Battery capacity estimation method, computer readable medium and vehicle

Abstract

The invention relates to a battery capacity estimation method, which comprises the following steps of: acquiring charging capacity and voltage of a battery in real time, and deriving the voltage through the charging capacity to obtain a real-time charging capacity change rate; determining a target IC peak point with the highest IC value in the charging process; calculating a half-peak area corresponding to the target IC peak point, wherein the half-peak area is a capacity increment in an interval from a voltage corresponding to the target IC peak point to a predetermined voltage increment; taking the half-peak area as a health factor; and correcting the health factor based on the temperature and the multiplying power according to the reference mapping relationship between the health factorand the capacity, and estimating the battery capacity. According to the invention, the half-peak area is used as a health factor, and the storage and calculation costs are reduced. Especially, the online acquisition of the health factor is realized by using a recursive updating method, and the storage and calculation costs of the system are reduced. In addition, the method can be applied to multi-system power batteries, especially batteries with unobvious OCV curves along with aging.

Description

Technical field: [0001] The invention relates to battery management technology, in particular to a method for estimating battery capacity. Background technique: [0002] Battery capacity is one of the important evaluation indicators of battery health status. The existing battery capacity estimation methods are mainly divided into three categories: [0003] The first is the definition method. This method uses the inverse formula of the battery state of charge (SOC) definition (SOC=remaining power / capacity*100%) to calculate, that is, capacity=charged (discharged) power / SOC variation. Although this method is simple and feasible, it can realize online capacity estimation, but when the SOC estimation error increases, the capacity estimation error will also increase accordingly. [0004] The second is the model method. This method jointly estimates SOC and capacity based on the battery model. Online joint estimation of SOC and capacity. Compared with the definition method, t...

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Problems solved by technology

However, the existing geometric method is limited by the following reasons and it is difficult to realize the online estimation of capacity: 1) The shape of the curve is greatly affected by temperature and current rate, and the existing methods usually require fixed temperature and fixed small current charging and discharging conditions to obtain IC or DV curves, and the environmental conditions of vehicles are complex, and the temperature and charging current rate are uncertain; 2) The health factor is difficult to obtain online, the peak height usually fluctuates greatly, and the reliability is poor. Intervals, etc. require the storage of a large amount of curve history data, which is difficult for embedded systems

For example, in the prior art WO2019018974A1, it is necessary to store the generated IC curve and then calculate the health factor. The required storage and calculation costs are high and the applicability of working conditions is poor, which is difficult for online application in battery systems.

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