global CO2 and pollutant emission regulations imply that the optimization of conventional powertrains can only provide partial reductions in fleet emissions. Vehicle manufacturers are therefore responding by increasing the electrification of their powertrain portfolios. This in turn, results in higher levels of electrification of the individual powertrain units. The increase in electric power leads to a comprehensive range of possible technologies—from 48 V mild hybrids to pure electric concepts. The powertrain topology and the configuration of the electrical components of a hybrid powertrain play a decisive role in determining the overall efficiency when considering the individual market requirements. Different hybrid functions as well as performance and customer requirements are determined from statutory cycles and in customer operation. A virtual development chain that is based on MATLAB/Simulink then represents the steps for the identification, configuration, and evaluation of new electrified powertrains. The tool chain presented supports powertrain development through automated conceptualization, design, and evaluation of powertrain systems and their components. The outcome of the entire tool chain is a robust concept decision for future powertrains. Using this methodical and reproducible approach, future electrified powertrain concepts are identified.

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.

Conceptual design of battery electric vehicle powertrains

Alternative vehicle powertrain systems are currently the most promising solutions to meet the energetic and ecological challenges in the individual mobility. To accomplish the keen competition, considering the decreasing lead time and costs for a vehicle design and the aim to develop the optimal powertrain design, methods that ensure a quick evaluation of various configurations of a powertrain system are required. Although utilising powertrain simulation models is a common approach in powertrain design, there is a need for quick generalised analytical models. In this paper, an analytical model is developed using the example of a battery electric vehicle (BEV) with the aim of a quick exploration of powertrain design solutions. Moreover, a comparison between the analytical method and a numerical simulative method with respect to their accuracies and complexities is shown. Finally, a Pareto frontier analysis of energy consumption, driving performance, range and cost for BEVs is accomplished.

Efficiency, cost and life cycle CO2 optimization of fuel cell hybrid and plug-in hybrid urban buses

Fuel cell powered hybrid electric vehicles (FC-HEV) and plug-in hybrid electric vehicles (FC-PHEV) are being addressed by the automotive industry as improved and more sustainable alternative technologies relatively to conventional vehicles. Nevertheless, hybrid propulsion raises new challenges in designing the vehicle powertrain. This study highlights the significance of the driving conditions and the conflict between the optimization of investment cost, efficiency and life cycle impact (LCA) in powertrain design optimization of these kinds of vehicles. A single-objective (minimization of cost, fuel or LCA CO2eq) and multi-objective genetic algorithms (minimization of the couples cost and fuel, cost and LCA CO2eq, fuel and LCA CO2eq), linked with the vehicle simulation software ADVISOR, are used to optimize the design of powertrain components. The main outcomes of the research are as follows. The optimization of LCA CO2eq emissions and cost are conflicting as well as cost and energy use, what can be observed in the Pareto solutions. The fuel and LCA CO2eq emissions optimization are coupled for pure hybrids but not for plug-in hybrid configurations, due to the electricity consumption. Fuel cell buses can reduce the energy consumption by 58%, and emit 67% less LCA CO2eq than the conventional diesel bus, and achieve compensatory payback of 0.620$/km (depending on the hydrogen price). The FC-PHEV configuration shows more potential for achieving higher operation efficiencies, but the FC-HEV shows to have lower life cycle impact and lower cost in general.

A Generalized Powertrain Design Optimization Methodology to Reduce Fuel Economy Variability in Hybrid Electric Vehicles

Hybrid electric vehicles (HEVs) are considered to be one of the energy-efficient technologies for near-term sustainability of the transportation sector. Over the years, research has focused on improving fuel economy (FE) for a given drive cycle, but FE variability over a realistic range of real-world driving patterns has been generally overlooked, and this can lead to FE benefits not being fully realized in real-world usage. No systematic methodology exists to reduce FE variability by design optimization of powertrain components. This study proposes a methodology of powertrain component optimization to reduce the FE variability due to variations in driving patterns. In the proposed methodology, powertrain components are optimum over a range of driving patterns of different traffic conditions and driving styles simultaneously. The proposed methodology demonstrates the potential to reduce FE variability by up to 34% over six driving patterns of different traffic conditions and driving styles.