In the design process of electric powertrains, consisting of electric machine, gearbox and power electronics, the requirements regarding performance, package and costs are typically set on system level. This imposes that deduction of component requirements is not unique and component properties interfere with each other. As a component of the powertrain system, the gearbox represents a linking element between the electric machine and drive shafts to the wheels. Through this the available installation space of the gearbox shows manifold characteristics due to multiple possible motor-and power electronics variants as also versatile system installation positions and angles. This space can be utilized by different gearbox variants, which are characterized by gearbox-internal design parameters. They affect gear ratio, configuration of gear wheels, outer shape of the gearbox and therefore the package as well as efficiency and production costs. The high variability of gearbox design parameters and packaging-related aspects lead to a complex problem in the design process. In this context, the present contribution introduces a gearbox design optimization process to support decision-making in the early development phase. For given load-, lifetime-and package-requirements, the introduced differential-evolution-based process delivers design parameters for shafts, gears, bearings and their arrangement to handle efficiency, package and costs in a multi-objective manner. The results are represented by a Pareto front of gearbox designs variants, from which decision makers are able to choose the best and most suitable trade-off. The new approach is exemplarily demonstrated on a single-speed, two-stage helical gearbox with an integrated differential drive, which represents a common gearbox topology for xEV-axle drives.
Electric drive systems consisting of battery, inverter, electric motor and gearbox are applied in hybridor purely electric vehicles. The layout process of such propulsion systems is performed on system level under consideration of various component properties and their interfering characteristics. In addition, different boundary conditions are taken under account, e. g. performance, efficiency, packaging, costs. In this way, the development process of the power train involves a broad range of influencing parameters and periphery conditions and thus represents a multi-dimensional optimization problem. Stateof-the-art development processes of mechatronic systems are usually executed according to the V-model, which represents a fundamental basis for handling the complex interactions of the different disciplines involved. In addition, stage-gate processes and spiral models are applied to deal with the high level of complexity during conception, design and testing. Involving a large number of technical and economic factors, these sequential, recursive processes may lead to suboptimal solutions since the system design processes do not sufficiently consider the complex relations between the different, partially conflicting domains. In this context, the present publication introduces an integrated multi-objective optimization strategy for the effective conception of electric propulsion systems, which involves a holistic consideration of all components and requirements in a multi-objective manner. The system design synthesis is based on component-specific Pareto-optimal designs to handle performance, efficiency, package and costs for given system requirements. The results are displayed as Pareto-fronts of electric power train system designs variants, from which decision makers are able to choose the best suitable solution. In this way, the presented system design approach for the development of electrically driven axles enables a multi-objective optimization considering efficiency, performance, costs and package. It is capable to reduce development time and to improve overall system quality at the same time.
Due to safety considerations, electric axle drives (e-drives) are often equipped with a parking lock system, which prevents vehicle movement while parking in redundancy with the parking brake. In order to integrate the parking lock into the e‑drive, various mounting positions inside the e‑drive are eligible, which have a direct influence on the e‑drive packaging. Furthermore, engaging the parking lock may happen at small vehicle velocities and while driving downhill, leading to high loads on the e‑drive components. These loads depend on the mounting position of the parking lock and have to be considered in the design phase to prevent failure of the system. That way, the designs of shafts, gear wheels and bearings of the gearbox are affected by the parking lock integration. A suboptimally integrated parking lock system can thus lead to undesirably high costs and reduced energy efficiency of the entire e‑drive—all alongside the packaging aspect. Consequently, finding the best suitable parking lock integration for a certain e‑drive is a complex task for the design engineers. To reduce the level of problem complexity, an established computer-based system design method for e‑drives by means of a multi-objective optimization is extended to be capable of considering the parking lock integration. The proposed method is applied to a case study and the impact of the parking lock on the optimality of an exemplary e‑drive system is shown.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.