Vehicle Usage Data Model for Dynamic Battery Selection
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Solution Overview
Problem
Conventional vehicle management systems fail to adapt to changing user requirements, often necessitating vehicle replacement and may not extend battery life in a manner suitable for user needs.
Innovation Solution
A management apparatus and method that acquires vehicle usage state information, applies it to models to select a suitable secondary battery for electric vehicles, recommending batteries based on user inputs and usage patterns, allowing for periodic re-evaluation and adaptation to user needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If conventional vehicle management systems are used to extend battery life, then battery longevity is improved, but the system cannot adapt to changing user requirements and may not provide suitable battery solutions
Solution Approach 1:
The system dynamically adapts battery management strategies based on real-time vehicle usage state information and changing user requirements. The management apparatus continuously monitors usage patterns and adjusts battery selection and management approaches accordingly, transitioning from static to dynamic adaptation.
Solution Approach 2:
The system implements feedback mechanisms by acquiring vehicle usage state information and using it to continuously optimize battery selection. The management apparatus receives feedback on actual usage patterns and adjusts future battery recommendations to better match user needs while extending battery life.
2Adaptability or versatility
If vehicle replacement is performed when user requirements change, then user requirements are satisfied, but resource waste increases and productivity decreases
Solution Approach 1:
Instead of discarding vehicles when requirements change, the system recovers value by matching existing vehicles with new users whose requirements align with the vehicle's capabilities. The management apparatus facilitates vehicle redistribution and battery replacement rather than complete vehicle replacement, reducing resource waste.
Solution Approach 2:
The system enhances the universality of vehicle assets by enabling the same vehicle to serve different user requirements through battery replacement and reallocation. A single vehicle platform can adapt to multiple usage scenarios by changing its powertrain configuration.
3Measurement precision
If comprehensive vehicle usage state information is collected and processed through models, then battery selection accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces a management apparatus as an intermediary between raw usage data and battery selection decisions. This intermediary layer processes complex usage state information through trained models, transforming multifaceted data into actionable battery recommendations without requiring direct complex interactions between all system components.
Solution Approach 2:
The system uses trained prediction models that capture the essential relationships between usage patterns and suitable battery characteristics. Instead of processing all raw data directly, the models create simplified representations (copies) of usage states that can be efficiently evaluated for battery matching.
Data Source
AI summary
A management apparatus includes an acquirer configured to acquire vehicle usage state information about a usage state of a vehicle, and a processing unit configured to apply the vehicle usage state information to a model which outputs feature amounts when vehicle usage information is input thereto to acquire the feature amounts, to select a secondary battery recommended to be mounted in the vehicle for which the vehicle usage state information is acquired from secondary batteries providable as the secondary battery to be mounted in the vehicle and storing electric power for travel on the basis of the acquired feature amounts, and to present the selected secondary battery.