Convex Mapping Formulations Enabling Optimal Power Split and Design of the Electric Drivetrain in All-Electric Vehicles

摘要

All-electric drivetrains have been identified as a promising alternative to contemporary hybrid vehicle technology. Extending their operational range is key and can be achieved by means of design procedures based on high-fidelity models capturing the dynamical behavior of the electric drivetrain. This paper proposes a dedicated power split embodying a dual electric drive and a model-based strategy to design the drivetrain. Advancements are required in model-based design that can cope with the complexity of the computationally expensive and high-dimensional parametric design problems. We propose a nested optimization approach wherein parameter exploration is attained using an evolutionary algorithm and the optimal power flows are determined by abstracting the high-fidelity behavioral models into appropriate convex loss mappings. This allows for an accelerated design procedure based on convex optimization without compromising accuracy. We size an electric drivetrain for maximal range extension, consisting of a battery stack, buck-boost converter, inverter and mechanically coupled induction motors subjected to variable load conditions. A tractable convex formulation is obtained and optimization time is reduced by 99.3% compared to the traditional approach without convexification. Optimal control of the incorporated power split increases the operational range by 0.7% compared to the isolated operation of a single motor. The proposed methodology thus paves the way for extensive designs of drivetrains and complex mechatronic systems in a general context.