# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 1 Introduction  
With the rapid development of industry, the world today is facing many challenges, such as global warming, energy crisis and environmental degradation. As a new form of transportation, electric vehicles (EVs) have quickly become the focus of research and development in various automobile companies and research institutions due to their superior energy efficiency, braking energy recovery and low emissions [1, 2, 3]. The battery management system (BMS) is an integral component of EVs. It is used to monitor and manage battery packs to ensure the safety of EVs. The BMS can reasonably control the charge and discharge of the battery packs to increase the driving range, extend the battery life and reduce the vehicle operating cost [4, 5, 6, 7]. Effective battery state of charge analysis, energy control management, battery fault diagnosis and safety are aspects of BMS technology that require improvement and continue to restrict the development of EVs. The accurate online estimation of the SoC of lithium-ion batteries is related to the ability to accurately predict the remaining range of an EV. Therefore, accurate online estimation of SoC is the key aspect of BMS technology [8, 9]. At present, SoC estimation methods mainly include OCV-based estimation, ampere-hour integration estimation, internal resistance-based estimation, electrochemical model-based estimation, machine learning-based estimation, equivalent circuit-based estimation and modern control theory-based estimation. OCV is the stable electrode potential difference in the open-circuit state. In theory, the SoC-OCV relationship curves of the same batch of lithium-ion batteries are identical. However, as the battery cycle life [10; 11; 12] or the ambient temperature [13; 14] changes, the SoC-OCV relationship curve changes accordingly. If the SoC-OCV curve cannot be obtained in a standardized and accurate manner, it is still a problem to use this method for SoC estimation. An adaptive method is established for the online estimation of OCV [15]. It needs to stand for a long time when measuring the battery OCV when the current excitation is a dynamic working condition; thus, the method based on OCV cannot accurately estimate the SoC. The ampere-hour integration method offers simple and reliable estimation, given that the initial SoC value and the current measurement value are accurate. [PERSON] et al. [16] used the ampere-hour integration method for strings cells online SoC estimation. [PERSON] et al. improved SoC estimation accuracy by regularly calibrating battery capacity and SoC initial value. The SoC value estimated by the ampere-hour integration method without calibration was not found to be accurate [17]. The internal resistance of the battery represents the voltage response characteristics of the battery under current excitation. The internal resistance is equal to the ratio of the battery voltage change to the current change in a short period of time. Internal resistance is significantly affected by battery cycle life [18] and external temperature [19]. The mapping relationship between the internal resistance and SoC is not as reliable as the SoC-OCV curve, and online electrical impedance spectroscopy (EIS) measurements can be challenging [20]. Thus, SoC estimation based on the battery internal resistance is also challenging. [PERSON] et al. [21; 22] established a pseudo-two-dimensional (P2D), electrochemical model based on the porous electrode and concentrated solution theories. The model includes a series of algebraic equations and partial differential equations, which can more accurately describe the lithium-ion diffusion and migration inside a battery. Additionally, the electrochemical reactions on the particle surface offer a detailed description of the microscopic reactions inside the battery. In the model established by [PERSON] et al. [23], it is inconvenient to obtain material characteristic parameters such as the conductivity and diffusion coefficient.