Chen Peiyu, Lu Chengyi, Mao Zhaoyong, Li Bo, Wang Chiyu, Tian Wenlong, Li Mengjie, Xu Yunwei
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
ACS Omega. 2022 Aug 19;7(34):30504-30518. doi: 10.1021/acsomega.2c03985. eCollection 2022 Aug 30.
For underwater vehicles, the state of charge (SOC) of battery is often used to guide the optimal allocation of energy. An accurate SOC estimation can improve work efficiency and reliability of underwater vehicles. Model-based SOC estimation methods are still mainstream routes used in practical applications. Hence, accurate battery models are highly desirable, which depends not only on the circuit structure but also on the circuit parameters. Four-parameter identification algorithms, offline mechanism-based and least squared (LS) methods, as well as online recursive least-squares with forget factor (FFRLS) and extended Kalman filter (EKF) methods were analyzed in terms of SOC estimation under three different conditions. The results revealed that in the case without any disturbance, the predicted SOCs based on four-parameter identification circuits fitted well with the reference. Moreover, it is remarkable that the LS offline methods work better than the FFRLS online routes. In addition, the robustness has also been accessed through the other two conditions, i.e., measurement data with disturbance and initial SOC value with deviation. The results showed that maximum errors of SOC estimation based on the EKF approach are significantly lower than those of the other methods, and the values are 0.51% and 0.20%, respectively. Thus, the circuit model based on the EKF parameter identification approach possessed a stronger anti-interference performance during the SOC estimation process. This research can provide corresponding theoretical support on ECM parameter identification for lithium-ion batteries in underwater vehicles.
对于水下航行器而言，电池的荷电状态（SOC）常被用于指导能量的优化分配。准确的SOC估计能够提高水下航行器的工作效率和可靠性。基于模型的SOC估计方法仍是实际应用中的主流途径。因此，精确的电池模型非常必要，这不仅取决于电路结构，还取决于电路参数。针对三种不同工况下的SOC估计，分析了四参数辨识算法、基于离线机理和最小二乘法（LS）的方法，以及带遗忘因子的在线递推最小二乘法（FFRLS）和扩展卡尔曼滤波器（EKF）方法。结果表明，在无任何干扰的情况下，基于四参数辨识电路预测的SOC与参考值拟合良好。此外，值得注意的是，LS离线方法比FFRLS在线方法效果更好。另外，还通过另外两种工况，即带有干扰的测量数据和有偏差的初始SOC值，评估了鲁棒性。结果表明，基于EKF方法的SOC估计最大误差显著低于其他方法，分别为0.51%和0.20%。因此，基于EKF参数辨识方法的电路模型在SOC估计过程中具有更强的抗干扰性能。本研究可为水下航行器锂离子电池的电化学模型（ECM）参数辨识提供相应的理论支持。
