In order to ensure the driving safety of electric vehicles and avoid potential failures, it is important to properly estimate the state of health (SOH) of lithium-ion batteries. In this paper, a method of lithium-ion battery SOH estimation based on electrochemical impedance spectroscopy (EIS) and an algorithm fused by Elman neural network and cuckoo search (CS-Elman) is proposed. First, by extracting 19 features of EIS and using principal component analysis to reduce dimension, we obtain four principal components as model inputs. Second, CS algorithm optimizes the weights and thresholds of Elman algorithm. Next, we use the CS-Elman model to estimate the battery SOH and verify the model with the remaining battery data. In addition, we propose a variable temperature estimation model and verify the feasibility of the model between 25 °C and 45 °C. Finally, the experimental results show that the mean absolute error of the method is less than 1.36%.

Flood classification is an important basis for flood forecasting, flood risk identification, flood real-time scheduling, and flood resource utilization. However, flood classification results may be not reasonable due to uncertainty, the fuzziness of evaluation indices, and the demerit of not comprehensively considering the index weight. In this paper, based on the fuzzy clustering iterative model, a sensitivity coefficient was applied to combine the subjective and objective weights into a combined weight, then the fuzzy clustering iterative model with combined weight (FCI-CW) was proposed for flood classification. Moreover, an immune grey wolf optimizer algorithm (IGWO) based on the standard grey wolf optimizer algorithm and an immune clone selection operator was proposed for the global search of the optimal fuzzy clustering center and the sensitivity coefficient of FCI-CW. Finally, simulation results at Nanjing station and Yichang station demonstrate that the proposed methodology, i.e., FCI-CW combined with IGWO, is reasonable and reliable, can effectively deal with flood classification problems with better fitness and a comprehensive consideration of the subjective and objective aspects, and has great application potential in sorting, evaluation, and decision-making problems without evaluation criteria.

In order to reasonably and rapidly evaluate flood disaster, based on a fuzzy clustering iterative model (FCI) and differential evolution algorithm (DE), an adaptive fuzzy clustering iterative model using a hybrid differential evolution algorithm (AFCI-HDE) is proposed, which has three advantages: firstly, the decision-maker's subjective preference was considered to flexibly modify the objective function; secondly, HDE was introduced to optimize the index weight vector of AFCI; thirdly, the validity of its clustering effect was more credible than that of FCI. Finally, the case study revealed that AFCI-HDE is feasible and effective by comparing the optimal fitness and clustering validity values with other approaches, which could reflect various decision-maker's preferences by simple adaptive adjustments and rapidly obtain reasonable evaluation results, thus providing a new effective approach in flood risk management.

Impedance spectroscopy can characterize a variety of states within a battery and shows great potential for application in safety warning and condition assessment, but there is a lack of an effective online monitoring method. Therefore, this article proposes a method for online measurement of impedance spectroscopy based on harmonic injection of equalization currents, which superimposes different frequency sinusoidal perturbation signals on the original equalization currents through process reconstruction and collaborative control, and realizes real-time impedance spectroscopy monitoring at the level of single cell without additional hardware. In order to verify the validity of the method proposed in this article, a hardware experimental platform was constructed. First, a four-switch buck-boost (FSBB) converter is used to perform energy equalization of a six-cell Li-ion battery pack under different working conditions, and the experimental results show that the Li-ion battery pack can be equalized by the active equalization circuit under the states of resting, charging, and discharging in about 60 min, 75 min, and 100 min, respectively. Then, the impedance spectroscopy in the frequency range of 0.2 Hz–800 Hz is achieved through the collaborative control of active equalization and impedance spectroscopy. Finally, in order to verify the reliability of the measured data, the measured impedance spectroscopy data were evaluated by the Kramers–Kronig (K-K) transforms, and the data measured at the initial stage were compared and analyzed with the data measured by a professional electrochemical workstation, which verified the validity of the method proposed in this article.