State of charge and online model parameters co-estimation for liquid metal batteries

Abstract Liquid metal battery (LMB) is a novel battery technology that shows great application potential in the electric energy storage system. For the utilization of battery systems, an accurate estimate of the state of charge (SOC) for LMBs is of great significance. However, there are still many challenges need to be addressed due to the relatively low voltage and flat open-circuit-voltage versus SOC curve of LMBs. In this work, a novel state and parameter co-estimator is developed to concurrently estimate the state and model parameters of a Thevenin model for LMBs. The adaptive unscented Kalman filter is employed for state estimation including the battery SOC, and the forgetting factor recursive least squares is applied for online parameter estimation, which increase the model fidelity and further enhance the accuracy and robustness of the SOC estimation. A comparison with other algorithms is made based on the experimental data from laboratory-made LMBs. The evaluation results show that the proposed co-estimator exhibits the smallest root mean square error of 0.21% and is robust to external disturbances.

[1]  Mohamed Becherif,et al.  Extended kalman filter for accurate state of charge estimation of lithium-based batteries: a comparative analysis , 2017 .

[2]  Le Yi Wang,et al.  A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .

[3]  Furong Gao,et al.  State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer , 2016 .

[4]  Rui Xiong,et al.  A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries , 2018 .

[5]  Cheng Xu,et al.  State of charge and model parameters estimation of liquid metal batteries based on adaptive unscented Kalman filter , 2019, Energy Procedia.

[6]  Donald R. Sadoway,et al.  Lithium–antimony–lead liquid metal battery for grid-level energy storage , 2014, Nature.

[7]  Kangli Wang,et al.  Numerical study on the thermal management system of a liquid metal battery module , 2018, Journal of Power Sources.

[8]  F. Baronti,et al.  Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles , 2013, IEEE Industrial Electronics Magazine.

[9]  Jihong Wang,et al.  Overview of current development in electrical energy storage technologies and the application potential in power system operation , 2015 .

[10]  Bizhong Xia,et al.  Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles , 2014 .

[11]  Brian L. Spatocco,et al.  Liquid metal batteries: past, present, and future. , 2013, Chemical reviews.

[12]  Kangli Wang,et al.  High Performance Liquid Metal Battery with Environmentally Friendly Antimony-Tin Positive Electrode. , 2016, ACS applied materials & interfaces.

[13]  Zhongbao Wei,et al.  Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery , 2018, Journal of Power Sources.

[14]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[15]  Hui Jiang,et al.  Open-circuit voltage-based state of charge estimation of lithium-ion power battery by combining controlled auto-regressive and moving average modeling with feedforward-feedback compensation method , 2017 .

[16]  Shijie Cheng,et al.  Liquid Metal Electrodes for Energy Storage Batteries , 2016 .

[17]  Guangzhao Luo,et al.  Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine , 2016, IEEE Transactions on Power Electronics.

[18]  Shengbo Eben Li,et al.  Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles , 2015 .

[19]  Zhongbao Wei,et al.  Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer , 2018, IEEE Transactions on Industrial Electronics.

[20]  Guangzhong Dong,et al.  Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter , 2017 .

[21]  Kangli Wang,et al.  Tellurium-tin based electrodes enabling liquid metal batteries for high specific energy storage applications , 2018, Energy Storage Materials.

[22]  Zonghai Chen,et al.  Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator , 2017 .

[23]  Donald R. Sadoway,et al.  Self-healing Li–Bi liquid metal battery for grid-scale energy storage , 2015 .

[24]  King Jet Tseng,et al.  A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model , 2017 .

[25]  Xidong Tang,et al.  Li-ion battery parameter estimation for state of charge , 2011, Proceedings of the 2011 American Control Conference.

[26]  Qianqian Wang,et al.  An online method to simultaneously identify the parameters and estimate states for lithium ion batteries , 2018, Electrochimica Acta.