• Energy Research

To safeguard the security and dependability of battery management systems (BMS), it is essential to provide reliable forecasts of battery capacity and remaining useful life (RUL). However, most of the current prediction methods use the measurement data directly to carry out prediction work, which ignores the objective measurement noise and capacity increase during the aging process of batteries. In this study, an integrated prediction method is introduced to highlight the prediction of lithium-ion battery capacity and RUL. This approach incorporates several techniques, including variational modal decomposition (VMD) with entropy detection, a double Gaussian model, and a gated recurrent unit neural network (GRU NN). Specifically, the PE−VMD algorithm is first utilized to perform a noise reduction process on the capacity data obtained from the measurements, and this results in a global degradation trend sequence and local fluctuation sequences. Afterward, the global degradation prediction model is established by employing the double Gaussian aging model proposed in this paper, and the local prediction models are built for each local fluctuation sequence by GRU NN. Lastly, the proposed hybrid prediction methodology is validated through battery capacity and RUL prediction studies on experimental data from three sources, and its accuracy is also compared with prediction algorithms from the recent related literature. Experimental results demonstrate that the proposed hybrid prediction method exhibits high precision in the predicting future capacity and RUL of lithium-ion batteries, along with strong robustness and predictive stability.

An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.

The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden.