Development of battery charging station for automated guided vehicle - Tan, Tar Wei (2023)
The use of Automated Guided Vehicles (AGVs) has been instrumental in transitioning industries from traditional labor-based to automation. Lithium-ion batteries are the primary energy storage devices used in AGVs, owing to their higher energy density and longer cycle life compared to other types of cells (Fuqiang et al., 2019). However, the charging and discharging of these batteries require proper precautions to avoid affecting their performance and lifespan. This project aimed to develop a battery charging station for AGVs that are able to charge lithium-ion batteries optimally. Firstly, the main charger (DPS5015) was modified to enable autonomous control via an Arduino microcontroller. Next, the charging station incorporated the concept of the Internet of Things (IoT) by visualizing and storing the charging data on a web server named Cayenne. Additionally, the charger was equipped with a state-of-charge (SoC) estimator that utilized coulomb counting to estimate the battery's SoC. Upon completion, the battery charging station’s prototype proves its flexibility by performing constant current - constant voltage (CC-CV) charging at 0.2C (0.6A) termination criteria and multistage constant current (MSCC) charging profile at 5.4A - 4.1A - 2.8A - 1.6A - 0.6A. Moreover, the IoT data management function by Cayenne stored thousands of charging data with no issues. On the other hand, the SoC estimator had an error of 7.16% when tested under the external discharging circuit. The accuracy of the SoC estimator was acceptable as most industrial SoC estimators were only able to indicate the SoC at 20% or 25% intervals. Finally, a comparison was made between CC-CV and MSCC charging profiles using hardware experimentation data obtained during the charging process, showing that both profiles have similar charging times, but MSCC charging is better for the battery's health.