Abstract
Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries’ state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries in electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries’ parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.
Highlights
Energy storage systems (ESS) play a significant role in a wide variety of technical and industrial applications [1,2], either as a mass store of energy or as a dispersed temporary power source
In [217], a hybrid electrochemical, heat generation, and thermal model for large prismatic cells are developed to predict in real time the core temperature and terminal voltage to improve the reliability of a battery management system (BMS) at various operating conditions
This paper presents a detailed review of various state of charge (SoC) estimation strategies intended for developing and installing an electrical vehicle BMS, which has been a challenging task due to complicated electrochemical reactions and degraded efficiency, depending on a variety of factors
Summary
Energy storage systems (ESS) play a significant role in a wide variety of technical and industrial applications [1,2], either as a mass store of energy or as a dispersed temporary power source. The modelling of the lithium-ion batteries with different equivalent circuits is presented This is followed by an in-depth analysis of the common key technologies of battery state estimation. It includes Lithium-Nickel-Cobalt-Aluminum (NCA), which has an excellent lifespan but involves security issues; and Lithium-Nickel-Manganese-Cobalt (NMC), which performs well in all aspects, in addition to having an excellent energy density. There are many different types of lithium ion batteries, each with its own set of advantages, such as: LCO, which has a high specific energy; LMO, which has a high specific power; NCA and NMC, which are the cheapest and most thermally stable lithium ion batteries; LFP, which has a flat OCV curve but a low capacity and a high self-discharging rate; and LTO, which has a long lifespan and is quick charging but has low specific energy and a greater cost [45].
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