{"id":4859,"date":"2026-01-28T08:30:29","date_gmt":"2026-01-28T08:30:29","guid":{"rendered":"https:\/\/www.krlpower.com\/?post_type=glossary&p=4859"},"modified":"2026-01-28T08:30:34","modified_gmt":"2026-01-28T08:30:34","slug":"data-analysis","status":"publish","type":"glossary","link":"https:\/\/www.krlpower.com\/fr\/glossary\/data-analysis\/","title":{"rendered":"Data analysis"},"content":{"rendered":"

Data analysis<\/a> refers to a systematic approach to analyzing the performance of batteries during manufacturing, testing, operation or retirement.
Common analysis methods:
Descriptive statistics (mean, variance, box plots, etc.)
Trend analysis (such as capacity attenuation curves, resistance change trends)
Anomaly detection (determining the existence of over-temperature, over-voltage, and abnormal welding of terminals, etc.)
Regression modeling and prediction (predicting SOH or RUL)
Clustering and classification (identifying different usage patterns or fault types)
Machine learning\/deep learning methods (for intelligent diagnosis and control optimization)<\/p>\n","protected":false},"excerpt":{"rendered":"

Data analysis<\/a> refers to a systematic approach to analyzing the performance of batteries during manufacturing, testing, operation or retirement.Common analysis methods:Descriptive statistics (mean, variance, box plots, etc.)Trend analysis (such as capacity attenuation curves, resistance change trends)Anomaly detection (determining the existence of over-temperature, over-voltage, and abnormal welding of terminals, etc.)Regression modeling and prediction (predicting SOH or […]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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resistance change trends)Anomaly detection (determining the existence of over-temperature, over-voltage, and abnormal welding of terminals, etc.)Regression modeling and prediction (predicting SOH or\u2026","_links":{"self":[{"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/glossary\/4859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/glossary"}],"about":[{"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/types\/glossary"}],"author":[{"embeddable":true,"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":1,"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/glossary\/4859\/revisions"}],"predecessor-version":[{"id":4861,"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/glossary\/4859\/revisions\/4861"}],"wp:attachment":[{"href":"https:\/\/www.krlpower.com\/fr\/wp-json\/wp\/v2\/media?parent=4859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}