After the rollout of iOS 17.4, a noticeable subset of iPhone users reported unusually rapid battery drain. In several cases, devices shed a substantial portion of charge within just a couple of hours, with reports citing a drop of around 40 percent. These concerns were highlighted by Daily Mail coverage, sparking discussions about how the update affects power usage on various models.
Beyond the energy draw, some owners encountered charging delays post-update. For instance, a user with an iPhone 11 Pro described a long charging journey from 40 percent to 94 percent that extended beyond four hours. While anecdotal, such experiences contributed to a broader conversation about the update’s impact on charging efficiency and overall device performance.
Industry observers note that these battery and charging anomalies could be temporary, potentially stabilizing after a short adjustment period. Practical steps are commonly suggested: a device restart, ensuring all apps are up to date, and reviewing battery usage in Settings. The Battery section offers a breakdown of energy distribution by app, helping users pinpoint power-hungry processes. When an app shows unusually high energy impact, updating, closing, or uninstalling it is frequently recommended to restore normal consumption patterns.
iOS 17.4 also introduces new battery health insights, including data such as charge cycles, device manufacture date, and the time since first use. The update became available on March 5 and brought additional features like expanded support for third-party app stores in EU countries, along with other enhancements across Apple devices. These additions aim to give users more visibility into battery longevity and to expand app distribution options within compliant regions.
Meanwhile, comparisons with other manufacturers continue to surface. In recent discussions, attention has turned to how devices from major rivals perform under similar update conditions. For example, conversations around the Samsung Galaxy S24 Ultra reference discharge behaviors seen with last year’s model, highlighting how software changes can influence power metrics across different ecosystems.