A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries

Abstract

A proper battery management system (BMS) plays a vital role in ensuring the safety and reliability of electric vehicles (EVs) and other electronic products. Accurate State-of-Health (SOH) estimation of Lithium-ion (Li-ion) batteries is a key factor in a BMS. It is difficult to determine SOH because of the complexity of the electrochemical reactions within the battery. To improve the accuracy of SOH estimation, a dynamic spatial-temporal attention-based gated recurrent unit (DSTA-GRU) model is proposed in this paper. First, we extract six features from the battery's charging and discharging processes that can reflect the aging degree of the battery to some extent. Second, this paper proposes a model to combine spatial attention and temporal attention that can not only consider the effects of states at different time step on the results, but also consider the effects of different features in the space domain. Third, the proposed model is trained and tested on NASA battery datasets and compared with other conventional models. Experiments carried on these data sets demonstrate that our model achieves higher accuracy than other conventional models. INDEX TERMS Lithium-ion battery, state of health, dynamic spatial attention, temporal attention, gated recurrent unit

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

“…The data driven based methods extract and model the ageing patterns or characteristics from the historical data without needing to know the underlying ageing mechanisms of the battery [4][5][34][35][36][37][38][39][40][41][42][43][44][45][46]. Because of the recent advances in deep learning algorithms and computation capability of computers or microcomputers, the data-driven based methods are widely utilized to tackle and solve complex and challenging tasks.…”

“…Ref. [46] presents a gated recurrent unit (GRU) based SOH estimation method by extracting the spatial and temporal characteristics of battery data during charging and discharging processes.…”

“…For data driven based methods, the quality of data utilized to train the models affects the estimation performance [4]. In order to estimate SOH of battery cells under different operation conditions, the data utilized to train the models needs to cover the corresponding battery usage scenarios that the models plan to make the predictions under [34][35][36][37][38][39][40][41][42][43][44][45][46].…”

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

“…Recently, researchers have proposed multiple attention mechanisms for time series tasks and achieved better results than LSTM and GRU [37][38][39]. Inspired by the dynamic spatial-temporal attention mechanism [39], we employ the temporal attention mechanism to predict the acceleration of EVs in this work, since our task is univariate time series forecasting, which does not require consideration of spatial attention. Therefore, the temporal attention mechanism assigns a weight to each hidden state by correlating the output at each time step with the output at the last time step.…”

State‐of‐Health Prediction for Lithium‐Ion Batteries Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise‐Gate Recurrent Unit Fusion Model

“…Compared with LSTM, GRU has a more straightforward structure and fewer parameters, reducing the problem of a large amount of model calculation. [27] However, several works [28][29][30][31] have shown the capacity regeneration phenomenon in the degradation of Liion batteries. Capacity regeneration is related to many factors during cyclings, such as physicochemical aspects of battery, temperature, and load conditions.…”