Method for estimating charge state of power cell

Abstract

The invention provides a method for estimating a charge state of a power battery with relatively less errors, which includes the following steps: through fitting of measured data, a Ki curve of correction coefficient of charging-discharging efficiency at different rates, a Kw curve of the correction coefficient of the charging-discharging efficiency under different temperature as well as a Kc curve of the correction coefficient of actual electric quantity are obtained and a Kc value can be obtained through a historical record; according to the state of the power, the following formulas are applied: in the working state, Ct = (KCC0 + sigma KiIiti) Kw is applied; in the non-working state and when a self-recovery effect is completed, C = KWKCC0t is applied; in the non-working state and when the self-recovery effect is not completed, Ct = Cm is applied; Ki, Kw, Kc are called from the curves to obtain remaining capacity Ct at the moment of obtaining the power battery so as to complete the estimation of a SOC battery. The invention with easy industrial application of models is applicable to both working state and non-working state of the battery; the errors of the estimation result are relatively less and the relevant curves are established.

Description

technical field [0001] The invention relates to the field of power management systems for power batteries, in particular to a method for estimating the state of charge of a power battery. Background technique [0002] The estimation of the SOC-State of Charge of the power battery is a very important content in the power management system. At present, there are three commonly used power battery SOC estimation methods as follows: [0003] First, the ampere-hour measurement method, which adds the integral value of the charging and discharging current over time to the initial SOC to estimate the real-time SOC. This method has the following two defects: on the one hand, since the power battery may start working at any SOC, it is difficult to determine the initial SOC; Discharge, and in the case of different rate current discharge, especially in the case of high rate current discharge, the discharge efficiency of the power battery has a large change (such as figure 2 As shown),...

AI Technical Summary

Problems solved by technology

This method has the following two defects: on the one hand, since the power battery may start working at any SOC, it is difficult to determine the initial SOC; Discharge, and in the case of different rate current discharge, especially in the case of high rate current discharge, the discharge efficiency of the power battery has a large change (such as figure 2 As shown), at this time, only the direct integration of the charge and discharge current over time without correction of the integration process cannot reflect the impact of the discharge efficiency change on the real-time SOC, so the estimated SOC has a large error

This method is also not suitable for SOC estimation of power batteries. On the one hand, it is difficult to determine the initial SOC; The rate of current discharge, so the neural network method does not meet the actual use

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