A battery internal temperature information processing method, a computer device, and a storage medium that first acquire off-line testing data for off-line testing a battery module and construct an equivalent thermal network model from the off-line testing data, determine optimal model parameters of the equivalent thermal network model based on a multi-objective function fitting method; thereafter, a first battery internal temperature estimate of the battery of the vehicle at a first moment in actual operation of the vehicle is determined, in turn, based on the acquired initial state vector values of the battery of the vehicle, first operational data at a first moment in actual operation of the vehicle, and an equivalent thermal network model including the optimal model parameters.

Disclosed is an apparatus and method for updating a current pattern for rapid charging, and a computer program stored in a storage medium performing the method, the apparatus includes a resistance calculation unit for calculating an internal resistance of a battery module, a storage unit for storing a current pattern for rapid charging of the battery module, and a calculation unit for updating the current pattern according to a state of the internal resistance of the battery module, and the calculation unit calculates a resistance increase rate based on the calculated internal resistance, calculates an adjustment coefficient based on the calculated resistance increase rate, and updates the current pattern using the calculated adjustment coefficient and the current pattern so that when performing rapid charging, an impact on the battery module life is minimized.

Disclosed is an apparatus and method for updating a current pattern for rapid charging, and a computer program stored in a storage medium performing the method, the apparatus includes a resistance calculation unit for calculating an internal resistance of a battery module, a storage unit for storing a current pattern for rapid charging of the battery module, and a calculation unit for updating the current pattern according to a state of the internal resistance of the battery module, and the calculation unit calculates a resistance increase rate based on the calculated internal resistance, calculates an adjustment coefficient based on the calculated resistance increase rate, and updates the current pattern using the calculated adjustment coefficient and the current pattern so that when performing rapid charging, an impact on the battery module life is minimized.

A vehicle electricity storage system includes a control device including: a calculation unit configured to calculate an internal resistance value of the battery based on the voltage value and the current value; a derivation unit configured to derive an estimated internal resistance value, which is an estimated value of the current internal resistance of the battery, based on the calculated internal resistance value calculated and the current voltage value; and a setting unit configured to set a charging electricity upper limit value, which is an upper limit value of charging electricity for charging the battery, based on the derived estimated internal resistance value, the current voltage value, and a current value at present. The derivation unit is configured to derive a greater value as the estimated internal resistance value, as a difference between an upper limit voltage value of the battery and the current voltage value becomes smaller.

A vehicle electricity storage system includes a control device including: a calculation unit configured to calculate an internal resistance value of the battery based on the voltage value and the current value; a derivation unit configured to derive an estimated internal resistance value, which is an estimated value of the current internal resistance of the battery, based on the calculated internal resistance value calculated and the current voltage value; and a setting unit configured to set a charging electricity upper limit value, which is an upper limit value of charging electricity for charging the battery, based on the derived estimated internal resistance value, the current voltage value, and a current value at present. The derivation unit is configured to derive a greater value as the estimated internal resistance value, as a difference between an upper limit voltage value of the battery and the current voltage value becomes smaller.

A battery capacity display method and device, electronic equipment and a computer-readable storage medium are disclosed. The method comprises: judging whether a battery to be detected can be fully charged when the battery to be detected is in a constant voltage charging state (S101); if not, determining a current capacity state of the battery to be detected and monitoring a time duration of the battery to be detected under the current capacity state (S102); judging whether the time duration is greater than a corresponding preset overtime threshold (S103); and if the time duration is greater than the preset overtime threshold, displaying full capacity via a UI interface (S104).

A battery capacity display method and device, electronic equipment and a computer-readable storage medium are disclosed. The method comprises: judging whether a battery to be detected can be fully charged when the battery to be detected is in a constant voltage charging state (S101); if not, determining a current capacity state of the battery to be detected and monitoring a time duration of the battery to be detected under the current capacity state (S102); judging whether the time duration is greater than a corresponding preset overtime threshold (S103); and if the time duration is greater than the preset overtime threshold, displaying full capacity via a UI interface (S104).

A battery model construction method includes: a step ST2 for constructing a battery model; steps ST3 and ST4 for evaluating, for each sample battery, the prediction error between a measured value of the SOH and a predicted value according to the battery model, and determining whether there is inherent bias in the prediction error for each sample battery; steps ST5 and ST6 for constructing a first error prediction model associating explanatory variables defined on the basis of usage history parameters with an objective variable, and determining whether a first correlation exists between the measured value of the average prediction error acquired in steps ST3 and ST4 and the predicted value according to the first error prediction model; and a step ST7 for reconstructing the battery model in the case where it is determined that there is bias and that the first correlation exists in steps ST5 and ST6.

A battery charging method based on lithium plating detection includes: acquiring a battery charging strategy table after receiving a battery charging instruction; charging a battery according to a charging current in the battery charging strategy table, and performing at least one charging lithium plating detection on the battery during the charging of the battery, to obtain a first lithium plating detection result; continuing the charging of the battery according to the charging current and the charging lithium plating detection of the battery when no lithium plating phenomenon occurs, and stopping the charging lithium plating detection when the lithium plating phenomenon occurs or the charging of the battery is completed; and updating the charging current according to a preset first current reduction strategy when the lithium plating phenomenon occurs, and continuing the charging of the battery according to the updated charging current until the charging is completed.

An apparatus and a method for processing battery cell voltage data, which calculate a moving average by assigning a weight to one or more voltage data acquired from the battery cell and reflect the acquired voltage data to the calculated moving average and use the voltage data to rapidly follow a sudden change of the voltage data applied from the battery cell.