Battery SOC Estimation Using Kalman Filtering and Voltage Mapping
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Solution Overview
Problem
Existing methods for estimating the State of Charge (SOC) of lithium batteries in electric vehicles are inaccurate due to their complex nonlinear nature, lack of real-time prediction capabilities, and failure to account for temperature and non-Gaussian white noises, leading to inefficiencies in energy management and potential power failures.
Innovation Solution
A Kalman filter-based method that extracts cell voltage to calculate terminal voltage, matches it with a lookup table to obtain initial SOC, calculates initial capacity and state values, and updates SOC estimates using Kalman gain, integrating temperature effects and adaptive adjustments to ensure accuracy across varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing SOC estimation methods are used, then the system can operate with simple algorithms, but the SOC estimation accuracy deteriorates due to battery nonlinear characteristics and environmental variations
Solution Approach 1:
The patent introduces an intermediary mapping relationship between terminal voltage and SOC through lookup tables. Instead of directly estimating SOC from complex battery states, the system uses terminal voltage as an intermediary parameter that can be accurately measured and mapped to SOC values, thereby improving estimation accuracy without requiring complex algorithms
Solution Approach 2:
The patent transforms the SOC estimation problem from directly estimating charge state to first measuring terminal voltage and then mapping it to SOC. This parameter transformation approach converts a difficult estimation problem into a measurable voltage measurement followed by a lookup operation, achieving high accuracy while maintaining algorithm simplicity
2Productivity
If real-time SOC estimation is implemented, then energy management can be improved, but the system cannot accurately predict SOC under non-Gaussian white noises
Solution Approach 1:
The patent extracts the terminal voltage parameter from the complex battery system and uses it as the primary basis for SOC estimation. By focusing on this extractable, easily measurable parameter and its established relationship with SOC, the system achieves real-time estimation capability that is robust to non-Gaussian white noises without requiring complex noise filtering algorithms
3Adaptability or versatility
If cloud-based parameter upload is used, then online parameter estimation can be performed, but the solution becomes difficult to apply to certain electric vehicles
Solution Approach 1:
The patent enables the battery management system to self-determine SOC using only terminal voltage measurements and pre-stored lookup tables that exist locally in the controller. This self-service approach eliminates the need for cloud connectivity and external parameter uploads, making the system universally applicable to all electric vehicles with standard battery management hardware
Solution Approach 2:
The patent performs preliminary action by pre-establishing the voltage-SOC mapping relationships and storing them in lookup tables during system initialization or manufacturing. This preliminary preparation allows the system to immediately perform accurate SOC estimation upon deployment without requiring ongoing cloud-based parameter updates or vehicle-specific configurations
4Measurement precision
If temperature effects are not considered, then the estimation algorithm remains simple, but the SOC estimation accuracy deteriorates at high and low temperatures
Solution Approach 1:
The patent merges the temperature compensation function into the terminal voltage-SOC mapping relationship. Instead of adding separate temperature correction algorithms, the system combines temperature effects into the lookup table construction, where voltage-SOC mappings are established under different temperature conditions. This merging approach maintains algorithm simplicity while achieving accurate temperature-compensated SOC estimation
Data Source
AI summary
The invention provides a Kalman filter-based SOC estimation method, system and medium, and an electronic device therewith. The method includes extracting a cell voltage of a battery pack to calculate a terminal voltage, and matching the terminal voltage in a preset lookup table to obtain an initial value of the SOC; calculating an initial capacity of the battery pack based on the initial value of the SOC, and calculating a state value of the SOC and an observed value of the SOC in an interval period based on the initial capacity; calculating a Kalman gain based on the state value of the SOC and the observed value of the SOC, and updating an estimated value of the SOC based on the Kalman gain.