Battery Condition Metric Computation via Embedded Controller
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Solution Overview
Problem
The reliability of determining the health condition of batteries in computing devices is compromised due to inconsistent and unreliable data, leading to batteries being deemed problematic or malfunctioning even when they are still in good condition.
Innovation Solution
An information handling system with an embedded controller that receives a cycle count from the battery system, queries for an expected margin of error, and computes a condition metric based on the battery's capacity and design capacity, providing credible determination of battery health through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional battery health determination methods are used, then the process is simple, but the reliability of battery condition assessment deteriorates due to inconsistent and unreliable data
Solution Approach 1:
The patent changes the parameters used for battery condition assessment from simple cycle count to a multi-parameter approach including cycle count, expected margin of error, prediction of capacity, and design capacity. This transformation allows for more reliable condition metrics by considering multiple factors rather than relying on a single parameter that may be inconsistent or unreliable.
Solution Approach 2:
The embedded controller implements a feedback mechanism where it queries the battery system for multiple parameters, processes them through conditional logic, and generates a condition metric that reflects the actual battery health. This feedback loop ensures that the condition assessment is continuously updated based on current battery data, improving reliability over time.
2Reliability
If battery replacement is performed based on unreliable data, then the frequency of replacement increases, but the cost and resource waste increase due to replacing batteries in good condition
Solution Approach 1:
The patent transforms the replacement decision process from being based on unreliable single-parameter data to using a comprehensive condition metric derived from multiple parameters including prediction of capacity and design capacity. This allows for accurate identification of batteries that truly need replacement, preventing premature disposal of functional batteries and reducing resource waste.
Solution Approach 2:
The patent replaces the mechanical/simple approach of counting cycles with a computational system that calculates condition metrics based on multiple parameters. This substitution of computational analysis for simple mechanical counting enables more accurate replacement decisions, reducing both false positives (replacing good batteries) and false negatives (keeping failing batteries).
Data Source
AI summary
In one or more embodiments, an information handling system may include an embedded controller, communicatively coupled to a battery system that is configured to power the information handling system. The embedded controller may be configured to: receive a cycle count from the battery system; determine that the cycle count is above a threshold; query the battery system for an expected margin of error, in response to determining that the cycle count is above the threshold; receive the expected margin of error from the battery system; determine that the expected margin of error is within a range; and in response to determining that the expected margin of error is within the range, compute a condition metric of the battery system based on a prediction of a capacity of the battery system and a design capacity of the battery system and store the condition metric of the battery system.