## 1 Introduction
The experimental results showed that this method can reduce the SOC estimation error to within 3% [30]. [PERSON] et al. proposed an online fusion estimation method based on BP-GA to estimate the SOC and state of health (SOH) of Li-ion batteries. Compared to the estimation results of BP, genetic algorithm back propagation (GABP) has good accuracy and can reduce algorithm complexity and improve detection efficiency [31]. [PERSON] et al. proposed the use of GA to optimize the key parameters of the GRU model for battery SOC estimation. The authors used GA to optimize the number of layers and neurons in the GRU model structure. The results showed that this method is highly accurate and robust [32]. [PERSON] et al. combined GA with a fuzzy logic control neural network algorithm, utilizing the neural network to accurately estimate the dynamic SOC of the battery based on the static SOC. This research method improves the energy and time efficiencies of the balance system [33]. Although many studies have enhanced the accuracy of battery SOC estimation by improving the structure and parameters of neural networks, several issues remain unaddressed. 1. Previous studies have found that when the battery SOC is at a relatively low level (SOC < 20%), the error in battery SOC estimation increases significantly, affecting the effectiveness of the final model. 2. The different lengths of time-series data often affect the accuracy of battery SOC estimation. Determining the length of the time series and matching it with network parameters has a significant impact on battery SOC estimation. 3. Different neural network model structures and parameters yield different experimental results. The setting of these hyperparameters is typically based on data features and the experience of the researcher. The human involvement ultimately leads to experiential errors, affecting the accuracy of battery SOC estimation. 4. When using GA to optimize the population, individual optimization abnormalities are commonly observed within the population. An approach for solving this issue is to regenerate new individuals. However, this reduces the correlation between new and other individuals, thus slowing the convergence speed. To solve the aforementioned problems, this study proposes a method for optimizing the parameters of the TSCNN model using GA. First, the population, comprising the hyperparameters to be optimized of the TSCNN model, is initialized. Subsequently, the data are divided into time series, whereby the TSCNN model is built based on the population, using the mean square error (MSE) as the fitness function for GA optimization during TSCNN model training. The check function is used to convert abnormal data into normal data during GA optimization. When TSCNN is used to process time-series data for accurate battery SOC estimation, the input data of the model are richer and display more time-varying characteristics, reducing the huge error generated when the battery SOC is less than 20%. The estimation effect of the TSCNN model is further improved when using GA to optimize the structure and parameters of the TSCNN model. The length of the time series is related to the processing of the data, whereas the structure and parameters of the TSCNN model are used to construct the neural network model. Matching the two to achieve the best effect is the work of the GA, which uses the MSE obtained during TSCNN model training as the fitness function to perform genetic optimization and finally determine the optimal parameter combination. Following GA optimization, a check function is used to check and correct the optimized population, as the optimized data may cause abnormal phenomena (see Section 3.3.2). In particular, when the abnormal data contain zeros, the parameters of the neural network model produce errors because of an inability to use the data (such as the convolutional kernel length, neural data, and so on). In previous studies, a randomly generated usable parameter replaced abnormal data, which reduces the relevance between other parameters.