Traction Battery Energy Estimation Using Age-Dependent SoC Limits
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Solution Overview
Problem
Existing methods for estimating the amount of electrical energy stored in a vehicle's traction battery often underestimate it for new vehicles and overestimate it for older vehicles, leading to inaccurate range calculations due to age-independent lower state-of-charge limits.
Innovation Solution
A computer-implemented method that determines the age-dependent lower state-of-charge limit by analyzing historical battery data, using a simple linear correlation between the current maximum battery capacity and its original capacity, allowing for precise estimation of storable energy through a computing unit with minimal processing power and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If an age-independent lower state-of-charge limit is used, then the calculation method is simple, but the estimation accuracy deteriorates (underestimation for new vehicles, overestimation for older vehicles)
Solution Approach 1:
The lower state-of-charge limit is made dynamic by making it dependent on the battery's state of health (SOH). The computing unit determines the SOH based on the ratio of current maximum battery capacity to original maximum battery capacity, and then sets the lower state-of-charge limit accordingly (e.g., 0.20 for new batteries with SOH ≥ 80%, 0.15 for older batteries with SOH < 80%). This dynamic adjustment resolves the contradiction by adapting the limit to battery age while maintaining calculation simplicity.
2Measurement precision
If the lower state-of-charge limit is adjusted according to battery aging, then the energy estimation accuracy improves, but the device complexity increases due to higher computing power and memory requirements
Solution Approach 1:
The invention changes the parameter being monitored from complex multi-dimensional battery state analysis to a simple ratio calculation: current maximum battery capacity divided by original maximum battery capacity. This single parameter (SOH ratio) is then used to select from predefined lower state-of-charge limit values. This approach achieves accurate age-dependent energy estimation while keeping the computing requirements minimal, as it only requires storing and comparing a few threshold values rather than performing complex real-time analysis.
3Measurement precision
If extensive monitoring and complex calculation methods are used, then the energy estimation accuracy improves, but the manufacturing costs increase due to more complex control units
Solution Approach 1:
The control unit uses existing battery capacity data that is already available from normal battery management operations. By calculating the ratio of current to original maximum capacity and using this to adjust the lower state-of-charge limit, the system makes the existing data serve a dual purpose: both battery management and accurate energy estimation. This eliminates the need for additional sensors or complex external systems, thereby reducing manufacturing costs while improving estimation accuracy.
Data Source
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AI summary
The invention relates to a computer-implemented method for estimating the amount of electrical energy (1) that can be stored by a vehicle's traction battery, wherein a vehicle-internal computing unit for determining the amount of electrical energy (1) multiplies a battery capacity (2) of the traction battery with the integral (3) of the battery voltage (4) of the traction battery from a lower state of charge limit (5) to an upper state of charge limit (6), wherein a value dependent on the aging of the traction battery is assumed for the lower state of charge limit (5).The computer-implemented method according to the invention is characterized in that the computing unit determines the lower state of charge limit (5) by a linear correlation to the ratio (7) of the current maximum battery capacity to the original maximum battery capacity of the traction battery, taking into account at least one parameter, wherein the aging-dependent level of the at least one parameter (8.1, 8.2) is determined by analysis of historical battery data from charging and/or discharging processes of identical traction batteries.