A data-driven evaluation method for low-temperature performance of lithium-ion batteries

Abstract

Energy storage system plays an important role in smoothing out the electricity supply from renewable energy and improving stability of the power system. At present, most energy storage systems are still battery energy storage systems (BESS). However, the time-varying temperature condition has a significant impact on discharge capacity of lithium-ion batteries. When lithium-ion battery operates in a low temperature environment, the discharge capacity of the battery decreases. Therefore, this paper develops a discharge capacity evaluation method for lithium-ion batteries at low temperature. Firstly, we analyze the battery discharge characteristics. On this basis, battery tests have been conducted and we proposed some health indicators. Finally input the measured data and health indicators into the machine learning model. The applicability and effectiveness of this method are analyzed through numerical results.

Keywords

• Lithium-ion battery
• Low temperature
• Capacity
• Machine learning