## 1 Introduction  
As a lightweight high-energy rechargeable battery, lithium-ion batteries have been widely used in mobile phones, laptops and electric vehicles [1]. The lithium-ion batteries can also store clean energy, such as solar energy and wind energy, offering us a possibility of realizing a fossil fuel-free society [2]. State of charge (SOC) reflects the cell operating state. Accurate SOC estimation would not only be convenient for the battery management system to carry out safety control but also can be used to estimate the remaining use time of the battery and help the user to formulate a reasonable battery-use plan [3]. Therefore, accurately estimating the SOC of lithium-ion batteries is of great safety significance and practical value. There are many SOC estimation methods, including: the coulomb integral method [4], the open circuit voltage (OCV) method [5], the model-based observer [6; 7; 8; 9; 10] method, and the data-driven method [11; 12; 13]. Given the closed-loop state estimation structure, the model-based observer SOC estimation method is robust. Furthermore, this method can be combined with the coulomb integral method, OCV method, and data-driven method to obtain more accurate estimation results. OCV and model parameters are essential reference variables for model-based observer SOC estimation methods. The OCV curve represents the functional mapping relationship between SOC and OCV. However, for some chemical systems like the LiFePO4-cathode and graphite-anode, the OCV curves between 10% and 90% SOC range are very flat. The low-precision OCV curves will lead to huge SOC estimation errors [14]. The OCV curve can be measured by the low-current OCV test or incremental OCV test. The low-current OCV test uses a continuous low-current discharge mode to measure the OCV curve. A continuous discharge process retains the nonlinear characteristics of the electrode material, but its test results will be affected by the polarization effect [15]. The incremental OCV test uses the pulse discharge mode to measure the OCV curve. The sufficient relaxation time between two discharge pulses can ensure that the test results are not affected by the polarization effect. However, the measurement points obtained by incremental testing are few, which cannot reflect the local non-linear characteristics of the OCV. The battery model parameters change continuously with the depth of discharge (DOD) and working conditions. The time-varying model parameters are deeply coupled with the SOC, increasing the non-linear strength of the system equation. The off-line parameter identification methods lack adaptability to the working conditions, which will deteriorate the accuracy of SOC estimation [16]. Recently, online parameter identification methods such as dual observer and recursive least squares are used to improve the accuracy of SOC estimation. For example, [PERSON], et al. [17] use two EKF (Extended Kalman filter) connected in parallel for SOC and model parameters co-estimation. [PERSON], et al. [18] used EKF for parameter identification and PF (Particle filter) for SOC estimation. [PERSON], et al. [19] and [PERSON], et al. [20] used RLS (Recursive least squares) for parameter identification and EKF for SOC estimation. The above methods achieve high SOC estimation accuracy under lab testing conditions. However, the dual-filter methods require two parallel filters for parameter identification and state estimation, respectively. The results are prone to cross-interference between identification parameters and estimated state, which reduces system stability. The hypothesis of the recursive least squares method is that only the output contains noise, not the state variable. Thus, it is not suitable for battery-management systems with various unexpected noises in real applications. To solve the above problems, we propose a data fusion method to construct a high-precision OCV curve.