Optimal Sensor Placement and Battery Pack Configuration in Electrochemical Energy Storage Systems: A Mixed-Integer Non-Linear Programming Approach

The integration of electrochemical energy storage (EES) systems in diverse applications from portable devices to grid storage is crucial for the transition to sustainable energy sources. Efficient battery management is vital for optimizing performance and ensuring safety, with a focus on the strategic placement of sensors and battery pack configuration. This paper introduces a mixed-integer non-linear programming (MINLP) model that optimizes these aspects by considering three-dimensional spatial configurations and diverse battery types. The model aims to minimize costs while ensuring comprehensive coverage and stringent safety standards. It extends beyond traditional methods by incorporating adaptive strategies that enhance predictive capabilities and operational efficiency. By applying this model in detailed case studies, the paper demonstrates significant improvements in cost efficiency and system reliability, offering a robust framework for enhancing battery management systems in various technological and operational settings. This approach not only addresses current challenges but also adapts to dynamic conditions, highlighting its potential to significantly impact the future of energy storage solutions.