Bench-marking Computational Performance of Dynamic Programming For Speed Profiling and Fuel Efficiency of Autonomous-capable HEV 2020-01-0968
Dynamic programming has been used for optimal control of hybrid powertrain and vehicle speed optimization particularly in design phase for over a couple of decades. With the advent of autonomous and connected vehicle technologies, automotive industry is getting closer to implementing predictive optimal control strategies in real time applications. The biggest challenge in implementation of optimal controls is the limitation on hardware which includes processor speed, IO speed, and random access memory. Due to the use of autonomous features, modern vehicles are equipped with better onboard computational resources. In this paper we present a comparison between multiple hardware options for dynamic programming. The optimal control problem considered, is the optimization of travel time and fuel economy by tuning the torque split ratio and vehicle speed while maintaining charge sustaining operation. The system has two states - battery state of charge and vehicle speed, and two inputs namely, total torque and torque split ratio. First, we develop a Matlab® based program to solve the optimal control problem. The Matlab® code is optimized for performance and memory on PC. Secondly, we use the code-generation tools to deploy the code in C based application. The code is prepared to be able to use with parallel processing. Finally, we compare three different hardware options for computational efficiency and memory usage. The hardware options considered are a single core on a PC (7th gen intel Xeon processor), 6 cores on PC, a GPU (NVDIA PX2) and an Android cell phone. The GPU chosen is specifically designed for automotive applications, and an android phone is chosen since it is the most realistic situation. The results of this paper suggest that the DP can be used in real time if used in real applications if the problem can be simplified to 2 state 2 input case.
Wilson Perez, Amit Ruhela, Punit Tulpule