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Battery state of health estimation method based on sparse auto‐encoder and backward propagation fading diversity among battery cells
Author(s) -
Sun Yening,
Zhang Jinlong,
Zhang Kaifei,
Qi Hanhong,
Zhang Chunjiang
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6346
Subject(s) - fading , battery (electricity) , voltage , adaptability , artificial neural network , engineering , computer science , electronic engineering , state of health , artificial intelligence , power (physics) , telecommunications , electrical engineering , channel (broadcasting) , ecology , quantum mechanics , biology , physics
Summary This paper studies LiFePO 4 (LFP) battery capacity fading diversity among different cells with same type and specification under same working states during their whole life cycle; and with consideration of this phenomenon, a novel battery state of health ( SOH ) estimation method with adaptability to capacity fading diversity is proposed. In order to cope with this capacity fading diversity, a machine learning structure involving a sparse auto‐encoder (SAE) and a backward propagation neural network (BPNN) is designed for battery SOH estimation. In this strategy, battery terminal voltage during the later stage of charging process is used as input of SAE; through the reconstruction of input signal, compressive feature of battery voltage is abstracted by SAE; then this compressive feature is used as the input signal of BPNN, and through nonlinear mapping of the neural network, battery SOH can be finally obtained. In this way, a relationship between the battery voltage information at its later charging stage and its SOH can be established. Verification tests show that this SAE‐BPNN based SOH estimation strategy possesses a good accuracy with adaptability to the capacity fading diversity and voltage differences among different battery cells, the SOH estimation error can be restrained within the range of ±5%, and it is also very convenient to adopt this method in real online battery management system (BMS).