Battery Preconditioning Using Charging Intent Prediction
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Solution Overview
Problem
Conventional battery preconditioning technologies lack accuracy in determining a customer's charging intent, leading to unnecessary preconditioning and inefficient energy consumption.
Innovation Solution
A system and method that utilizes big data to collect and analyze vehicle charging tendency factors, generating charging scenarios with high probability of actual charging, and performs preconditioning only when conditions are met, thereby optimizing charging time and reducing energy waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional preconditioning technology applies preconfigured conditions unilaterally, then the system complexity is reduced, but the accuracy in determining customer charging intent deteriorates
Solution Approach 1:
The system changes the parameters used for preconditioning decisions from simple preconfigured conditions to multiple charging tendency factors including navigation destination, proximity to charging stations, battery state of charge, and customer charging history. This allows accurate determination of charging intent while maintaining reasonable system complexity through modular factor analysis.
2Ease of operation
If preconditioning is performed based on simple preconfigured conditions, then the ease of operation is improved, but the energy consumption increases due to unnecessary conditioning
Solution Approach 1:
The system performs preliminary analysis of multiple charging tendency factors before executing preconditioning actions. By evaluating navigation destination, proximity to charging stations, battery state of charge, and customer charging history in advance, the system determines whether preconditioning is truly necessary, thereby avoiding unnecessary energy consumption while maintaining ease of operation through automated decision-making.
3Adaptability or versatility
If the same preconditioning condition is applied to all vehicles, then the adaptability is reduced, but the device complexity is lowered
Solution Approach 1:
The system applies local quality by customizing preconditioning conditions for each customer based on their individual charging history, preferences, and behavior patterns. Each customer receives tailored preconditioning strategies rather than uniform treatment, achieved through analyzing individual charging tendency factors while using a standardized modular system architecture to manage the complexity.
4Productivity
If preconditioning is performed without accurate charging intent detection, then the productivity is maintained, but the loss of energy increases
Solution Approach 1:
The system implements feedback by continuously monitoring charging tendency factors and actual charging behavior, then using this information to refine future preconditioning decisions. The navigation destination, proximity to charging stations, battery state of charge, and customer charging history are constantly evaluated, and the system learns from actual charging outcomes to improve accuracy, thereby reducing energy waste while maintaining charging productivity.
Data Source
AI summary
A battery conditioning system and method configured to shorten charging time and block energy consumption due to unnecessary conditioning by performing battery pre-conditioning in a timely manner through collecting customer charging tendency data, etc., using big data and generating a charging scenario by combining charge-inducing factors to perform pre-conditioning.