Predictive EV Battery Thermal Management
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Solution Overview
Problem
Current battery thermal management systems in electric vehicles are reactive and inefficient, leading to reduced vehicle range due to poor thermal control, as they rely on heuristic approaches based on design-phase data and do not optimize energy usage under varying conditions.
Innovation Solution
A predictive model is created using data from autonomous drive platforms and vehicle characterization, combined with real-time data from sensors and network locations, to determine if active cooling or warming is necessary, optimizing energy usage and extending battery life by avoiding excessive temperature ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If battery size and capacity are increased to extend vehicle range, then the vehicle can travel longer distances, but the vehicle size and weight increase
Solution Approach 1:
The system performs preliminary thermal management actions by predicting future battery temperature based on upcoming route, weather, and traffic conditions. The predictive model calculates expected temperature changes and activates cooling or heating before the battery actually reaches problematic temperature ranges, preventing thermal issues before they occur and avoiding the need for oversized batteries with excessive cooling capacity.
Solution Approach 2:
The system dynamically adjusts thermal management strategy in real-time based on changing conditions. The predictive model continuously updates temperature predictions as new data becomes available from sensors, GPS, and weather services, allowing the system to adapt cooling/heating intensity to actual needs rather than using fixed conservative thresholds, thereby reducing energy consumption and extending range.
2Device complexity
If reactive thermal management with sensor data and set limits is used to control cooling power, then the system is simple to implement, but energy usage is not optimized and vehicle range is reduced by 10-15%
Solution Approach 1:
Instead of reacting to current temperature readings, the system performs preliminary thermal management by predicting future temperature based on upcoming driving conditions, ambient temperature changes, and battery charge/discharge patterns. This allows the system to activate cooling or heating before the battery actually reaches problematic temperature ranges, preventing thermal issues proactively rather than reactively.
Solution Approach 2:
The system implements a feedback loop where the predictive model continuously receives new sensor data, GPS location, weather information, and actual battery temperature readings. This feedback refines the temperature predictions and adjusts the thermal management strategy in real-time, optimizing energy usage by activating cooling or heating only when and where actually needed based on predicted future conditions rather than current static thresholds.
3Ease of operation
If conventional heuristic-based thermal management is used based on design phase data, then the system is easy to control, but it is not optimal for varying real-world conditions and reduces vehicle efficiency
Solution Approach 1:
The system dynamically adapts thermal management strategy to varying real-world conditions by continuously updating predictive models with actual sensor data, GPS location, weather information, and traffic patterns. Rather than relying on static heuristic rules from design phase, the system adjusts cooling/heating intensity and timing based on predicted future battery temperature under actual driving conditions, making the system highly adaptable to diverse scenarios.
Solution Approach 2:
The system changes key parameters of thermal management based on predicted conditions, including activation temperature thresholds, cooling/heating power levels, and timing of thermal management actions. The predictive model calculates optimal parameter settings by considering upcoming route elevation changes, ambient temperature variations, and battery charge/discharge schedules, allowing the system to optimize for each specific driving scenario rather than using fixed parameters.
Data Source
AI summary
A thermal management system of a battery of an electric vehicle proactively manages the temperature of the battery based on sensor data and sets limits to control cooling and heating of the battery. Using the data gathered from an autonomous drive platform, a highly-efficient control system which uses predictive modelling can be created. A control system predicts the battery final temperature and determines if cooling and/or heating is necessary for the route. If cooling and/or heating is not necessary, the thermal management system may be simply turned off to save energy. This is a dynamic approach which should optimize energy usage under all situations using trip predictive information (from GPS, route-calculation algorithms, and weather information), and thermal model predictive controls to determine battery final temperatures.