Lithium-Ion Battery State-of-Charge and State-of-Health Co-Estimation with Feasible Real-Time Implementation

The key to improving the efficiency of lithium-ion battery systems and reducing safety incidents is that the battery management system can accurately estimate the state of lithiumion batteries, including the state of charge and the state of health. At present, the commonly used algorithms do not achieve an effective balance between accuracy and complexity, and it is difficult to apply them in practice. Therefore, on the basis of the existing algorithms, this paper proposes to reduce the order of the equivalent circuit model, simplify the observer polynomial, achieve low computational complexity, and use a processor with limited storage to complete the estimations. The simulation and experimental results confirm that the maximum absolute error of the state of charge and state of health of the lithium-ion battery systems fully meet the requirements of practical use. Compared with the reference method, there is no obvious difference in convergence speed and stability, but the run time is relatively faster. The proposed method has a significant simplification effect.