Electric tricycle lithium battery state of charge dynamic calibration method and system

By collecting multi-source information in an electric tricycle and using parameter identification and Kalman filtering algorithms, the state of charge (SOC) of lithium batteries is dynamically calibrated, solving the problems of large errors and poor environmental adaptability in existing technologies. This achieves high-precision SOC calibration during dynamic operation, adapting to battery aging and environmental changes.

CN122307381APending Publication Date: 2026-06-30SUZHOU HUAYU INTELLIGENT TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SUZHOU HUAYU INTELLIGENT TECH CO LTD
Filing Date
2026-04-16
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

In existing technologies, the state of charge (SOC) calibration methods for lithium batteries in electric tricycles suffer from large errors and poor environmental adaptability, making it impossible to achieve accurate calibration during dynamic operation. In particular, under conditions of frequent start-stop, heavy/light load switching, high current discharge, and drastic temperature changes, the SOC estimation error is large, and the SOC drift problem during dynamic operation cannot be solved.

Method used

By collecting the total voltage, total current, and temperature of the lithium battery, and obtaining the speed, motor power, and load status of the electric tricycle, the triggering conditions of the dynamic calibration event are determined. The ohmic internal resistance and polarization internal resistance are identified using a parameter identification algorithm, the virtual open-circuit voltage is calculated, and the data is fused using the ampere-hour integration method and the Kalman filter algorithm to achieve dynamic calibration of the lithium battery's state of charge.

Benefits of technology

Under various dynamic operating conditions of electric tricycles, automatic and accurate lithium battery SOC calibration is achieved, which significantly improves the SOC estimation accuracy throughout the entire life cycle, reduces the absolute error, and adapts to battery aging through an adaptive learning mechanism to maintain high-precision calibration.

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Abstract

This application relates to a method and system for dynamic calibration of the state of charge (SOC) of lithium batteries for electric tricycles. The method includes: determining whether the current operating conditions meet the triggering conditions for a dynamic calibration event based on the total voltage, current, and temperature of the lithium battery, and the speed, motor power, and load status of the electric tricycle; when any dynamic calibration event is triggered, identifying the ohmic internal resistance and polarization internal resistance of the lithium battery using a parameter identification algorithm, calculating the virtual open-circuit voltage using a compensation calculation formula, querying a preset SOC table based on the virtual open-circuit voltage and temperature to obtain a reference SOC value, estimating the predicted SOC value using the ampere-hour integration method, and fusing the reference SOC value and the predicted SOC value using a Kalman filter algorithm to obtain the calibrated SOC value. This solves the problems of large errors, poor environmental adaptability, and inability to address SOC drift during dynamic operation in traditional battery SOC calibration methods, significantly improving the SOC estimation accuracy throughout the entire lifespan.
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