Adaptive Cruise and Battery Cooling for Driver-Specific Vehicle Control
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Solution Overview
Problem
Existing vehicle systems struggle to efficiently manage energy resources and predict battery life based on individual driving styles, leading to suboptimal performance and accuracy in adaptive cruise control and distance-to-empty predictions.
Innovation Solution
Implementing a battery thermal management system and adaptive cruise control system that utilize machine learning models to analyze historic driving data and predict individual driving patterns, allowing for personalized adjustments to cooling rates and control operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed cooling rate is used for the battery thermal management system, then the system structure is simple, but the energy management efficiency is suboptimal for different driving styles
Solution Approach 1:
The cooling rate of the battery thermal management system is dynamically adjusted based on predicted driving behavior. The controller modifies the cooling rate in response to predicted acceleration or deceleration events, transitioning from a static fixed cooling rate to a dynamic adaptive cooling rate that matches actual thermal demands of different driving styles.
Solution Approach 2:
The system performs preliminary cooling before anticipated high-power events by predicting future driving behavior. Machine learning models analyze historical driving data to forecast upcoming acceleration or deceleration events, allowing the thermal management system to proactively adjust cooling rates before thermal demands actually occur, optimizing energy management efficiency.
2Measurement precision
If generic adaptive cruise control is used, then the system is simple to implement, but the accuracy of distance-to-empty prediction is reduced
Solution Approach 1:
The adaptive cruise control system incorporates driver behavior parameters derived from machine learning models. Instead of using generic control parameters, the system adjusts cruise control behavior based on predicted driver actions (acceleration patterns, deceleration rates) specific to individual driving styles, thereby improving distance-to-empty prediction accuracy without substantially increasing system complexity.
Solution Approach 2:
The system uses historical driving data feedback to continuously improve distance-to-empty predictions. Machine learning models analyze past driving behavior patterns and feed this information back into the adaptive cruise control system, enabling increasingly accurate predictions of future driving scenarios and their impact on energy consumption.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables vehicles to efficiently allocate resources, accurately predict battery life, and recreate personalized driving behaviors, enhancing both energy management and driving experiences.
Implementation Method 1
A vehicle may include a battery thermal management system and a controller. The controller may use data to change a rate of cooling of the battery thermal management system.
Data Source
AI summary
A vehicle, among other things, may use predicted rates of change in vehicle speed based on past driving data of a particular user to allocate resources within the vehicle, to recreate the behavior of particular user in an automated driving system, or to accurately predict a capability of the vehicle.