A Consistency Diagnosis Method of Series-Connected Lithium-Ion Batteries Based on Charging Curve Transformation

In new energy storage applications, lithium-ion batteries are usually used in parallel and series connections to meet the power and energy requirements. However, the inevitable capacity and state of charge (SOC) inconsistency within the series battery pack can decrease the available capacity and result in accelerated aging and safety issues. In this paper, a consistency diagnosis method based on charging curve transformation is utilized to diagnose capacity and SOC differences within the battery pack. Since traditional curve transformation method impose high data storage and computational requirement to battery management system, it is difficult to implement the algorithm in real-time. A charging throughput-voltage (QV) curve transformation model is proposed and the consistency diagnosis is realized by parameter identification of the model. Finally, the proposed method is verified on a battery pack with six cells connected in series. Experimental results indicate that the propose method can achieve good diagnosis accuracy on both capacity inconsistency and SOC inconsistency conditions.