Hybrid Vehicle Battery SOC Estimation Using Dual Feedback Control
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Solution Overview
Problem
Existing methods for estimating the state of charge (SOC) and capacity of batteries in hybrid/electric vehicles face inaccuracies due to integration accumulation errors and initial SOC estimation challenges, especially when batteries operate within narrow charge/discharge ranges, leading to divergence from actual SOC values over time.
Innovation Solution
A dual feedback control system that combines a coulomb counting feedforward loop with online and pre-calibrated battery models to correct SOC estimation errors, using a first feedback loop based on an online battery model and a second feedback loop based on a pre-defined battery model that accounts for temperature and current variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If coulomb counting algorithm is used for SOC estimation, then real-time monitoring capability is improved, but integration accumulation errors cause estimation inaccuracy over time
Solution Approach 1:
The patent implements a dual feedback control system where the first feedback loop uses an online battery model to correct SOC estimation errors by comparing estimated voltage with measured voltage, and the second feedback loop uses a pre-calibrated battery model to further refine the estimation by comparing its estimated voltage with the online model's estimated voltage. This multi-layer feedback mechanism continuously corrects the accumulation errors from coulomb counting, maintaining high measurement precision while preserving real-time monitoring capability.
Solution Approach 2:
The patent combines multiple estimation approaches (coulomb counting, online battery model, and pre-calibrated battery model) into a composite estimation system. Each method compensates for the weaknesses of others: coulomb counting provides real-time response, online battery model corrects voltage deviations, and pre-calibrated model accounts for temperature and current variations. This composite approach achieves both real-time monitoring and high accuracy.
2Adaptability or versatility
If battery models account for temperature and current variations, then estimation accuracy under varying conditions is improved, but system complexity increases
Solution Approach 1:
The patent segments the battery model into two distinct components: an online battery model that operates in real-time with minimal computational burden, and a pre-calibrated battery model that is prepared offline and stores pre-computed parameters. This segmentation allows the system to handle temperature and current variations accurately while keeping the real-time computational complexity manageable, as the complex pre-calibration work is done beforehand.
Solution Approach 2:
The patent performs preliminary calibration of the battery model offline to generate pre-computed parameters that account for temperature and current variations. This pre-calibration process prepares lookup tables and parameter sets that can be quickly referenced during real-time operation, eliminating the need for complex real-time calculations while maintaining high adaptability to varying conditions.
3Measurement precision
If dual feedback control system is implemented, then SOC estimation precision is improved, but computational load and control system complexity increase
Solution Approach 1:
The dual feedback control system is segmented into two distinct loops with clearly defined functions: the first feedback loop handles immediate voltage correction using the online battery model, while the second feedback loop provides longer-term refinement using the pre-calibrated battery model. This segmentation allows each loop to be optimized independently, reducing the overall computational burden while maintaining high precision.
Solution Approach 2:
The patent uses the pre-calibrated battery model as a reference copy that stores pre-computed voltage characteristics under various temperature and current conditions. Instead of performing complex real-time calculations, the system copies relevant parameters from this pre-prepared model, significantly reducing computational load while maintaining estimation precision.
Data Source
AI summary
A vehicle includes a battery, an electric machine, and a controller. The battery has a state of charge. The electric machine is configured to draw electrical power from the battery to propel the vehicle in response to an acceleration request and to deliver electrical power to the battery to recharge the battery. The controller is programmed to adjust an estimation of battery state of charge based on a feed forward control that includes a coulomb counting algorithm, a first feedback control that includes a first battery model, and a second feedback control that includes a second battery model. The controller is further programmed to control the electrical power flow between the battery and the electric machine based on the estimation of the state of charge of the battery.