The results demonstrate that the proposed health feature effectively reveals the battery performance degeneration. Additionally, this paper only utilizes the average IC of the extracted voltage subintervals to estimate battery SOH. Future work should prioritize the extraction and fusion of multiple health features.
The results show that the goodness of fittings for two variables of both battery A1 and A2 exceeds 0.8, which demonstrates the feasibility of using the proposed health feature to reveal the battery SOH. The superior performance of Battery A2 compared to that of Battery A1 may be attributed to its lower capacity decline. b. Fig. 9.
Most of the extracted health features exhibit a strong correlation with battery capacity, except for small values of dv and Δ v (such as 5 mV). The most probable reason is that the small dv and Δ v would lead to some noise disturbances. However, the health features obtained with different parameters still have differences.
Results on NASA battery based on collaborative features: (a) B6; (b) B7; (c) B18; (d) MAE and RMSE analyses. As shown in Figure 6 d, the results based on different batteries reach a maximum RMSE of 0.93% and a maximum of 0.80%, which provides good overall prediction results and demonstrates the model’s dependability and generality.
A health feature for battery SOH estimation is extracted using unsmoothing discrete incremental capacity curves. The consistency between the average IC of each voltage subinterval and battery capacity is quantitatively evaluated. The average IC of the corresponding subinterval that is most consistent with capacity degeneration is identified.
Lin et al. proposed a method for predicting the SOH of a battery by dividing the IC curve into voltage segments and using a back-propagation neural network for each segment to achieve high-precision online SOH estimation. In addition, Zhang et al. and Li et al. also employed IC analysis for SOH estimation.
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