Development of Automated Power Unit Control Strategy Calibration - Optimisation Methodology

Abstract

For various powertrain components selection and optimisation goals, the automatic generation, calibration and comparison between algorithms are implemented in this project. This research unlocks the opportunity for multiple optimisation targets, including SOC, components selection, CO2 emission, drive modes and driveability for the hybrid powertrain system from McLaren. For different optimisation objectives, the robustness of the Equivalent Consumption Minimisation Strategy (ECMS) is automatically generated with the robustness validation. The automatic calibration of the torque split strategy is applied to the optimisation of another PHD’s work – the selection of powertrain components. All the simulations are based on McLaren’s powertrain model.

Afterwards, from the research of the McLaren powertrain model, the limitation of the model application is discussed, which leads to the establishment of a brand-new powertrain model. The research on powertrain modelling is discussed and explored; then, the new powertrain model generation is presented in this project. The calibration and validation of the model with the implementation of the control strategy are researched subsequently. The new powertrain model is named with my name YR-Sim.

For the application of the Dynamic Programming (DP) algorithm, the new powertrain model is modified and upgraded with more subsystems and features. All the inputs and outputs of these subsystems are standardised. The calibration and validation of the model with different algorithms guarantee the simulation results are comparable to McLaren’s model with different algorithms and optimisation cases.

Besides the optimisation on CO2 emission and SOC conditions, the driveability is introduced to research deeper on different algorithms. The driveability is still an open question, but in this project, some novelty concepts are introduced. The CO2 emission, SOC condition and driveability are optimised and researched in different drive cycles, including NEDC, WLTC, FTP75 even Nürburgring race track. For different views of driveability, the benefits from the application of ECMS and DP are deep researched.

Because this is a project that focuses on real industry applications, the research on the algorithms shows the pros and cons of these two algorithms in different dimensions. From the view of the industry, the research results bridge the gap between mathematical algorithm research and strategies application, a series of functions for automatic generation, calibration and optimisation of control strategy, and the experiences in complex powertrain modelling, control, and validation are also can be used for future hybrid / electric vehicle platform development. The academic goals for algorithm research include the performance comparison between different algorithms, and the calibration and validation of powertrain model. The industry goals include the algorithm application, robustness test and multicomponents selection in different drive cycles. Both of these two series of requirements are satisfied by the end of this project.

This is an IDE (Institute of Digital Engineering) funded project, McLaren Automotive is the project leader, University of Bath and Ricardo are the collaborative partners. There are two PHDs involved in this project. One focuses on the optimisation of components selection, scaling and architecture; other researches the automatic generation of energy management strategy, control algorithms and calibration. This project is working on the second part, the control algorithm research.

Date of Award	2022
Original language	English
Awarding Institution	University of Bath
Supervisor	Sam Akehurst (Supervisor), Robert Wragge-Morley (Supervisor), Simon Pickering (Supervisor) & Chris Brace (Supervisor)