Battery state of health estimation method based on sparse auto-encoder and backward propagation fading diversity among battery cells

This paper studies LiFePO4 (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).

Automated summary

This paper investigates the diversity in LiFePO4 (LFP) battery capacity fading among different cells of the same type and specification under the same working conditions. A novel battery State of Health (SOH) estimation method is proposed to adapt to this fading diversity. The method involves a machine learning structure with a sparse auto-encoder (SAE) and a backward propagation neural network (BPNN) for SOH estimation, showing good accuracy in verification tests.