A Reinforcement Learning Approach to Powertrain Optimisation

A strategy to reduce computation time and improve minimisation performance in the context of optimisation of battery electric vehicle power trains is provided, motivated by constraints in the motor manufacturing business. This paper proposes a holistic design exploration approach to investigate and identify the optimal powertrain concept for cars based on the component costs and energy consumption costs. Optimal powertrain design and component sizes are determined by analysing various powertrain configuration topologies, as well as single and multi-speed gearbox combinations. The impact of powertrain combinations on vehicle attributes and total costs is investigated further. Multi-objective optimisation in this domain considers a total of 29 component parameters comprised of differing modalities. We apply a novel reinforcement learning-based framework to the problem of simultaneous optimisation of these 29 parameters and demonstrate the feasibility of this optimisation method for this domain. Our results show that, in comparison to single rear motor setups, multi-motor systems offer better vehicle attributes and cheaper total costs. We also show that load points with front and back axle motors may be shifted to a greater efficiency zone to achieve decreased energy consumption and expenses.