Design and Implementation of a Robust State of Charge Estimation Approach for a Single Battery Cell, a Hardware-in-the-Loop Test Bench, and a Battery Disconnect Unit for an Electric Vehicle Battery Pack

Abstract

As transportation electrification accelerates, battery-powered vehicles, including cars, airplanes, and boats, are rapidly emerging. This thesis provides solutions and practical insights on two key topics: implementing robust machine learning algorithms on commercial Battery Management System (BMS), and building a high-performance Battery Disconnect Unit (BDU). The experience was gained during participation in the North American Battery Workforce Challenge.

First, two machine learning approaches for State of Charge (SoC) estimation are introduced. The first approach is an adaptive algorithm using SoC-OCV-T (State of Charge-Open Circuit Voltage-Temperature) lookup table and Extreme Learning Machine (ELM). The experiment began at 100% SoC, with temperature ranging from -20°C to 60°C. From -20°C to 0°C, the maximum absolute error (MAE) ranged from 0.030 to 0.025. In the mid-range from 5°C to 40°C, the MAE decreased to within 0.015 to 0.020 range. Lastly, at higher temperature range of 45°C to 60°C, the MAE was below 0.013.

In the second approach, advanced differential features are added to improve the accuracy of the ELM model, particularly below 0°C. Under noisy condition, both the maximum absolute error (MAE) and the root mean square error (RMSE) were reduced to below 1.5% at -20, 20, and 60°C. Both algorithms were validated on a customized Hardware-in-the-loop (HIL) test bench. The HIL platform was developed to streamline validation of algorithms such as SoC estimation.

Finally, the thesis details the design and testing process for the BDU, highlighting key design considerations, test results, and engineering challenges.