Adaptive Fast Charging Control for Lithium Plating Prevention
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Solution Overview
Problem
Lithium plating in Li-ion batteries, which occurs during fast charging and low temperatures, significantly reduces battery lifespan and poses safety risks, with current solutions lacking effective predictive maintenance and recycling mechanisms.
Innovation Solution
Implementing a dynamic adaptive charging system that uses machine learning models to monitor anode potential, predict lithium plating likelihood, and adjust charging policies in real-time to prevent lithium plating, utilizing cloud and edge processors for data analysis and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If fast charging is applied to Li-ion batteries, then charging speed is improved, but lithium plating occurs which reduces battery lifespan and safety
Solution Approach 1:
The system performs preliminary actions by continuously monitoring battery state parameters (temperature, voltage, current, state of charge) before lithium plating can occur. The machine learning model predicts the likelihood of lithium plating in advance, allowing the charging policy to be adjusted preemptively to prevent plating while maintaining fast charging capabilities
Solution Approach 2:
The charging policy is made dynamic and adaptive rather than fixed. The system continuously adjusts charging parameters (current, voltage, power limits) based on real-time battery state and predicted lithium plating risk. The charging curve is dynamically optimized to keep the anode potential within safe boundaries while maximizing charging speed
2Speed
If fast charging is applied to Li-ion batteries, then charging speed is improved, but safety risks increase due to lithium plating
Solution Approach 1:
The system implements continuous feedback monitoring of battery state parameters including temperature, voltage, current, and state of charge. The machine learning model uses this feedback to predict lithium plating risk and dynamically adjust charging parameters. Safety boundaries for anode potential are monitored in real-time, and charging is adjusted or halted when thresholds are approached, preventing harmful lithium plating while enabling fast charging
3Reliability
If traditional charging methods are used, then battery lifespan is maintained, but charging speed is reduced
Solution Approach 1:
The system changes key charging parameters dynamically based on battery state. Instead of using fixed traditional charging rates, the system adjusts current, voltage, and power limits in real-time based on temperature, state of charge, and predicted lithium plating risk. The charging protocol transitions between different charging stages (constant current, constant voltage, tapering) with optimized parameters that extend battery life while maintaining high charging speeds
4Reliability
If real-time monitoring and adaptive control systems are implemented, then lithium plating is reduced, but system complexity increases
Solution Approach 1:
The system performs self-service by using the battery's own operational data (voltage, current, temperature, state of charge) to predict lithium plating risk and adjust charging parameters. The machine learning model is trained on battery-specific characteristics and adapts to individual battery behavior patterns. This self-monitoring and self-adjusting capability reduces lithium plating without requiring complex external intervention systems
Data Source
AI summary
In one aspect, a computer-implemented method may include receiving, at a cloud-based computing system, historical data comprising (i) battery charging profiles associated with a fleet of vehicles comprising battery packs, (ii) geographical climate information associated with the fleet of vehicles, (iii) lithium plating prediction results associated with the fleet, or (iv) some combination thereof. The method includes training, using the historical data, lithium plating prediction models to predict a likelihood of occurrence of lithium plating for a battery pack, receiving, from an edge processor communicatively coupled to a battery pack, battery pack charging data, determining, using the models based on the battery pack charging data, the likelihood of occurrence, based on the likelihood of occurrence, modifying a battery charging policy for the battery pack, and transmitting the battery charging policy to cause the edge processor to control charging of the battery pack.