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State‐of‐Power Estimation of Li‐Ion Batteries Considering the Battery Surface Temperature
Author(s) -
Liu Xintian,
He Yao,
Zeng Guojian,
Zhang Jiangfeng,
Zheng Xinxin
Publication year - 2018
Publication title -
energy technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.91
H-Index - 44
eISSN - 2194-4296
pISSN - 2194-4288
DOI - 10.1002/ente.201700680
Subject(s) - battery (electricity) , state of charge , extended kalman filter , power (physics) , kalman filter , automotive engineering , range (aeronautics) , control theory (sociology) , state of health , computer science , engineering , control (management) , aerospace engineering , thermodynamics , physics , artificial intelligence
The state‐of‐power (SOP) of a Li‐ion battery (LIB) is an important evaluation index for safety control and energy recovery in electric vehicles. Major state estimation methods are only applicable to fixed room temperature at 20 °C, although the battery capacity and resistance vary dramatically with a change in battery surface temperature in the working process of the battery. This causes significant errors in state estimation if only room temperature is assumed. The inaccurate state may cause further excessive current at high or low temperatures to affect safety and life cycle of the battery. Therefore, a novel state estimation method applicable to various battery surface temperatures is developed here. This method uses capacity–temperature and resistance–temperature relationships from experimental data to more accurately predict battery capacity and resistance over the full temperature range. It then applies the extended Kalman filter (EKF) technique to estimate the state‐of‐charge, and a method of constrained multi‐parameter algorithm (CMPA) to estimate the SOP of LIBs. Simulation and experimental results show that the proposed method can obtain accurate SOP results at different temperatures.