Cell Balancing Paradigms: Advanced Types, Algorithms, and Optimization Frameworks

Abstract: The operation efficiency of the electric transportation, energy storage, and grids mainly depends on the fundamental characteristics of the employed batteries. Fundamental variables like voltage, current, temperature, and estimated parameters, like the State of Charge (SoC) of the battery pack, influence the functionality of the system. This motivates the implementation of a Battery Management System (BMS), critical for managing and maintaining the health, safety, and performance of a battery pack. This is ensured by measuring parameters like temperature, cell voltage, and pack current. It also involves monitoring insulation levels and fire hazards, while assessing the prevailing useful life of the batteries and estimating the SoC and State of Health (SoH). Additionally, the system manages and controls key activities like cell balancing and charge/discharge processes. Thus functioning of the battery can be optimised, by guaranteeing the vital parameters to be well within the prescribed range. This article discusses the several cell balancing schemes, and focuses on the intricacies of cell balancing algorithms and optimisation methods for cell balancing. We begin surveying recent cell balancing algorithms and then provide selection guidelines taking into account their advantages, disadvantages, and applications. Finally, we discuss various optimization algorithms and outline the essential parameters involved in the cell balancing process.