Abstract: An aspect of the present invention is a model evaluation device including an acquisition part configured to acquire updated second auxiliary filter information in which first auxiliary filter information and second auxiliary filter information are updated through learning, the first auxiliary filter information being generated by a first processing based on data for generation which was used in generation of a mathematical model which predicts degradation of an analysis target, the second auxiliary filter information indicating a regulation of estimating a reliability of a prediction result by the mathematical model while using the first auxiliary filter information, and an evaluation part configured to evaluate an accuracy of prediction by the mathematical model while using the second auxiliary filter information in a case input-scheduled data which is scheduled to be input to the mathematical model is actually input to the mathematical model.

Abstract: A battery state diagnosing device includes: an acquisition unit configured to acquire time-series data including at least a current value and a voltage value of a battery; a deterioration state estimating unit configured to estimate an index value associated with a deterioration state of the battery on the basis of the time-series data; a reliability evaluating unit configured to evaluate a reliability of the index value on the basis of at least one of distribution information of the voltage value included in the time-series data and distribution information of a dischargeable capacity calculated using the current value included in the time-series data; and a deterioration state determining unit configured to determine a final index value on the basis of the reliability.

Abstract: Provided is a battery degradation estimation device including a storage medium that stores computer-readable instructions, and a processor coupled to the storage medium, the processor executing the computer-readable instructions to: acquire time-series data including at least a voltage, SOC (State Of Charge), temperature, and current of a battery; convert the time-series data into intermediate data to be used for training; predict a future value of the intermediate data and a future value of an indicator relating to a degradation state of the battery by using a prediction function to generate training data; train a prediction model for estimating the indicator relating to the degradation state of the battery on the basis of the training data; and estimate the indicator relating to the degradation state of the battery on the basis of the prediction model.

Abstract: A battery characteristic estimation device including a storage medium that stores computer-readable instructions, and a processor coupled to the storage medium, the processor executing the computer-readable instructions to: acquire time-series data of at least a voltage and current of a secondary battery having a positive electrode containing a positive electrode active material and a negative electrode containing a negative electrode active material; store a reference positive electrode OCP curve, and a reference negative electrode OCP curve; convert the reference positive electrode OCP curve into a positive electrode OCP curve in accordance with a first parameter group, convert the reference negative electrode OCP curve into a negative electrode OCP curve in accordance with a second parameter group, and estimate an OCV curve on the basis of a difference between the positive electrode OCP curve and the negative electrode OCP curve; and optimize the first parameter group and the second parameter group.

Abstract: A battery model construction method is a method for constructing a battery model that treats powers of a plurality of usage history parameters defined on a basis of time series data about a current, a voltage, and a temperature of a battery as explanatory variables and treats a predicted SOH value as an objective variable, the method including: an acquiring step ST1 for acquiring time series data of usage history parameters and measured SOH values; an exponentiating step ST2 for raising the usage history parameters by a prescribed exponent to thereby generate time series data of input parameters; a training step ST4 for training the battery model by using the time series data of the input parameters and the measured SOH values as training data; and a searching step ST8 for searching for an optimal exponent by repeatedly performing the steps ST2 and ST4 while varying the exponent.

Abstract: 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.

Abstract: A battery state determination system is a battery state determination system including an in-vehicle device and a server device, the in-vehicle device includes an acquisition part acquires physical quantity data showing a physical quantity related to a state of a battery mounted in a vehicle, an observation part that observes a feature related to a state change of the battery on the basis of the physical quantity data at a plurality of different observation times, and a transmission part that transmits a plurality of pieces of feature data which expresses the feature observed each observation time to the server device, and the server device includes a reception part that receives the plurality of pieces of feature data transmitted from the in-vehicle device, and a diagnosis part that diagnoses a deterioration state of the battery on the basis of the plurality of pieces of feature data.

Abstract: A battery state determination system includes a first in-vehicle device and a server device, the first in-vehicle device includes a first acquisition part that acquires physical quantity data related to a state of a battery, a first diagnostic part that performs deterioration diagnosis on the battery at a first operation cycle on the basis of the physical quantity data, and a first transmission part that transmits the physical quantity data and the diagnostic results of deterioration diagnosis to the server device, and the server device includes a reception part that receives the physical quantity data or the diagnostic result of the deterioration diagnosis from the first in-vehicle device, and a server-side processing part that performs a processing other than the deterioration diagnosis at an operation cycle which is longer than the first operation cycle on the basis of the diagnostic result of the deterioration diagnosis and/or the physical quantity data.

Abstract: A secondary battery includes a positive electrode, a negative electrode, and an electrolytic solution. The positive electrode includes a positive electrode active material layer including a lithium-cobalt composite oxide. The lithium-cobalt composite oxide has a layered rock-salt crystal structure. The negative electrode includes a negative electrode active material layer including graphite. When the secondary battery is charged and discharged with an upper limit of a closed circuit voltage being set to 4.42 V or higher, a unit capacity (mAh/g) satisfies a condition represented by 168 mAh/g?unit capacity (mAh/g)?(?0.28×area rate (%)+178) mAh/g.

Abstract: A battery state determination system is a battery state determination system including an in-vehicle device and a server device, the in-vehicle device includes an acquisition part acquires physical quantity data showing a physical quantity related to a state of a battery mounted in a vehicle, an observation part that observes a feature related to a state change of the battery on the basis of the physical quantity data at a plurality of different observation times, and a transmission part that transmits a plurality of pieces of feature data which expresses the feature observed each observation time to the server device, and the server device includes a reception part that receives the plurality of pieces of feature data transmitted from the in-vehicle device, and a diagnosis part that diagnoses a deterioration state of the battery on the basis of the plurality of pieces of feature data.

Abstract: A battery state determination system includes a first in-vehicle device and a server device, the first in-vehicle device includes a first acquisition part that acquires physical quantity data related to a state of a battery, a first diagnostic part that performs deterioration diagnosis on the battery at a first operation cycle on the basis of the physical quantity data, and a first transmission part that transmits the physical quantity data and the diagnostic results of deterioration diagnosis to the server device, and the server device includes a reception part that receives the physical quantity data or the diagnostic result of the deterioration diagnosis from the first in-vehicle device, and a server-side processing part that performs a processing other than the deterioration diagnosis at an operation cycle which is longer than the first operation cycle on the basis of the diagnostic result of the deterioration diagnosis and/or the physical quantity data.

Abstract: A charging method includes: in a case of charging a lithium ion secondary battery having a positive electrode, a negative electrode, and an electrolyte housed in an exterior body, calculating a value obtained by second-order differentiation of a charging current in constant-voltage charging with respect to a charging electric quantity or time during charging; and, during charging with an environmental temperature of T1 immediately before start of charging, in a case where a change in a sign of the value calculated by second-order differentiation from positive to negative or from negative to positive is observed, if an ambient temperature immediately before start of next and subsequent charging is T2 or lower, making the whole or a part of a current in a constant-current charging current section lower than an initial setting.