Though lithium-ion batteries are extensively applied in electric vehicles as a power source due to their excellent advantages in recent years, the security risk has inarguably always existed. The state of health (SOH) of lithium-ion batteries is one of the most important indicators related to security, the prediction of SOH is paid close attention spontaneously. To improve the prediction accuracy of SOH, this paper constructs an SOH prediction model based on a multi-kernel relevance vector machine and error compensation (EC-MKRVM). The provided model comprises a pre-estimation model and an error compensation model, both of which use the multi-kernel relevance vector machine (MKRVM) algorithm. The pre-estimation model takes the feature factors extracted in the charging segment as the input variable and the SOH pre-estimation value as the output. The error compensation model takes the pre-estimation error sequence as the input variable and the SOH prediction error as the output. Finally, the SOH prediction error is used to compensate for the SOH pre-estimation value of the pre-estimation model, and the final SOH prediction value is obtained. To verify the effectiveness and advancement of the model, the CACLE dataset is used for comparative experimental analysis. The results show that the proposed prediction model in this paper has higher prediction accuracy.

Two-dimensional materials are widely used as a new generation of functional materials for photovoltaic, photocatalyst, and nano-power devices. Strain engineering is a popular method to tune the properties of two-dimensional materials so that performances can be improved or more applications can be obtained. In this work, a two-dimensional heterostructure is constructed from SiC and GaN monolayers. Using first-principle calculations, the SiC/GaN heterostructure is stacked by a van der Waals interaction, acting as a semiconductor with an indirect bandgap of 3.331 eV. Importantly, the SiC/GaN heterostructure possesses a type-II band structure. Thus, the photogenerated electron and hole can be separated in the heterostructure as a potential photocatalyst for water splitting. Then, the external biaxial strain can decrease the bandgap of the SiC/GaN heterostructure. From pressure to tension, the SiC/GaN heterostructure realizes a transformation from a type-II to a type-I semiconductor. The strained SiC/GaN heterostructure also shows suitable band alignment to promote the redox of water splitting at pH 0 and 7. Moreover, the enhanced light-absorption properties further explain the SiC/GaN heterostructure’s potential as a photocatalyst and for nanoelectronics.