Battery-Integrated AUV SOC Analysis Using Model-Based Nonlinear PID Controller With Disturbance Observer

Effective battery management is critical to ensure the operational longevity and reliability of Autonomous Underwater Vehicles (AUVs), especially in long-duration or deep-sea missions. This paper presents a strategy for monitoring State-of-Charge (SOC) within a model-based nonlinear proportional integral derivative with a disturbance observer (M-BNLPID-DO). The proposed method addresses the SOC analysis in dynamic underwater conditions by embedding a disturbance observer in the control loop to mitigate the effects of disturbances and model uncertainties, which often complicate underwater vehicle control. This approach offers a reliable, energy-aware control solution and advances SOC management in underwater systems. By incorporating system nonlinearities and external disturbances into the control framework, the method improves energy efficiency and operational reliability. The approach offers a reliable, energy-aware control solution, advancing SOC management and contributing to the field of battery-integrated AUV control. Simulation results highlight: in undisturbed conditions, the M-BNLPID-DO controller achieved a battery depletion of 46.51%, compared to 49.55% for the proportional integral derivative (PID) controller, representing a difference of 3.04%. Under disturbed conditions, the M-BNLPID-DO maintained a depletion of 46.52%, while the PID controller reached 50.10%, resulting in a difference of 3.59%. By embedding system nonlinearities and external disturbances into the control framework, the method enhances energy efficiency and operational reliability. These findings validate the controller’s performance, demonstrating improved battery utilization and resilience against model uncertainties and environmental variations. This work establishes a foundation for more efficient and effective AUV missions through integrated SOC monitoring and adaptive control strategies. The result establishes a foundation for more efficient and effective AUV missions through integrated SOC monitoring and adaptive control strategies.