A Dual-Input Neural Network for Online State-of-Charge Estimation of the Lithium-Ion Battery throughout Its Lifetime

Abstract

Online state-of-charge (SOC) estimation for lithium-ion batteries is one of the most important tasks of the battery management system in ensuring its operation safety and reliability. Due to the advantages of learning the long-term dependencies in between the sequential data, recurrent neural networks (RNNs) have been developed and have shown their superiority over SOC estimation. However, only time-series measurements (e.g., voltage and current) are taken as inputs in these RNNs. Considering that the mapping relationship between the SOC and the time-series measurements evolves along with the battery degradation, there still remains a challenge for RNNs to estimate the SOC accurately throughout the battery’s lifetime. In this paper, a dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) is developed to overcome the above-mentioned challenge. Its most important characteristic is the adoption of the state of health (SOH) of the battery as the network input, in addition to time-series measurements. According to the experimental data from a batch of LiCoO2 batteries, it is validated that the proposed DIGF network is capable of providing more accurate SOC estimations throughout the battery’s lifetime compared to the existing RNN counterparts. Moreover, it also shows greater robustness against different initial SOCs, making it more applicable for online SOC estimations in practical situations. Based on these verification results, it is concluded that the proposed DIGF network is feasible for estimating the battery’s SOC accurately throughout the battery’s lifetime against varying initial SOCs.

State of health estimation of lithium-ion battery based on DAGRU

“…We compare GRU [10] and encoder-decoder GRU (CodecGRU) [11] with the proposed model, as shown in Table 1.lists the RMSE and R 2 of the three battery SOH estimation experiments. The RMSE of the three battery experiments is within 0.5%, and R 2 is above 0.99, which indicates that the battery SOH estimation method based on DAGRU has good fitting ability.…”

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

“…In addition to conventional approaches, novel artificial intelligence algorithms utilizing extensive datasets have emerged to estimate the battery's SOC. The data-driven approaches do not need to consider the internal characteristics of the battery and use the previously existing data and experience to predict the results in unknown cases [28,29]. Omer Ali and Ishak et al (2022) introduced an online estimation method for SOC using Gaussian process regression.…”

State of Charge Estimation and Evaluation of Lithium Battery Using Kalman Filter Algorithms

“…According to the trade-off between the structural complexity and the model prediction accuracy, a second-order model was selected, as shown in Figure 1. [41]. The present works in the literature have focused more on the accuracy the algorithms, with less emphasis on the usage environment, which has imp for successful estimation.…”

“…Wen [40] et al provided a multi-state control strategy that could effectively manage the SOC while improving the system frequency stability. A dual-input neural network combining gated recurring unit (GRU) layers and fully connected layers (acronymized as a DIGF network) was developed by taking both the time-series voltage and current measurements and the battery's SOH as inputs [41]. The present works in the literature have focused more on the accuracy and speed of the algorithms, with less emphasis on the usage environment, which has important effects for successful estimation.…”