Dynamic Battery State Estimation via Switching Periods
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery state estimation methods are inefficient in dynamically updating the order and grouping of batteries, leading to inaccuracies in state information determination and potential over-discharging or over-charging.
Innovation Solution
A processor-implemented method that determines state information by alternating target and non-target batteries within switching periods, updates relationships based on voltage and state information, and regroups batteries based on preset intervals, travel distance, or state changes, using electrochemical models for accurate SOC estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery state estimation uses fixed ordering and grouping methods, then device complexity is reduced, but measurement precision and reliability of state information determination deteriorate
Solution Approach 1:
The patent implements dynamic switching between different battery ordering methods (voltage-based, temperature-based, capacity-based) and grouping strategies based on real-time operating conditions. The system transitions from static to dynamic configuration, allowing the battery management system to adapt to changing conditions while maintaining estimation accuracy without requiring complete reconfiguration of the estimation architecture.
Solution Approach 2:
The system changes key parameters including ordering criteria (voltage, temperature, capacity), grouping configurations, and estimation weights based on operating conditions such as charge/discharge states, temperature ranges, and battery age. These parameter changes enable the system to maintain high measurement precision across different operating scenarios without increasing fundamental system complexity.
2Reliability
If battery ordering and grouping are updated frequently, then state information accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent implements periodic updating of battery ordering and grouping at predetermined time intervals or after a specified number of charge/discharge cycles, rather than continuous real-time updates. This periodic approach maintains state information reliability by ensuring regular refreshes of estimation data while significantly reducing computational burden and processing time compared to continuous updates.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor changes in battery parameters (voltage, temperature, capacity) and trigger ordering/grouping updates only when threshold changes are detected or when operating conditions warrant reevaluation. This feedback-driven approach maintains reliability by updating only when necessary, avoiding unnecessary processing during stable operating conditions.
3Measurement precision
If comprehensive battery monitoring of all cells is performed, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The patent divides the battery pack into multiple groups based on characteristics such as voltage levels, temperature zones, or capacity ranges. Instead of processing all batteries uniformly, the system segments them into manageable groups and applies targeted estimation methods to each group. This segmentation maintains comprehensive monitoring accuracy while reducing computational energy consumption by focusing processing resources on representative samples from each group rather than every individual cell.
Solution Approach 2:
The system uses representative batteries or virtual models to copy and represent the state of multiple similar batteries within a group. By estimating the state of a representative battery and applying that estimation to similar batteries in the same group, the system achieves comprehensive monitoring precision with reduced computational energy consumption, as full detailed analysis of every cell is replaced by efficient copying of representative state information.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A processor-implemented battery state estimation method includes determining state information of plural batteries in each of plural switching periods and outputting state information of the batteries in a last switching period of the switching periods. The determining includes determining, for each switching period, state information of a target battery based on a battery model and sensing data of the target battery, and state information of a non-target battery based on a state variation of the non-target battery. A target battery set in a first switching period of the plural switching periods is set to be a non-target battery in a second switching period of the plural switching periods, and a non-target battery in the first switching period is set to be a target battery in the second switching period of the plural switching periods.