Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

Abstract

The aging of lithium-ion batteries (LiBs) is inevitable during their operation owing to their irreversible side reactions. It is practical to capture only the dominant physicochemical processes with a physics-based model for engineering applications, as the degradation mechanism of LiBs is complex and interconnected. Numerous factors dramatically affect the performance of LiBs; thus, it is necessary to use the real-time operational data to estimate the model parameters online. The combination of the degradation mechanism and real-time data can effectively reduce the complexity of the parameter identification, and offers practicality from the perspective of engineering applications. In this study, a transfer learning method based on a back propagation neural network (BPNN) is introduced as the data-driven parameter identification method for the physics-based fractional-order model (FOM) of the LiBs. Accelerated aging tests are designed for commercial LiBs to reduce the experimental time, and the reference performance tests under different aging states are implemented to capture the degradation modes with increasing cycle numbers. The initial key parameters of the fresh FOM are estimated based on the BPNN with two hidden layers. The model-based transfer learning is proposed to estimate the key parameters of the aging FOM. The parameter identification results at different aging states are validated under various working loads. The good fit with the experimental data indicated that the parameters can be identified accurately using the transfer learning with BPNN. The outstanding results show the effectiveness of the parameter identification method based on transfer learning.

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

“…Other parameter-based transfer learning approaches using more machine learning methods include [130], [131], and [132] for condition diagnosis and [133] and [134] for condition prognosis. Guo et al [135] trained and transferred a data-driven model for parameter identification of a physical model.…”

Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects

“…In Ref. [68] , fine-tuning-based TL was also adopted to identify the parameters of the physics-based fractional-order model (FOM). A back propagation (BP) NN was designed to identify the time constants of the FOM, where the measured battery impedance was treated as the input of the BPNN.…”

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

“…However, in the later stage of SOC estimation, the filtering error will be larger than the average level, and it easily deviates from the reference value. Therefore, considering the robust learning of the neural network algorithm for data processing, an appropriate BP neural network algorithm is selected for the error compensation of the UKF algorithm [39]. The problem of how to adjust the connection weights of the hidden layer has long existed in the study of neural network algorithms until the introduction of the BP neural network, which successfully solved the problem of weight adjustment for multi-layer feed-forward neural networks with nonlinear functions.…”