Battery Health-Aware MPC Planning for Autonomous Racing Vehicles

A battery health-aware trajectory planner for autonomous racing vehicles is developed in this work. The proposed scheme is based on a two-layer robust control strategy which the outer layer defines a zonotopic-tube-based model predictive control (MPC), meanwhile, the inner layer combines a linear quadratic zonotopic controller (LQZ) with a zonotopic Kalman filter (ZKF). In addition, a data-driven Takagi-Sugeno (TS) model is trained using an adaptive neuro-fuzzy inference system for control and estimation purposes. For assessing the proposed control scheme a simulated 1/10 Scale RC car is employed.