Beyond Limits: A Brief Exploration of Fault Detection and Balancing in Lithium-ion Battery Technology

Abstract

The process of achieving balance among sequentially connected cells is crucial to prevent excessive charging or discharging, and it also improves the overall energy capacity. This article discusses various algorithms created for equalizing cell charge within a battery management system (BMS). Proper cell balancing is indispensable for upkeeping lithium-ion battery (LiB) packs. Within the BMS, identifying faults is of utmost importance. This encompasses detecting, isolating, and estimating faults. To prevent batteries from operating in unsafe ranges, it is vital to ensure the accurate functioning of current, voltage, and temperature sensors. Accurate fault diagnosis is pivotal for the optimal operation of battery management systems. In the context of electric vehicle battery management systems, precise measurement of current, voltage, and temperature is greatly relied upon to estimate the State of Charge (SOC) and overall battery health. Swiftly identifying early failures can mitigate safety hazards and minimize damage. Nevertheless, effectively pinpointing these initial failures using genuine operational data from electric vehicles remains a intricate task. This paper presents an analysis of different algorithms for detecting balancing-related faults, covering both methods based on models and those not reliant on models. The strengths and weaknesses of the evaluated algorithms, along with upcoming challenges in the realm of balancing and fault detection for LiBs, are also discussed in this document.