Open AccessDual time-scale co-estimation of state-of-charge and state-of-health for lithium-ion battery pack with passive balance control over whole lifespan based on particle filter
Author(s) -
Zhe Li,
Chenhui Ma,
Bo Deng,
Yang Ou,
Yi Wang,
Zaiqiang Meng,
Peng Wang,
Jie Fan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012067
Subject(s) - battery pack , battery (electricity) , state of health , state of charge , particle filter , dual (grammatical number) , computer science , lithium ion battery , automotive engineering , control theory (sociology) , engineering , filter (signal processing) , control (management) , power (physics) , electrical engineering , artificial intelligence , art , physics , literature , quantum mechanics
Battery management system (BMS), as the key component in electric vehicles (EVs), takes the responsibility of sate-monitoring and safety-protection for the battery pack. State of charge (SOC) and state of health (SOH) are highly correlated with the safe and efficient operation of EVs, thus BMS must realize accurate estimation of them. This paper presents a combined SOC and SOH estimation for lithium-ion battery pack with passive balance control over the battery’s cycle lifespan. Considering the slow variation of SOH and fast variation of SOC, a dual time scale combined SOC and SOH estimation method is proposed. The battery cell with minimum capacity is located offline and then SOC is estimated and updated online, which lightens the computational burden for BMS on EVs. Results show that the proposed method can realize accurate SOC and SOH estimation. The SOH estimation error can be beneath 3% and the SOC estimation accuracy can improve 7%.