Battery Life Prediction Using Representative Usage Pattern Clustering
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Solution Overview
Problem
Conventional battery life prediction methods struggle with accuracy when faced with complex usage patterns, requiring recalculations and prolonged times, especially as batteries degrade, leading to increased prediction errors.
Innovation Solution
A battery life prediction apparatus using an artificial neural network model classifies usage patterns into groups and identifies a representative pattern within each group, predicting battery life based on the representative pattern's data rather than individual usage patterns, thereby reducing calculation time and maintaining accuracy even with changing usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If deep discharge occurs to maximize battery capacity utilization, then energy output is improved, but battery lifespan deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously monitoring battery state parameters (voltage, current, temperature) and predicting future states before actual discharge occurs. The prediction model anticipates potential deep discharge conditions and triggers protective measures in advance, allowing the system to maximize energy output while preventing lifespan-damaging deep discharge events.
Solution Approach 2:
The system implements feedback mechanisms where real-time battery state data is continuously fed into the prediction model. The model processes this feedback to adjust discharge control strategies dynamically, ensuring that energy extraction remains within safe boundaries while maximizing overall energy output over the battery's operational life.
2Power
If frequent discharge and charge cycles are performed to increase power output, then energy production is improved, but measurement and detection complexity increases
Solution Approach 1:
The prediction model serves multiple functions simultaneously: it monitors battery state, predicts future conditions, detects anomalies, and controls discharge rates. This multi-functionality consolidates what would otherwise require separate complex measurement and detection systems into a single integrated model, reducing overall system complexity while enabling frequent cycling for high power output.
Solution Approach 2:
The system uses its own operational data (discharge/charge cycle information, voltage, current measurements) to automatically update and refine the prediction model. This self-service approach allows the model to improve its accuracy over time without requiring external complex measurement systems or manual calibration, enabling frequent cycling with simplified detection infrastructure.
Data Source
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AI summary
A battery life prediction apparatus capable of more quickly and precisely predicting a remaining life of a battery for an input of a complex battery usage pattern includes a data generation unit configured to generate remaining life data corresponding to each of a plurality of usage pattern information regarding the battery and a battery life prediction unit configured to classify a plurality of usage patterns into at least one class by using a trained artificial neural network model, determine a representative usage pattern among usage patterns belonging to each class, and predict a life of the battery based on remaining life data corresponding to the representative usage pattern.