Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
SOC fusion estimation for lithium battery using dual OCV-SOC relationships combined with fractional order extended Kalman filter
by
Wei, Ying
in
Accuracy
/ Algorithms
/ Charging
/ Discharge
/ Driving conditions
/ Electric charge
/ Electric vehicles
/ Estimation
/ Extended Kalman filter
/ Kalman filters
/ Lithium
/ Lithium batteries
/ Methods
/ Neural networks
/ Open circuit voltage
/ Parameter identification
/ State of charge
/ Voltage
2025
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
SOC fusion estimation for lithium battery using dual OCV-SOC relationships combined with fractional order extended Kalman filter
by
Wei, Ying
in
Accuracy
/ Algorithms
/ Charging
/ Discharge
/ Driving conditions
/ Electric charge
/ Electric vehicles
/ Estimation
/ Extended Kalman filter
/ Kalman filters
/ Lithium
/ Lithium batteries
/ Methods
/ Neural networks
/ Open circuit voltage
/ Parameter identification
/ State of charge
/ Voltage
2025
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
SOC fusion estimation for lithium battery using dual OCV-SOC relationships combined with fractional order extended Kalman filter
by
Wei, Ying
in
Accuracy
/ Algorithms
/ Charging
/ Discharge
/ Driving conditions
/ Electric charge
/ Electric vehicles
/ Estimation
/ Extended Kalman filter
/ Kalman filters
/ Lithium
/ Lithium batteries
/ Methods
/ Neural networks
/ Open circuit voltage
/ Parameter identification
/ State of charge
/ Voltage
2025
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
SOC fusion estimation for lithium battery using dual OCV-SOC relationships combined with fractional order extended Kalman filter
Journal Article
SOC fusion estimation for lithium battery using dual OCV-SOC relationships combined with fractional order extended Kalman filter
2025
Request Book From Autostore
and Choose the Collection Method
Overview
Using a single or averaged OCV-SOC (open circuit voltage VS. state of charge) relationship obtained under charging or discharging condition will deteriorate battery SOC estimation of model-based methods. To address the issues, this paper proposes a novel SOC fusion estimation method considering charging and discharging OCV-SOC relationships. This method fully considers the actual working state of batteries in electric vehicles, which is beneficial for improving SOC estimation results. The SOC is respectively estimated using OCV-SOC relationship under discharging and charging conditions combined with fractional-order extended Kalman filter (FOEKF) algorithm. Both SOC estimation results using charging and discharging OCV-SOC relationships are obtained and then fuse them by the weighted summation for getting the final SOC. The fusion weights are established using the predicted voltage error. The US06 Highway Driving Schedule and Beijing Dynamic Stress Test (BJDST) are used for verifying the proposed method. The SOC root-mean square errors (RMSEs) compared to the regular technique that uses a single OCV-SOC relationship obtained under discharging condition decrease from 1.65% and 1.70–0.71% and 0.70% under the above two tests. The applicability of a certain OCV-SOC relationship for estimating SOC under all driving conditions is limited. Using multiple OCV-SOC relationships obtained under different conditions for SOC fusion estimation can effectively improve estimation accuracy. We have demonstrated the correctness and effectiveness of this paper’s ideas using BJDST test and US06 test.
Publisher
Springer Nature B.V
Subject
This website uses cookies to ensure you get the best experience on our website.