论文信息 - An Open Framework for Human-Like Autonomous Driving Using Inverse Reinforcement Learning

An Open Framework for Human-Like Autonomous Driving Using Inverse Reinforcement Learning

In this paper, a global optimization methodology is described to pre-design an electric vehicle powertrain in order to find the best compromises between components. The modeled system includes a transmission, an electric machine, an inverter and a battery pack. The challenge is to find the dedicated formulations, with the vehicle performance requirements, electric range, and cost calculation that include the whole system without exploding computational time. Bi-objective, range/costs, optimizations with performance constraints are performed to find the potential gain with the system model.

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