Gaussian Process Approximate Dynamic Programming for Energy Management of Parallel Hybrid Electric Vehicles

초록

This paper presents two non-parametric Bayesian techniques?Gaussian Process Dynamic Programming (GPDP) and Gaussian Process Dynamic Programming-Receding Horizon Control (GPDP-RHC)?for optimal energy management of parallel hybrid electric vehicles. Hybrid electric vehicles (HEVs) are powered by engine and electric machine and assigning the required traction power to the two sources. It is known as the supervisory control which can be formulated as an optimal control problem. To solve the supervisory optimal control, we adopt the approximate dynamic programming (ADP) with Gaussian processes (GPs) which are used for value function approximation in optimal control problem. High-fidelity models of real-world vehicle data for battery, engine, and electric machine are used to obtain discrete dynamic programming (DP) solutions for a known driving cycle. To overcome limitations in real-time application of DP, we use non-parametric Bayesian function approximation techniques using GPs. The state-value tables obtained by dynamic programming are approximated by Gaussian process regression. Furthermore, the future value function is predicted by GPDP in one-step lookahead with RHC. For demonstration of optimality and efficiency, the proposed GPDP-RHC solution is compared with both the offline global DP solution and real-driving result.