Electric machine co-optimization for EV drive technology development: Integrating Bayesian optimization and nonlinear model predictive control

Abstract

Electric powertrains are becoming increasingly prevalent in various mobile propulsion applications, not only due to legislations for lower CO${}_2$ emissions and local pollution, but also due to growing sustainable consciousness. However, conceptualizing those systems, consisting of component and controller design processes, is a complex task. The complexity itself arises from the amount of requirements for design objectives and use-cases, which can be met inside a multidimensional parameter space. Additionally, system design and evaluation are inherently tied to coupled component and system control strategy optimization. In this context, the paper presents a fully automated active machine learning methodology applied for a combined optimization of electric machine and system controller design, considering system performance, durability, and energy consumption. During this iterative approach a stochastic optimization of a permanent magnet synchronous machine (PMSM) takes place, constrained from a nonlinear model predictive control in a model-in-the-loop system environment. The active learning is covered by a Bayesian optimization algorithm with a Gaussian process regression to determine the most suitable parameter set in terms of exploration and exploitation. To demonstrate the feasibility of this novel methodology, a thermal subsystem from an electrified state-of-the-art powertrain has been used and further optimized regarding PMSM scaling and final gear ratio. Different real-world drive scenarios from highway to city were taken into account to cover typical sport utility vehicle use-cases. It could be shown that the electric machine losses of the optimized system are reduced by up to $32.7\text{\%}$, which equals a consumption of $-0.43\frac{kWh}{100km}$ compared to the reference vehicle. Due to slightly worse operating conditions of the inverter the whole system consumption has been minimized by $-0.35\frac{kWh}{100km}$. Three parameter studies with fixed iteration count have been executed to find the optimal machine diameter to be increased by $25\text{\%}$ and the length slightly reduced by $16\text{\%}$. Moreover, the total gear ratio was adjusted by $-31\text{\%}$ to shift the load points of highest energy conversion into the machine’s optimal efficiency area.