Battery Capacity Estimation Using Time-Temperature Constraints
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Solution Overview
Problem
Existing hybrid electric vehicles and plug-in hybrid electric vehicles face challenges in accurately estimating battery capacity due to limited usage of battery capacity and significant battery capacity fade, which affects state of charge calculations and fuel efficiency.
Innovation Solution
A controller is configured to estimate battery capacity using multiple state of charge estimations based on time and temperature constraints, allowing for independent calculation of state of charge parameters and incorporating uncertainty analysis to determine confident capacity updates, thereby enabling accurate battery capacity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If battery capacity is assumed constant in hybrid electric vehicles, then calculation simplicity is improved, but measurement precision deteriorates due to battery capacity fade
Solution Approach 1:
The system continuously monitors battery behavior data including voltage, current, temperature, and state of charge to dynamically update capacity estimates. This feedback mechanism allows the system to adapt to battery capacity fade over time while maintaining accurate SOC calculations without requiring complex manual recalibration
Solution Approach 2:
The battery management system automatically performs capacity estimation using onboard sensors and processors. The system self-calibrates by analyzing discharge/charge cycles and voltage relaxation patterns, eliminating the need for external testing equipment or manual capacity assessments
2Measurement precision
If multiple state of charge estimation methods are used independently, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system combines multiple independent SOC estimation approaches (amp-hour integration, open circuit voltage method, and electrochemical impedance spectroscopy) into a unified capacity estimation framework. By merging these methods, the system leverages their complementary strengths to achieve higher accuracy while the integrated architecture prevents exponential complexity growth
3Measurement precision
If battery capacity estimation is performed continuously, then measurement precision is improved, but loss of energy increases due to processor usage
Solution Approach 1:
The system performs comprehensive capacity estimation at periodic intervals based on drive cycles and battery usage patterns rather than continuously. During normal operation, simplified monitoring is used, while full estimation algorithms are executed at key events such as state of charge thresholds, temperature extremes, or scheduled maintenance intervals, significantly reducing processor energy consumption
4Ease of operation
If state of charge calculations depend on predetermined battery capacity, then ease of operation is improved, but reliability deteriorates due to capacity fade impact
Solution Approach 1:
The system transitions from static predetermined capacity values to dynamic capacity estimation that adapts to actual battery aging. The estimated capacity evolves continuously based on monitored battery behavior, allowing SOC calculations to remain simple while automatically compensating for capacity fade to maintain fuel efficiency accuracy
Data Source
AI summary
An electric vehicle includes a controller configured to estimate battery capacity in accordance with a first state of charge estimation, a charge integration, and a second state of charge estimation. The first and second state of charge estimations are in accordance with time and temperature constraints and are such that the estimated battery capacity has limited uncertainty. The controller is further configured to generate an output based on the estimated battery capacity.