Advancement in battery health monitoring methods for electric vehicles: Battery modelling, state estimation, and internet-of-things based methods

The battery health monitoring system (BHMS) ensures safety, performance, and reliability of lithium-ion battery (LIB) in electric vehicles (EVs). It also estimates and monitors the critical parameters of LIB, including state of charge (SoC), state of temperature (SoT), state of health (SoH), state of power (SoP), and depth of discharge (DoD) during charging and discharging processes. This article presents a comprehensive overview of diverse modelling and state estimation techniques for lithium-ion batteries (LIB), based on an analysis of 300 articles. The review begins with an examination of various battery cell modelling approaches, which include white box (electro-chemical), grey box (equivalent electrical circuit), and black box (data-driven) methodologies. Following this, various direct, model-based, and computerised techniques are outlined, including their respective advantages and disadvantages, for the estimation of SoC, SoH, and SoT. The resilience and efficiency of state estimation techniques are enhanced by integrating contemporary technologies such as blockchain, cloud