# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 5 Conclusions  
Aiming at the problems present in the existing methods for estimating the SoC of lithium batteries, this paper constructs the LM-IEKF algorithm, combining the LM method and the iterated extended Kalman filter algorithm. Through multiple iterations, and with the help of the LM algorithm, the covariance matrix of the measurement update stage is optimized compared with the traditional EKF algorithm and IKEF algorithm, and the SoC estimation accuracy is further improved. Using React and the polarization characteristics of the battery, the concentration polarization characteristics' second-order Thevenin equivalent battery model was developed, as well as the identification of the equivalent model parameters according to the HPPC experimental results. According to the results of the HPPC experiments, the OCV-SoC relationship curve is constructed. In the verification section, the MATLAB simulation software is used to apply the EKF algorithm under the constant discharge condition, FUDS condition, and BJDST condition. The SoC estimation results of the IEKF algorithm and LM-IEKF algorithm are verified and analyzed. The results show that the error between the SoC result estimated by LM-IEKF algorithm and the true value is less than 1.5% under the DC discharge conditions. The maximum error estimated under the FUDS and BJDST  
Figure 11: SoC prediction results for the BJDST operating conditions. Figure 12: SoC estimation error for the BJDST condition. conditions is less than 2%. In the IKEF algorithm, due to the iterated adjustment of the calculation, the maximum estimation error is about 2.3%, which is within the acceptable range. However, due to the problem that its convergence speed is still too slow, the average estimation error is large, and the estimation effect is not as good as the LM-IEKF algorithm proposed in this paper. In view of the error of the initial value of the system, the LM-IEKF algorithm can correct the estimated value to near the true value with a fast convergence speed, and it can also adapt well to the situation of large current fluctuations. In conclusion, the algorithm not only gives full play to the advantages of the high accuracy and low complexity of the Kalman filter algorithm, but it also avoids the shortcomings of the slow convergence speed of the conventional Kalman filter algorithm and the poor ability to adapt to the nonlinear system with the help of the characteristics of iterated and LM algorithms. It can accurately estimate the SoC of a lithium battery, which is of great help to improve the estimation accuracy of the SoC. In the era of the rapid development of electric vehicles, the algorithm proposed in this paper further promotes the development of battery-powered electric vehicles and makes battery technology develop in a more mature direction. Moreover, with the progress of artificial intelligence technology, all kinds of unmanned equipment and robots are facing the problem of rational use of battery energy. The method proposed in this paper can be adapted to the application fields of various lithium-ion batteries and contributes to the development of new energy technology. However, when the algorithm is actually applied to the estimation of lithium-ion battery SoC in electric vehicles, the process noise and observation noise will change according to different driving conditions or external conditions, which is reflected in the corresponding matrix value in the algorithm, which will change with a certain law. Therefore, it study the variation law of noise under different working conditions should be explored in the future and applied to the LM-IEKF algorithm to estimate SoC. [PERSON] and [PERSON] proposed the idea; [PERSON] designed the research; [PERSON] drew and output the figures; [PERSON], [PERSON], and [PERSON] wrote the paper; [PERSON] derived the LM-IEKF equation; [PERSON] and [PERSON] established the battery equivalent model and derived it; [PERSON] substituted the data into each operating condition for simulation.