Battery Model Parameter Tuning with Charging Pulse Feedback
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Solution Overview
Problem
Existing electrochemical battery models are insufficiently parameterized to accurately capture dynamic behavior, particularly during high-dynamic load changes, leading to inaccuracies in aging state determination and battery performance monitoring.
Innovation Solution
A method to adjust model parameters of an electrochemical battery model by generating semi-dynamic operating states during charging processes, using current or voltage pulses, and capturing resulting operating variable profiles to refine parameterization, especially focusing on temperature-dependent diffusion parameters and other dynamic model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive laboratory measurements are used to parameterize the battery model initially, then the model can be established for a battery type, but it is not possible to capture all dynamic dependencies with sufficient granularity, particularly high-dynamic load changes
Solution Approach 1:
The patent applies preliminary action by performing initial laboratory measurements to establish baseline model parameters before actual battery operation. This preliminary parameterization provides a starting point that can be later refined using operational data, allowing the model to progressively improve its accuracy for dynamic conditions without requiring complete initial coverage of all possible operating scenarios
Solution Approach 2:
The patent implements feedback by continuously comparing modeled battery states with actual measured operating variables during battery operation. When deviations are detected, the model parameters are adjusted based on the operational data, creating a closed-loop system that progressively refines the model's ability to capture dynamic dependencies and high-dynamic load changes
2Device complexity
If the electrochemical battery model is implemented in the central unit, then computational load in internal devices is reduced, but the model parameters remain insufficiently adjusted for individual battery dynamics
Solution Approach 1:
The patent applies self-service by enabling each battery to contribute its own operational data to refine its model parameters. The system automatically collects operating variables from each battery, compares them with model predictions, and adjusts parameters specific to each battery's individual characteristics, eliminating the need for manual re-parameterization while maintaining high individual accuracy
Solution Approach 2:
The patent implements feedback at the individual battery level by continuously monitoring operating variables and using deviations between modeled and actual behavior to adjust model parameters. This feedback mechanism ensures that each battery's model remains accurately tuned to its specific dynamics while the computational workload remains distributed to the central unit
3Measurement precision
If operating variable data are sampled at high temporal resolution to determine aging states, then accurate aging determination is achieved, but data transmission and processing requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for aging determination by using the electrochemical battery model to process high-resolution operational data locally. Instead of transmitting all raw high-resolution data, the model processes the data and extracts key features and derived parameters that capture the essential aging information, significantly reducing data transmission requirements while maintaining accuracy
Solution Approach 2:
The patent creates a digital representation (digital twin) of the battery's aging state through the electrochemical model. This virtual copy allows the system to work with compressed, model-derived data rather than raw measurements, enabling accurate aging monitoring with reduced data transmission and processing requirements
Data Source
AI summary
A method for adjusting a parameter of a model of a battery, including providing a temporal operating variable profile of a plurality of operating variables for a time period, and modelling a profile of one of the operating variables using the model based on an operating variable of the provided operating variable profile within the time period. The method includes determining an operating state of the battery when the modeled operating variable deviates from the corresponding operating variable by more than a threshold value during the time period. The method includes performing a charging process, wherein, in the presence of the operating state, a current pulse or a voltage pulse of a specified height and a specified duration is superimposed on a charging current or a charging voltage while a further temporal operating variable profile is captured and adjusting the parameter based on the further temporal operating variable profile.