Battery Preconditioning Using Charging Scenario 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 and reliability, and performs preconditioning only when conditions are met, thereby optimizing charging times and reducing energy waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional battery 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 patent segments the charging decision-making process into multiple independent modules: big data collection module, charging tendency factor analysis module, scenario generation module, and preconditioning execution module. Each module handles specific aspects of charging intent determination, allowing the system to maintain low complexity while achieving high accuracy through distributed processing of charging behavior data, navigation information, and vehicle state parameters.
Solution Approach 2:
The patent introduces an intermediary scenario generation module that acts as a mediator between raw data collection and preconditioning execution. This module synthesizes multiple charging tendency factors (navigation destination, battery state, historical behavior) into standardized charging scenarios, enabling accurate intent determination without requiring direct complex interactions between all system components.
2Ease of operation
If preconditioning is performed based on generalized conditions, then the ease of operation is improved, but the energy consumption increases due to unnecessary conditioning
Solution Approach 1:
The patent performs preliminary analysis of charging tendency factors and generates charging scenarios before executing preconditioning. By evaluating navigation destination, battery state of charge, temperature, and historical charging behavior in advance, the system determines whether preconditioning is actually needed, preventing unnecessary energy consumption while maintaining simple automated operation for the user.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor actual charging behavior and compare it with predicted scenarios. This feedback loop allows the system to learn from past charging decisions and adjust future preconditioning actions, reducing energy waste from unnecessary conditioning while keeping the operation simple and automated.
3Device complexity
If the same preconditioning condition is applied to all vehicles, then the device complexity is reduced, but the adaptability to different customer charging behaviors deteriorates
Solution Approach 1:
The patent applies local quality by customizing preconditioning parameters according to individual vehicle characteristics, customer charging behavior patterns, and specific operational contexts. Instead of uniform preconditioning, the system adjusts heating/cooling intensity, timing, and duration based on local factors such as battery state of charge, ambient temperature, navigation destination, and historical charging preferences of each customer.
Solution Approach 2:
The patent makes the preconditioning system dynamic by continuously adapting to changing conditions. The scenario generation module dynamically creates charging scenarios based on real-time data from big data collection, and the system adjusts preconditioning parameters dynamically during execution based on feedback from actual charging behavior, enabling high adaptability without requiring complex manual configuration.
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.