Hybrid FPGA/MCU Supervisory Controller for Multi-Source Inverter Integrated with Hybrid Energy Storage System in Electrified Vehicles

Abstract

This thesis discusses how to apply parts of the aerospace safety standard processes and guidelines such as the DO-254 and DO-160 to the firmware and hardware design of a supervisory control board for the E/E powertrain systems of an electric vehicle. A supervisory control board is developed as an ECU that is a computer-based electronic module intended to be used for automotive and aerospace applications. The functions of the developed ECU acquires/monitors system parameters, isolates and detects system faults, and communicates with the vehicle. The ECU includes two main sub-modules including a safety critical digital core based on NXP's MPC5777m MCU and a FDAC system based on Xilinx's Artix-7 FPGA. ECU micro-processing module and digitized analog I/O processed in an FPGA for aerospace application will enable this technology for the automotive application for fast and reliable supervisory controls capable of handling complex multi-physics control strategies.

A Neural Network Energy Management Controller (NN-EMC) is also designed and applied to a HESS using the Multi-Source Inverter (MSI). Its aim is to manage the current sharing between a Li-ion battery and an Ultracapacitor by actively controlling the operating modes of the MSI. A discharge duty cycle that biases the use of one source over another is used as the control variable. To limit the battery wear and the input source power loss, an optimized solution is obtained with Dynamic Programming (DP). The NN-EMC is designed with an artificial neural network and trained with the optimized duty cycle obtained by DP. The DP/NN-EMC solution was compared to the battery-only Energy Storage System (ESS) and the HESS-MSI with 50\% discharge duty cycle. Both the battery RMS current and peak battery current have been found to be reduced by 50\% using the NN-EMC compared to the battery-only ESS for the New York City drive cycle.