EV Range Estimation Using Kalman Filters and Trip Data
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Solution Overview
Problem
Electric vehicles face range anxiety due to limited driving range, long charging times, and limited charging infrastructure, which is exacerbated by uncertainties in driving conditions and battery state, leading to sub-optimal operating decisions and accelerated battery aging.
Innovation Solution
A method for generating accurate end-of-range estimates using previous trip data, model parameters, and error covariance matrices, updated in real-time through Kalman filters or moving horizon observers, to provide drivers with precise remaining range information, even without a defined destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional range estimation methods are used based on remaining battery capacity and simple voltage measurements, then the system complexity is low, but the measurement precision and reliability of range estimation is poor
Solution Approach 1:
The patent implements feedback by continuously monitoring actual driving conditions (speed, acceleration, road slope, temperature) and comparing predicted range with actual range consumed. The Kalman filter uses this feedback to recursively update model parameters and correct estimation errors in real-time, improving accuracy without requiring complex hardware changes
Solution Approach 2:
The patent replaces simple voltage-based mechanical/electrical measurement with a computational model-based estimation system. Instead of relying on direct voltage measurements, the system uses software algorithms (Kalman filter, moving horizon observer) to estimate range based on battery capacity, current draw, and environmental factors, substituting computational complexity for measurement complexity
2Reliability
If static range estimates are provided without real-time updates, then the computational load and processing time are reduced, but the reliability of range information deteriorates under changing driving conditions
Solution Approach 1:
The patent implements periodic action by updating range estimates at regular intervals during driving (e.g., every second or at predefined events). The Kalman filter recursively processes new measurements periodically, balancing the need for current information with computational efficiency. This periodic updating maintains reliability without requiring continuous processing
Solution Approach 2:
The patent applies preliminary action by pre-calculating model parameters offline or during idle periods. Battery characteristics, motor efficiency maps, and road gradient data are pre-processed and stored, so that during actual driving the system only needs to perform lightweight real-time calculations based on current sensor inputs, reducing processing time while maintaining reliability
3Reliability
If drivers reserve a buffer of battery capacity due to range anxiety, then the reliability of reaching destination is improved, but the productivity and effective driving range are reduced
Solution Approach 1:
The patent changes the parameter of confidence level by providing probabilistic range estimates (e.g., 95% confidence intervals) instead of fixed buffer-based limits. The system dynamically adjusts the effective range based on actual driving conditions, weather forecasts, and route characteristics, allowing drivers to maximize range while maintaining acceptable reliability without arbitrary capacity buffers
Solution Approach 2:
The patent introduces an intermediary information layer between the battery management system and the driver. Instead of directly limiting available capacity, the system provides enhanced range information that accounts for actual consumption patterns and conditions, mediating between the physical battery state and driver decision-making to optimize both reliability and productivity
4Measurement precision
If simple current integration methods are used to estimate battery capacity, then the measurement system is simple, but the measurement precision deteriorates due to self-discharge and measurement errors
Solution Approach 1:
The patent introduces an intermediary estimation layer between raw current measurements and final capacity calculation. The Kalman filter acts as a mediator that processes current draw measurements along with battery voltage, temperature, and historical data to estimate actual capacity consumption, compensating for self-discharge and measurement errors without requiring additional sensors
Solution Approach 2:
The patent replaces direct electrical measurement (current integration) with a computational estimation approach. Instead of relying on ampere-hour integration which accumulates errors, the system uses software-based state estimation that continuously corrects capacity calculations based on voltage characteristics and environmental factors, substituting computational complexity for measurement complexity
Data Source
AI summary
Methods and systems for estimating driving range for an electric vehicle. Each of Kalman Filter-based and receding horizon-based approaches to estimating driving range and providing driving range data to a driver of a vehicle are illustrated. Approaches configured for use when a destination is not known as well as when a destination is known are illustrated.