Traction Battery Cell Balancing via Statistical Skewness Analysis
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Solution Overview
Problem
Traction batteries in electrified vehicles face inefficiencies in cell balancing due to variations in cell capacities, leading to energy wastage and uneven state of charge distribution, which existing methods fail to address effectively.
Innovation Solution
A controller estimates cell capacities, calculates standard deviation and skewness, and commands the balance circuit to discharge cells based on these statistical metrics to achieve uniform state of charge, optimizing when balancing occurs to minimize energy loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If cell balancing is performed using traditional methods, then all cells are discharged to achieve uniform state of charge, but this causes energy wastage and does not optimize balancing timing
Solution Approach 1:
The controller performs preliminary statistical analysis of cell capacity distributions (calculating standard deviation and skewness) before initiating balancing operations. This preliminary assessment allows the system to predict which cells will require balancing and determine optimal timing, thereby reducing unnecessary energy discharge and wastage while maintaining operational simplicity through automated decision-making
Solution Approach 2:
The system changes the approach from uniform cell discharge to selective cell discharge based on statistical parameters (standard deviation and skewness of capacity distributions). By monitoring these parameters and comparing them against thresholds, the controller dynamically adjusts balancing operations to discharge only necessary cells at optimal times, minimizing energy loss while preserving operational ease
2Loss of energy
If statistical analysis is implemented to optimize balancing timing, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The controller performs self-service by automatically conducting statistical analysis of cell capacity distributions and making autonomous decisions about balancing operations. The system monitors its own battery pack's capacity variations, calculates standard deviation and skewness, and determines optimal discharge timing without external intervention, thereby reducing energy wastage while managing complexity through self-contained decision-making logic
3Measurement precision
If selective cell discharge is commanded based on skewness and SOC thresholds, then balancing precision improves, but control complexity increases
Solution Approach 1:
The control system uses parameter changes by monitoring statistical metrics (standard deviation and skewness of capacity distributions) and comparing them against predetermined thresholds. When skewness exceeds a threshold and SOC is below a threshold, the controller commands selective discharge of specific cells. This parameter-based control approach achieves high balancing precision by targeting only cells that statistically require it, while managing control complexity through systematic threshold-based decision rules
Data Source
AI summary
A vehicle includes a traction battery having a plurality of battery cells and a balance circuit configured to selectively discharge one or more of the battery cells. A controller is programmed to estimate charge capacities of the battery cells and determine a standard deviation of the estimated capacities. The controller is further programmed to determine a skewness of the estimated capacities. The controller is also programmed to, in response to the skewness exceeding a first threshold, command the balance circuit to discharge one or more of the battery cells when a state of charge (SOC) of the traction battery is less than a second threshold, and, in response to the skewness being less than the first threshold, command the balance circuit to discharge one or more of the battery cells when the SOC of the traction battery is greater than the second threshold.