@article { , title = {A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence.}, abstract = {Effective energy prediction is of great importance for the operational status monitoring of high-power lithium-ion battery packs. It should be embedded in the battery system performance evaluation, energy management, and safety protection. A new Streamlined Particle-Unscented Kalman Filtering method is proposed to predict the available energy of lithium-ion batteries, in which an Adaptive-Dual Unscented Transform treatment is conducted to realize the precise mathematical expression of its working conditions. For the accurate mathematical description purpose, an improved Synthetic-Electrical Equivalent Circuit modeling method is introduced into the internal effect equivalent process considering the influence of time-varying temperature and current conditions. As can be known from the experimental results, the proposed prediction method has a maximum estimation error of 2.27\% and an average error of 0.80\%, for the complex varying-current Beijing Bus Dynamic Stress Test. Under the Urban Dynamometer Driving Schedule working conditions, the available energy prediction has high accuracy with a maximum error of 1.83\% and a voltage traction error of 3.28\%. It provides vehicle-mounted available energy prediction schemes for effective management and safety protection of high-power lithium-ion batteries.}, doi = {10.1002/er.6930}, eissn = {1099-114X}, issn = {0363-907X}, issue = {12}, journal = {International journal of energy research}, note = {INFO COMPLETE (Now published, checked and updated 30/9/2021 LM; Info via Scopus 17/7/2021 LM) PERMISSION GRANTED (version = AAM; embargo = 12 months; licence = pub's own; SHERPA = https://v2.sherpa.ac.uk/id/publication/7470 ) DOCUMENT READY (AAM rec'd 9/8/2021 LM) ADDITION INFO - Contact: Carlos Fernandez Set statement - (This is the peer reviewed version of the following article: ZHANG, L., WANG, S., ZOU, C., FAN, Y., JIN, S. and FERNANDEZ, C. 2021. A novel streamlined particle-unscented Kalman filtering method for the available energy prediction of lithium-ion batteries considering the time-varying temperature-current influence. International journal of energy research [online], 45(12): environmentally friendly energy solutions, pages 17858-17877, which has been published in final form at https://doi.org/10.1002/er.6930. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions [https://authorservices.wiley.com/author-resources/Journal-Authors/licensing/self-archiving.html\#3].)}, pages = {17858-17877}, publicationstatus = {Published}, publisher = {Wiley}, url = {https://rgu-repository.worktribe.com/output/1391094}, volume = {45}, keyword = {Available energy prediction, Lithium-ion battery, Streamlined particle-unscented Kalman filtering, Synthetic-electrical circuit modeling, Temperature-current influence}, year = {2021}, author = {Zhang, Liang and Wang, Shunli and Zou, Chuanyun and Fan, Yongcun and Jin, Siyu and Fernandez, Carlos} }