Novel SOH Estimation of Lithium-Ion Batteries for Real-Time Embedded Applications

Estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring that the batteries operate safely and have a long lifespan. The existing approaches for SOH estimation on embedded systems only consider one health indicator (HI) to represent either capacity or internal resistance (IR) behavior because of limitations in the hardware devices. Nevertheless, both capacity and IR provide valuable battery health information and neither of these could be neglected. Hence, we propose the SOH estimation method that can consider both capacity degradation and IR growth by representing it with HIs that can be directly measured in embedded systems with less complex computation. The results reveal that the proposed method improves the estimation accuracy by at least 47.59% and reduced the inference time by an average of 29.20%. All tests are performed on an actual embedded system using several datasets to demonstrate and verify both the accuracy and effectiveness of the proposed method.

Automated summary

A new method for estimating the state of health (SOH) of lithium-ion batteries is proposed, which can simultaneously consider capacity degradation and internal resistance growth, achieving better estimation accuracy and shorter inference time with simple computations.