Electric Vehicle Range Prediction Using HVAC Energy Lookup
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Solution Overview
Problem
Current systems for predicting the driving range of electric vehicles do not consider the power draw from the HVAC system and are unable to learn a driver's habits over time, providing limited accuracy in range estimation.
Innovation Solution
A system and method that determines the remaining travel distance by calculating usable battery energy, accounting for HVAC power usage, and using short-term and long-term accumulators to adapt to driver habits, providing quick and accurate range predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current range prediction systems are used, then the system complexity is low, but the measurement precision of range estimation is insufficient because they do not consider HVAC power draw
Solution Approach 1:
The system pre-calculates and stores HVAC energy consumption data across different operating conditions before actual use. During range prediction, it retrieves pre-computed HVAC energy values based on current temperature and humidity conditions, avoiding real-time complex thermal calculations while improving accuracy.
Solution Approach 2:
The patent introduces an intermediary lookup table that maps environmental conditions (temperature, humidity) to HVAC energy consumption values. This intermediary structure bridges the gap between simple range prediction and complex thermal modeling, providing accurate HVAC energy estimation without direct real-time simulation.
2Adaptability or versatility
If simple range prediction algorithms are used, then the ease of operation is high, but the adaptability to driver habits is poor
Solution Approach 1:
The system dynamically adjusts the range prediction model by incorporating learned driver behavior patterns. It transitions from a static algorithm to a dynamic adaptive system that evolves based on observed driving habits, optimizing predictions for individual users over time.
Solution Approach 2:
The patent implements a feedback mechanism where actual driving data is continuously collected and used to refine the range prediction model. The system learns from discrepancies between predicted and actual energy consumption, progressively improving accuracy through iterative optimization.
3Measurement precision
If real-time HVAC energy calculation is performed, then the measurement precision improves, but the loss of time for computation increases
Solution Approach 1:
The system pre-computes HVAC energy consumption for various environmental conditions and stores these results in lookup tables. During operation, it retrieves pre-calculated values based on current conditions, eliminating the need for real-time thermal simulations while maintaining accurate energy estimation.
Solution Approach 2:
The patent uses simplified, pre-computed HVAC energy models that are computationally inexpensive to query. Rather than performing expensive real-time calculations, it employs lightweight lookup operations that provide sufficient accuracy with minimal computational overhead.
Data Source
AI summary
A method for predicting the remaining travel distance of an electric vehicle. The method includes determining a useable battery energy value based on battery state-of-charge and battery capacity and a power value needed to heat or cool a vehicle cabin. The method determines an available battery energy value based on the useable battery energy value and an estimated energy value to provide the vehicle cabin heating or cooling, where the estimated energy value is determined using the power value. The method determines a recent energy used value based on an actual recent HVAC energy used value, a recent energy used value with no HVAC system loads and a recent energy used value with maximum HVAC system loads. The method determines a recent distance traveled value and determines the range by dividing the recent distance traveled value by the recent energy used value and multiplying by the available battery energy value.