Battery SOC Estimation Using Adaptive Weights by Upper System
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Solution Overview
Problem
Existing battery state estimation methods lack reliability due to the use of uniformly determined weights that do not consider the specific type or state of the upper system on which the battery is mounted, leading to unreliable diagnosis results.
Innovation Solution
A battery state estimation method and system that adjusts weight values in estimation models based on identification information of the upper system and current battery conditions, using a controller to calculate average values from multiple estimation models with optimized weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniformly determined weights are used in estimation models, then the estimation method is simple to implement, but the reliability of battery state estimation deteriorates because it does not consider the specific type or state of the upper system
Solution Approach 1:
The patent applies dynamics by making the weight values adjustable and adaptable based on upper system identification information and battery state information. Instead of fixed uniform weights, the system dynamically selects or adjusts weights according to the specific application context (e.g., electric vehicle, energy storage system) and current battery conditions, thereby improving estimation reliability while maintaining reasonable implementation complexity through automated adaptation.
Solution Approach 2:
The patent changes the parameter of weight values from fixed to variable based on identification information and battery state information. By modifying the weight parameters according to different upper systems and battery conditions, the estimation model adapts to specific scenarios, improving reliability without requiring complete redesign of the estimation architecture.
2Measurement precision
If multiple estimation models with different weights are used to improve reliability, then the estimation precision improves, but the device complexity increases due to need for weight management and selection
Solution Approach 1:
The patent applies self-service by enabling the estimation system to automatically select or adjust weight values based on identification information and battery state information without requiring external manual configuration. The system serves itself by adapting to different upper systems and battery conditions autonomously, improving precision while minimizing the operational complexity for users.
Solution Approach 2:
The patent applies preliminary action by pre-establishing multiple estimation models and weight values that correspond to different upper systems and battery states. This preparation work is done in advance, allowing the system to quickly select appropriate models and weights when deployed, thereby improving response precision without adding significant real-time computational complexity.
3Ease of operation
If uniform weights are applied to all estimation models, then the system is easy to operate, but the diagnosis result reliability deteriorates due to lack of customization for different battery conditions
Solution Approach 1:
The patent changes the weight parameters based on battery state information (such as state of charge, temperature, aging level) and upper system identification. This automatic parameter adjustment maintains ease of operation for end users while significantly improving diagnosis reliability by customizing the estimation behavior to match current battery conditions and application requirements.
Solution Approach 2:
The patent makes the weight values dynamic rather than static, allowing them to change based on real-time battery state information and upper system characteristics. This dynamic adaptation improves diagnosis reliability without requiring complex manual intervention, as the system automatically adjusts parameters based on sensor inputs and identification data.
Data Source
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AI summary
A battery state estimation method and a battery system providing the method. The battery system includes a battery including a plurality of battery cells; a communication device that communicates with an upper system on which the battery system is mounted and receives identification information of the upper system; a monitoring device that collects battery information; a storage device that stores a plurality of state of charge (SOC) estimation models estimating an SOC of each of the plurality of battery cells based on the battery information according to a predetermined algorithm and stores first weight values corresponding to the identification information; and a controller that calculates an average value of first SOCs by adding up results of applying the first weight values to a plurality of SOCs estimated by the plurality of SOC estimation models.