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.

Dielectric-metal nanostructures have lately emerged as one of the most promising approaches to modulating light at the optical frequency. Their remarkable electric and magnetic resonances give them a one-of-a-kind ability to augment local field enhancements with negligible absorption losses. Here, we propose a hybrid metal-dielectric-metal (MDM) nanoantenna that contains a dimer of three-layers of shell nanoparticles. In addition, we only theoretically and numerically show the optical properties of the hybrid dimer nanoantenna. We found that the nanoantenna sustained unidirectional forward scattering with narrow beamwidth (30.9 deg) and strong scattering intensity (up to 5 times larger than the single MDM particle). Furthermore, when the hybrid asymmetric dimer was excited by the plane wave with different electric polarization directions, our findings revealed that the hybrid nanoantenna boosted the gap’s electric near-field while also supporting unidirectional forward scattering. Finally, we analyzed the hybrid dimer with substrates of different materials. It supported strong electric high-order moments along the z-axis and x-axis in gaps between MDM nanoparticles and between MDM nanoparticles and the Ge substrate, owing to the intense displacement currents inside of the dielectric layer. We found that the local electric field of this MDM hybrid dimer nanoantenna with Ge substrate was well improved and attained 3325 v/m.

The reliability of insulated gate bipolar transistors (IGBTs) is closely related to junction temperature ( ${T}_{j}$ ). However, accurately detecting the junction temperature of IGBTs online has always been a challenging problem. In order to solve this problem, an online junction temperature detection for IGBTs using collector-emitter voltage rise time ( ${t}_{\text {rv}}$ ) during IGBT turn-off process is proposed in this article. First, the feasibility of ${t}_{\text {rv}}$ as a temperature-sensitive electrical parameter (TSEP) is analyzed. Then, a ${t}_{\text {rv}}$ extraction circuit and junction temperature detection equipment are proposed and designed. Finally, a mathematical model of the relationship between ${t}_{\text {rv}}$ and junction temperature is established. The effectiveness of the ${t}_{\text {rv}}$ extraction circuit is verified by experiments. The online detection of IGBT junction temperature is realized. The average error of junction temperature detection equipment is $1.04~^{\circ }$ C, and maximum error is $3.535~^{\circ }$ C.

For the problem of low accuracy of lithium battery state of charge (SOC) estimation by a single algorithm, this paper proposes an algorithm combining extreme learning machine (ELM) and cubature Kalman filter (CKF). By dynamically tracking the Thevenin equivalent circuit model parameters by recursive least squares (RLS), the initial estimate of SOC is obtained by the CKF algorithm. The battery operating voltage and current are used as input, corresponding to the estimation error of the CKF algorithm as output, using the ELM algorithm to build an error prediction model based on data-driven. Then the output of the ELM error prediction model is used to compensate for SOC initial estimation value to achieve the purpose of reducing the estimation error. The simulation results show that the proposed ELM-CKF algorithm effectively improves the SOC estimation accuracy compared with the CKF algorithm.