Battery Management Control for Uniform Cell State-of-Health
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Solution Overview
Problem
Conventional battery management systems for lithium-ion batteries with silicon-dominant anodes lack the ability to make small adjustments to operating parameters based on state-of-health (SOH) values, leading to suboptimal performance and reduced durability in advanced battery technologies.
Innovation Solution
A state-of-health balanced battery management system (BMS) that calculates and adjusts operating parameters such as voltage, charge rate, and temperature to ensure uniform SOH across cells, using physics-based and machine-learning models to predict and update SOH values, allowing for finer control and optimization of battery performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional battery management systems are used for lithium-ion batteries with silicon-dominant anodes, then the system structure is simple, but the performance is suboptimal and durability is reduced
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting operating parameters (voltage, charge rate, temperature) based on calculated SOH values. The BMS calculates SOH using physics-based models and machine-learning algorithms, then modifies operating parameters to optimize battery performance and extend durability, transforming the static conventional BMS into a dynamic adaptive system.
Solution Approach 2:
The patent implements feedback mechanisms where the BMS continuously monitors battery state, calculates SOH values, and uses this information to adjust operating parameters. The system incorporates feedback loops that update SOH predictions based on actual battery performance data, enabling continuous optimization of battery management strategies to improve durability.
2Productivity
If conventional battery management systems are used, then the system is easy to operate, but energy density and performance are suboptimal
Solution Approach 1:
The patent applies self-service by enabling the BMS to automatically calculate SOH values and adjust operating parameters without manual intervention. The system uses integrated physics-based models and machine-learning algorithms to autonomously optimize battery management, maximizing energy density and performance while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The system dynamically changes operating parameters (voltage, charge rate, temperature) based on calculated SOH values to optimize energy density. The BMS adjusts these parameters in real-time based on battery state, enabling the battery to operate at optimal performance levels while the system handles the complexity of parameter management automatically.
3Duration of action of moving object
If conventional battery management systems are used, then the system is cost-effective, but battery lifetime is limited
Solution Approach 1:
The patent applies preliminary action by calculating and adjusting operating parameters based on predicted SOH values before degradation becomes critical. The BMS uses physics-based models and machine-learning algorithms to forecast battery health trends and proactively modifies operating parameters to prevent accelerated degradation, extending battery lifetime through preventive management.
Solution Approach 2:
The system dynamically adjusts operating parameters (voltage, charge rate, temperature) based on calculated SOH values to optimize battery lifetime. By changing these parameters in response to battery health status, the system extends usable battery life while managing the complexity of continuous monitoring and adjustment.
Data Source
AI summary
Systems and methods are provided for state-of-health balanced battery management system. State-of-health (SOH) of one or more lithium-ion cells may be assessed, and based on the assessing of state-of-health (SOH), the one or more lithium-ion cells may be controlled. The controlling may include setting or modifying one or more operating parameters of at least one lithium-ion cell, and the controlling may be configured to equilibrate the state-of-health (SOH) of the one or more lithium-ion cells or to modify a state-of-health (SOH) of at least one lithium-ion cell so that the one or more lithium-ion cells have a uniform state-of-health (SOH).