Generic stochastic particle filter algorithm for predictive energy optimization of a Plug-in Hybrid Electric Vehicle extended by a battery temperature control and implemented on a Hardware-in-the-Loop system. (March 2022) - Aubeck, Franz; Lenz, Martin; Mertes, Simon; Zylka, Kevin; Pischinger, Stefan
Abstract: This work presents an implementation and demonstration of a generic stochastic particle filter-based algorithm (GSPFA) to solve the energy management problem of a Plug-in Hybrid Electric Vehicle (PHEV, P2). The objective is to minimize energy consumption considering the driving mission-specific state of charge and the battery system's desired temperature range. To achieve this objective, a two-level Model Predictive Control (MPC) approach is used for trajectory planning in the distance domain. The control signals are vehicle velocity, torque-split, gear shift, and the internal combustion engine on/off state. The mixed-integer nonlinear optimal control problem is solved by the second MPC. The PHEV model is executed on the real-time system of a Hardware-in-the-Loop test bench with a battery module as a real component to measure physical responses. A reference driving cycle (CADC URM130) without temperature control leads to a temperature increase of the battery of 2.1 K. By allowing a temperature increase of only 1 K under equal boundary conditions, the results show that this increase could be maintained with a temperature delta of 0.4 K at the end of the cycle. By targeting a maximum temperature delta with identical boundary conditions, a temperature delta of 4 K was achieved. Graphical abstract: Highlights: Generic stochastic particle filter algorithm (GSPFA) for predictive energy management. Solving the mixed-integer nonlinear problem by applying the GSPFA. Velocity optimization by the GSPFA in an optimal control model predictive manner. GSPFA for a battery temperature control in a Hardware-in-the-Loop (HiL) system. Execution and validation on a battery HiL test bench within a real-time application.