This article demonstrates a novel, compact-sized hardware-in-the-loop (HIL) system, and its verification using machine learning (ML) and artificial intelligence (AI) features in battery controls. Conventionally, a battery management system (BMS) involves algorithm development for battery modeling, estimation, and control. These tasks are typically validated by running the battery tester open-loop, i.e., the tester equipment executes the predefined experimental protocols line by line. Additional equipment is required to make the testing closed-loop, but the integration is typically not...