Integration of Hardware and Software for Battery Hardware-in-the-Loop Toward Battery Artificial Intelligence

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 straightforward. To improve flexibility and accessibility for battery management, this work proposes a low-cost highly reliable closed-loop charger and discharger. We first focus on the electronic circuit design for battery testing systems to maximize the applied current accuracy and precision. After functional verification, we further investigate applications for closed-loop BMSs. In particular, we extend the proposed architecture into the learning-based control design, which is a feedback controller. We utilize reinforcement learning (RL) techniques to highlight the benefits of closed-loop controls. As an example, we compare this learning-based control strategy with a conventional battery charging control. The experimental results demonstrate that the proposed experimental design is able to handle the learning-based controller and achieve a more reliable and safer charging protocol driven by AI.