A Model-Based Approach for Voltage and State-of- Charge Estimation of Lithium-ion Batteries

TY - GEN

T1 - A Model-Based Approach for Voltage and State-of- Charge Estimation of Lithium-ion Batteries

AU - Andalibi, Milad

AU - Madani, Saeed

AU - Ziebert, Carlos

AU - Naseri, Farshid

AU - Hajihosseini, Mojtaba

PY - 2022

Y1 - 2022

N2 - Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

AB - Electric vehicles are equipped with a large number of lithium-ion battery cells. To achieve superior performance and guarantee safety and longevity, there is a fundamental requirement for a Battery Management System (BMS). In the BMS, accurate prediction of the State-of-Charge (SOC) is a crucial task. The SOC information is needed for monitoring, controlling, and protecting the battery, e.g. to avoid hazardous over-charging or over-discharging. Nonetheless, the SOC is an internal cell variable and cannot be straightforwardly obtained. This paper presents a Kalman Filter (KF) approach based on an optimized second-order Rc equivalent circuit model to carefully account for model parameter changes. An effective machine learning technique based on Proximal Policy optimization (PPO) is applied to train the algorithm. The results confirm the high robustness of the proposed method to varying operating conditions.

KW - Battery

KW - Battery Management System

KW - Electric vehicle

KW - State Estimation

KW - State of Charge

UR - http://www.scopus.com/inward/record.url?scp=85148621366&partnerID=8YFLogxK

U2 - 10.1109/iSPEC54162.2022.10032998

DO - 10.1109/iSPEC54162.2022.10032998

M3 - Article in proceedings

SN - 978-1-6654-8523-4

BT - 2022 IEEE Sustainable Power and Energy Conference (iSPEC)

A2 - Pashajavid, Ehsan

A2 - Kim, Dowon

A2 - Rajakaruna, Sumedha

A2 - Abu-Siada, Ahmed

PB - IEEE

T2 - IEEE Sustainable Power and Energy Conference (iSPEC)

Y2 - 4 December 2019 through 7 December 2022

ER -