## 1 Introduction
This method has low robustness against the acquired signal quality and the initial SOC. The neural network method and the deep learning method can be referred to as machine learning methods. These methods do not require detailed information on the battery system, also known as the \"black box\" model. [PERSON] et al. designed a SOC estimation method based on the recurrent neural network (RNN) [12]. Observable variables, such as voltage, current, and temperature, are directly mapped to SOC estimates. Experimental results show that the method is accurate and robust. [PERSON] et al. described a method for predicting state-of-health (SOH) and remaining useful life (RUL) of the battery based on deep learning [10]. Although there have been many studies on machine learning methods and many of them have shown very good performance, these methods still have shortcomings. The computational burden is heavy and poses a serious challenge to the performance of the BMS. Moreover, the cost of implementing these methods is very high and the practicality is still relatively poor. Although the practicality is not strong at present, more and more people have begun to explore ways to estimate SOC by better machine learning methods. The hybrid technique means an SOC estimation method that combines various SOC estimation methods appropriately. For example, [PERSON] et al. designed an SOC estimation method combining the adaptive extended Kalman filter method (AEKF) and the ampere-hour integration method together [9]. The experimental results show that the accuracy of this method is relatively high and the calculation time is short, and the practicability is better. The greatest problem with this method at present is that the cost of computing is still very high. Still with the article of [PERSON] as an example [9], the method proposed in this paper needs to construct the adaptive extended Kalman filter method and the ampere-timeintegral method at the same time, which undoubtedly brings a great burden to the BMS. However, finding the right hybrid method is still one of the development directions for the future SOC estimation. Compared with the above methods, the Kalman filter method is more accurate and easier to implement. The Kalman filter method does not require an accurate SOC initial value because the result will gradually approach the optimal value and the current measurement error will update the algorithm during operation. At the same time, it is a closed-loop observer that achieves the accurate and continuous estimation of performance over the entire battery operating range. The above advantages make the Kalman filter a promising solution to BMS application implementation. The traditional Kalman filter is only applicable to linear systems, while BMS applications require nonlinear algorithms for more complex systems [13]. Several modifications have been proposed, such as the Extended Kalman Filter (EKF) [14], the Unscented Kalman Filter (UKF) [15], and the Adaptive Kalman Filter (AKF) [16]. Among them, AKF has ability to update the process and measure the noise covariance, which can estimate the SOC more accurately [17]. The most challenging issue in the SOC estimation is the trade-off between balance accuracy and computational cost [9]. In general, the more complex the algorithm, the higher the accuracy, but the higher the cost. According to the authors' knowledge, BMS engineers of electric vehicle manufacturers generally believe that the SOC estimation module can be considered qualified if the error is less than 5%. The cost of the BMS is relatively high, accounting for more than 10% of the cost of the whole vehicle. A more complicated method may increase the accuracy of the SOC to less than 2%, but the cost usually doubles. In summary, modern intelligent algorithms for the BMS for the online SOC estimation require the following features: an accurate description of the battery system under dynamic excitation; an adaptive adjustment of system noise; system stability and robustness; and lower calculation cost.