Battery Management Transfer Learning for Real-World EV Adaptation
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Solution Overview
Problem
Existing battery management systems for electric vehicles are limited by their inability to generalize beyond training conditions, leading to conservative and heuristic designs that fail to adapt to real-world scenarios, resulting in suboptimal performance and potential safety issues due to data shortages and lack of adaptability as batteries age.
Innovation Solution
The method employs transfer learning using training data from target and auxiliary vehicles with similar battery systems, optimizing algorithms through cloud computing and connected vehicle technology to adapt to individual driving and charging patterns, enabling the battery management system to learn from past usage and adapt to novel situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If battery management algorithms are designed based on limited laboratory experimental data using conservative and heuristic methods, then system safety is maintained under worst-case scenarios, but the system lacks adaptability to real-world conditions and individual vehicle usage patterns
Solution Approach 1:
The patent applies preliminary action by pre-training battery management algorithms using transfer learning from auxiliary vehicles before deployment to the target vehicle. This allows the system to learn from past usage patterns and environmental conditions in advance, improving adaptability while maintaining safety through pre-established conservative parameters that can be dynamically adjusted
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual battery performance in real-world conditions and using this data to refine and update management algorithms. The system learns from operational feedback to adapt to individual vehicle usage patterns while maintaining safety constraints, resolving the contradiction between conservative design and adaptability
2Measurement precision
If battery management algorithms are designed to operate in very orchestrated and specific environments with exacting experimental data, then prediction accuracy is maintained for known conditions, but the system fails to generalize to novel scenarios encountered in the field
Solution Approach 1:
The patent applies universality by developing battery management algorithms that can function across multiple vehicle types and usage scenarios. Through transfer learning from auxiliary vehicles with similar battery systems, the algorithms achieve universal applicability while maintaining prediction accuracy through adaptive parameter adjustment for each target vehicle's specific conditions
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting battery management parameters based on transfer learning results from auxiliary vehicles. The system maintains core safety parameters from conservative design while adapting operational parameters to match actual usage patterns and environmental conditions, enabling both accuracy and generalization
3Ease of manufacture
If conservative and heuristic methods are used to design battery management algorithms, then the system can operate with limited battery data, but development time is extended and system capability is severely limited
Solution Approach 1:
The patent applies copying by using transfer learning to replicate successful battery management strategies from auxiliary vehicles with similar battery systems. Instead of designing algorithms from scratch for each vehicle, the system copies and adapts proven approaches, significantly reducing development time while maintaining or enhancing system capability through targeted optimization
Data Source
AI summary
A method and apparatus for optimizing a battery management system (BMS) are provided. The method includes: obtaining training data, wherein the training data comprises data from a target vehicle and data from auxiliary vehicles, and the auxiliary vehicles are vehicles mounted with a same or similar battery system as the target vehicle; optimizing a BMS related algorithm for the target vehicle by performing transfer learning based on the training data, wherein the BMS related algorithm comprises one or a combination of: a battery model, parameters for the battery model, a battery management algorithm, and parameters for the battery management algorithm.