## 5 Conclusions  
Considering the requirement in terms of robustness and accuracy for SOC estimation, model-based estimation has become popular for EV applications recently. By analyzing the structure of the model-based SOC estimation, it is found that the performance of SOC estimation is closely related to the accuracy of the battery model. This paper provides an extended review of the battery modeling methods. According to their features and theoretical foundations, the latest battery models are  
Figure 10: The performance of the SVM battery model in UDDS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Type** & **MAE (V)** & **Execution Time (s)** \\ \hline Combined model & 0.0212 & 6.3649 \(\times\) 10\({}^{-7}\) \\ Two RC ECM & 0.0184 & 9.6155 \(\times\) 10\({}^{-7}\) \\ SPM & 0.0159 & 2.2105 \(\times\) 10\({}^{-5}\) \\ SVM & 0.0034 & 0.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the four models in MAE and execution time.  
classified into four categories: empirical models, ECMs, electrochemical models, and data-driven models. On the basis of the electrochemical reactions inside the battery, the electrochemical models are the foundation of the other models. Empirical models are developed by using mathematical expressions to simplify the electrochemical model. The circuit components in ECM can be regarded as an alternative to the reactions in the electrochemical model. Moreover, the data-driven models can be seen as describing the performance of the electrochemical model through the data analysis of the training samples.  
The features and connections of the modeling methods and their future trends have been discussed. Empirical models and ECMs are easier to be understood and have a higher computational efficiency. The accuracy of the data-driven model closely relies on the training dataset. Electrochemical models give deep insight into the mechanism of the battery. In order to develop high-fidelity battery models, hybrid modeling methods and the relationship between ECM and the number of RC networks still require further research. In addition, more attention should be paid to how to update the parameters in the battery model. Temperature plays a key role in battery characteristics, but most of the proposed model-based SOC estimators have still not taken temperature into consideration.  
Four typical models are compared in terms of accuracy and execution time with the measurement data from a LiFePO\({}_{4}\) battery. After optimizing the parameters in each model by GA, the four models obtain acceptable results. The most accurate SVM model obtains 0.0034 V in MAE and the execution time of the combined model is only 6.3649 \(\times\) 10\({}^{-7}\) s (MATLAB 2017b, 64 bit, 2.30 GHz CPU). SVM and SPM obtain better results in terms of accuracy, but the execution time of SVM and SPM is also longer than for the other two models. However, considering the flaws of SVM and SPM, the combined model and ECM are recommended for a LiFePO\({}_{4}\) battery if their accuracy is acceptable for a specific application. It should be noted that the conclusions obtained from the experimental results in this paper are only limited to the LiFePO\({}_{4}\) battery, which has a flat OCV-SOC curve.  
[PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] helped with improving this paper.  
This work was supported in part by the Key Program for International S&T Cooperation and Exchange Projects of Shaanxi Province (2017 KW-ZD-05) and the Fundamental Research Funds for the Central Universities (3102017 JC06004) and (3102017 CQD029). [PERSON] would like to thank the China Scholarship Council for supporting his study in the Department of Energy Technology, Aalborg University.  
The authors declare no conflict of interest.