## 1 Introduction  
Because of global warming, environmental pollution, energy shortages, and many other challenges, electric vehicles are becoming increasingly more popular as a green, efficient, and sustainable ideal transportation tool [1]. Lithium-ion batteries have become an ideal power source for electric vehicles because of their high energy density, long cycle life, and low self-discharge rate [2]. The batteries will age with repeated use, which will decrease the battery's charge and discharge capacity and the actual remaining capacity [3, 4]. State of health (SOH) is a quantitative indicator used for evaluating the degree of battery aging. Generally, SOH is defined as the ratio of the actual remaining capacity of the battery to the rated capacity of the battery [5]. The accuracy estimation of the battery state of charge (SOC) and the cruising range of the electric vehicle both depend on the accurate estimation of the battery SOH [6]. In addition, aging batteries are more prone to thermal runaway [7]. Therefore, it is very important to evaluate reliable methods and strategies to accurately estimate the current remaining capacity and SOH of the battery. At present, SOH estimation methods mainly include direct measurement-based estimation, model-based estimation, and data-based estimation. The direct measurement-basedmethod estimates the battery SOH by measuring the battery's characteristic parameters, such as the voltage, current, temperature, and internal resistance. [PERSON] et al. [8] proposed a method to measure the available battery capacity based on the mapping relationship between SOC and open-circuit voltage (OCV). However, as the battery ages or the ambient temperature changes, the SOC and OCV curves must be corrected again. [PERSON] et al. [9] proposed a method for the performance analysis of lithium-ion batteries and SOH estimation through electrochemical impedance spectroscopy (EIS). Although the EIS method can accurately characterize the battery status, this method can generally only be performed in a laboratory or under relatively stable conditions [10]. This also limits the use of this method in real vehicles or in online situations. Significant efforts have been made to study the model-based method to estimate the SOH and capacity of batteries. A method combining the Kalman filter and equivalent circuit model (ECM) was developed to estimate the SOH of lithium-ion batteries [11; 12]. [PERSON] and [PERSON] et al. proposed electrochemical models to describe the lithium-ion diffusion and migration inside a battery [13; 14]. This type of model needs to input a lot of physical and chemical parameters, leading to the need to solve a series of algebraic equations and partial differential equations. How to balance the complexity and computational efficiency of this method is worthy of further study. With the development of artificial intelligence and the Internet, the data-based method has received increasingly more attention. This method is based on battery laboratory measurements or actual operating data of electric vehicles to achieve SOH estimation through algorithms, such as neural networks [15]. [PERSON] et al. [16] applied machine-learning tools to both predict and classify cells by their cycle life with the discharge-voltage curves obtained from the early cycles to determine capacity degradation. The result showed a 4.9% test error when the first 5 cycles were used for classifying the cycle life. Support vector machine (SVM) is a widely used machine learning technique [17; 18; 19]. [PERSON] et al. reported a deep convolutional neural network for capacity estimation based on measurements during charging [20]. Based on the BP neural network, a three-layer BP neural network was trained to estimate the SOH at a low computational cost [21]. [PERSON] et al. [22] proposed a variant long-short-term memory (LSTM) neural network. An NASA dataset was trained for the prediction of SOH and remaining service life, and the RMSE of the result was low.