An Intelligent Optimization Scheme for LoRaWAN-Based Electric Vehicle Batteries Monitoring System Located in Warehouses

This article presents an optimization scheme for LoRaWAN-based electric vehicle batteries monitoring system located in warehouses by utilizing techniques to optimize packet delivery and power settings. Utilizing simulations, we identify that system optimization largely depends on network traffic, influenced by active users and the adoption of the pure ALOHA protocol. We define a reward metric based on the packet delivery rate and power efficiency, aiming for settings that yield the maximum reward. Our approach includes duty cycle management to minimize network traffic and maximize throughput, especially critical when handling urgent data from batteries. Traffic management based on the number of critical batteries in the warehouse also plays a crucial role. Predictive modeling of future traffic further refines power settings for optimal performance. The proposed system, tested through simulations, shows an average of 31% higher reward compared to traditional methods without duty cycle management.