Battery Aging State Correction Model for Environmental Adaptation
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Solution Overview
Problem
Existing methods for determining the aging state of device batteries in grid-independently operated electrical devices, such as electric vehicles, are inaccurate due to reliance on conventional aging state models that fail to account for individual usage patterns and environmental conditions, leading to uncertainties in predicting remaining service life and residual value.
Innovation Solution
A method involving the detection of operating variable curves and load factors to create a correction model that corrects for systematic influences, using a combination of direct or indirect measurements and swarm intelligence to improve the accuracy of aging state determination, allowing for precise estimation of capacity-related and resistance-change-related aging states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional aging state models are used to determine battery aging, then the determination process is simple, but the accuracy of aging state prediction deteriorates due to inability to account for individual usage patterns and environmental conditions
Solution Approach 1:
The patent introduces a correction model as an intermediary component that bridges the simple conventional aging state model and the complex individual usage patterns/environmental conditions. The correction model takes load factors (derived from usage patterns and environmental data) as input and outputs correction values that adjust the aging state determined by the conventional model, thereby improving accuracy without requiring complete redesign of the determination process
Solution Approach 2:
The patent segments the aging state determination process into two independent parts: (1) a conventional aging state model that provides baseline aging determination, and (2) a separate correction model that accounts for individual usage patterns and environmental conditions. This segmentation allows each component to be optimized independently while working together to achieve high accuracy
2Reliability
If conventional aging state models are used, then implementation is straightforward, but reliability of remaining service life prediction deteriorates
Solution Approach 1:
The correction model serves as an intermediary that enhances the reliability of remaining service life predictions by incorporating individual usage patterns and environmental conditions without requiring complete model restructuring. It processes load factors and provides correction values that adjust the conventional model's output, making predictions more reliable while maintaining manageable complexity
Solution Approach 2:
The patent implements a feedback mechanism where the correction model continuously receives load factor information from actual usage patterns and environmental conditions, processes this feedback, and adjusts the aging state determination accordingly. This feedback loop ensures that predictions adapt to real-world conditions, improving reliability over time
3Measurement precision
If conventional aging state models are used, then computational resources are minimized, but accuracy under varying environmental conditions deteriorates
Solution Approach 1:
The correction model acts as an intermediary that specifically addresses environmental condition adaptability. It receives load factors representing various environmental conditions and usage patterns, processes them, and provides correction values that enable the conventional model to accurately determine aging state across diverse environmental conditions without requiring the conventional model itself to be complex
Data Source
AI summary
The disclosure relates to a method for providing an aging state of a device battery of a battery-operated device including detecting curves of operating variables of the device battery and providing at least one load factor at a determination time, providing a correction model that maps a correction variable depending on the at least one load factor, and ascertaining an aging state by evaluating the curves of the operating variables with the aid of an aging state model or an aging state observer or an aging state measurement and depending on the correction variable resulting from the at least one load factor of the device battery of the battery-operated device at the determination time.