ABSTRACT
The health status of the battery of new energy electric vehicles is related to the quality of vehicle use, so it is of high practical application value to predict the health status of the battery of electric vehicles. In order to predict the health status of lithium battery, this study proposes to optimize the empirical modal decomposition method and obtain the ensemble empirical modal decomposition algorithm, and use this algorithm to collect the vibration signal of the battery, then use wavelet transform to pre-process the collected signal, and finally combine K-mean clustering and particle swarm algorithm to cluster the signal types to complete the prediction of battery State of Health. The experimental results show that the ensemble empirical modal decomposition algorithm proposed in this study can effectively perform signal acquisition for different state types of batteries, and the K-mean clustering-particle swarm algorithm predicts a 63 % decrease in the health state of the battery at 600 cycles, with a prediction error of 2.6 %. Therefore, the algorithm proposed in this study is feasible in predicting the battery health state.
ABSTRACT
Low socioeconomic status appears to be an independent risk factor for stroke mortality in epidemiology studies, but there has been no systematic assessment of this association. We performed a systematic review and meta-analysis evaluating the association between low socioeconomic status and stroke mortality. A systematic review of MEDLINE, EMBASE, and Web of Science for cohort studies that reported low socioeconomic status and stroke mortality was conducted from inception until July 2017. Research information, adjusted risk ratio (RR) estimates and 95% confidence intervals (Cls) were extracted. Estimates were pooled using a random-effects model. Heterogeneity was examined using the Q statistic and I 2. Twenty-seven prospective cohort studies (471,354,852 subjects; 429,886 deaths) assessing stroke mortality with low socioeconomic status were identified. Compared with the highest socioeconomic status, overall RR of stroke mortality was 1.39 (95% CI, 1.31-1.48) for those with the lowest after adjustment for confounding factors, but there was substantial heterogeneity between studies (I 2 = 89.9%, P = 0.001). Significant relationships were observed between risk of stroke mortality and the lowest education (RR = 1.21, 95% CI 1.11-1.33; I 2 = 70.9%, P < 0.001), income (RR = 1.54, 95% CI 1.30-1.82; I 2 = 91.6%, P < 0.001), occupation (RR = 1.54, 95% CI 1.35-1.75; I 2 = 78.3%, P < 0.001), composite socioeconomic status (RR = 1.37, 95% CI 1.25-1.51; I 2 = 69.5%, P = 0.001). After subgroup analysis, it was found that the heterogeneity of each SES indicator mainly came from the follow-up time, study population, stroke type, study area. Patients with low socioeconomic status had a higher risk of stroke mortality. The heterogeneity of income and occupation is larger, and the education and composite SES is smaller.
