SOC Estimation Algorithm of Lithium-Ion Batteries Based on Particle Swarm Optimization

Sun, Qiang; Guo, Xiaoyu; Lv, Haiying; Gao, Shuang; Xu, Jiacheng; Wei, Kexin

doi:10.1109/asemd49065.2020.9276049

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…PSO is used in a wide range of industry applications [118][119][120], and has been extensively used in battery parameter estimations [121,122]. Sun et al [123] adopted PSO to tune up a Thevenin ECM to identify the critical parameters useful to SoC estimation. The proposed algorithm can estimate battery SoC with negligible errors.…”

Section: Data-drivenmentioning

confidence: 99%

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Di Luca,

Di Blasio,

Gimelli

et al. 2023

Energies

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The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

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