Battery SOH Prediction Using Vehicle Behavior Features
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Solution Overview
Problem
Current methods for predicting the state of health (SOH) of electric vehicle batteries are inaccurate due to their reliance solely on battery feature parameters and neural network models, neglecting user driving behavior and vehicle usage habits.
Innovation Solution
A method and device that extract vehicle-using behavior features from battery data, predict these features for the next time step using a linear fitting model, and integrate them with a battery health state prediction model to determine the battery's health degree, incorporating user behavior and battery performance features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only battery feature parameters and neural network models are used for SOH prediction, then the prediction method is simple, but the prediction accuracy is low
Solution Approach 1:
The patent merges multiple data sources including battery feature parameters, user driving behavior features, and vehicle usage habit features into a unified prediction framework. This combination of diverse features through the prediction model resolves the contradiction by achieving higher accuracy without excessive complexity, as the system integrates available data streams in a coordinated manner.
Solution Approach 2:
The prediction model is designed to handle multiple types of input data (battery parameters, user behavior, vehicle habits) universally, allowing the same model structure to process diverse features. This multi-functionality approach improves accuracy by considering comprehensive factors while maintaining a single versatile prediction system rather than multiple separate models.
2Measurement precision
If only battery feature parameters are considered, then the data processing is simple, but the prediction comprehensiveness is insufficient
Solution Approach 1:
The system performs preliminary extraction and processing of user driving behavior features and vehicle usage habit features before feeding them into the prediction model. This advance preparation of additional feature data ensures that comprehensive information is available for accurate SOH prediction without losing valuable user behavior insights during the prediction process.
3Productivity
If real-time SOH assessment is implemented, then the energy management optimization is improved, but the computational load increases
Solution Approach 1:
The system changes the parameters being monitored and predicted to focus on the most critical features for SOH assessment. By identifying and prioritizing key battery parameters, user behavior indicators, and vehicle habit features that have the greatest impact on SOH, the system achieves real-time assessment capability with reduced computational energy consumption compared to analyzing all possible parameters.
Data Source
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AI summary
Disclosed are a method and a device for predicting state of health of a battery, an electronic equipment and a readable storage medium. The method includes: (S10), obtaining battery data of a vehicle; (S20), performing feature extraction on the battery data to obtain a vehicle-using behavior feature corresponding to the vehicle; (S30), predicting and obtaining a predicted vehicle-using behavior feature of the vehicle in a next time step according to the vehicle-using behavior feature; and (S40), predicting and obtaining a health degree of the battery in the vehicle according to the predicted vehicle-using behavior feature and a battery health state prediction model.