proposed an adaptive noise correction-dual extended Kalman filtering algorithm and a new fusion ECM considering the influence of temperature, which achieves highly accurate co-estimation of SOE and SOC [8]. The EKF algorithm effectively addresses nonlinear estimation problems, but it is susceptible to divergence under high noise conditions and requires the computation of Jacobian matrices. Thus, the unscented Kalman filter (UKF) has been proposed. [PERSON] et al. proposed a joint state-of-health and SOE estimation approach by integrating the forgotten factor recursive least squares algorithm with the UKF, which demonstrates superior SOE estimation accuracy under adverse conditions, particularly during battery aging and at extremely low temperatures [9]. The cubature Kalman filter, an enhanced version of the UKF, utilizes a spherical-radial rule strategy for sigma point selection to improve accuracy in high-dimensional estimation. However, the model-based approach is computationally demanding and heavily reliant on the accuracy of the battery model. Moreover, simplified modeling assumptions can result in substantial deviations from actual battery behavior under complex operating conditions, consequently reducing the accuracy of SOE estimation [10; 11]. In contrast, data-driven approaches circumvent the need for explicit modeling of complex electrochemical mechanisms within batteries through end-to-end learning frameworks [12]. Additionally, they directly establish a nonlinear mapping of dynamic properties between multiphysics feature inputs and the SOE, which achieves enhanced accuracy estimation along with higher generalization performance. [PERSON] et al. introduced sliding windows in long short-term memory (LSTM) networks for the estimation of SOC-SOE [13]. [PERSON] et al. proposed a fusion model of a convolutional neural network (CNN) and bi-layer gated recurrent unit, which utilizes the BGRU to learn the depth features extracted by the CNN to achieve high-precision estimation of SOE across diverse operating conditions, including different operating conditions, temperatures, and battery materials [14]. Although these models show high accuracy in estimating SOE, their performance remains largely contingent upon data quality. During battery tests, data acquisition is susceptible to noise interference that could lead to significant fluctuations in SOE estimates. Some scholars have used a KF algorithm added after the deep learning model to smooth the outputs of the priors [15; 16]. [PERSON] et al. [17] proposed a data-driven model that incorporates transfer learning, multi-scale squeeze-and-excitation networks (MS-SENet), gated recurrent units, and UKF. This methodology employs MS-SENet to extract and enhance key features, which are subsequently fed into the GRU for prediction to obtain a preliminary estimate. The results of SOC and SOE estimation are then optimized through UKF to effectively mitigate noise effects [17]. Numerous studies have demonstrated the effectiveness of data-driven approaches for accurately estimating the SOE in various applications. However, most of the existing literature relies on conventional electrical and thermal signals such as voltage, current, and temperature as inputs to the model. With advancements in sensing technology, battery mechanical force has emerged as a noteworthy parameter for characterizing battery state, attracting increasing attention from researchers. The mechanical properties of LFP are tightly related to the state of the battery. Based on traditional features such as voltage, current, and temperature, [PERSON] et al. [18] innovatively introduced mechanical force as a new input feature. Additionally, a private multi-condition dataset was established to collect multidimensional feature information. High-precision battery SOC estimation was achieved using the LSTM network [18]. Under normal charge and discharge cycling conditions, the changes in battery mechanical force can be observed at both microscopic and macroscopic levels.