A learning apparatus comprising: a characteristic acquisition unit for acquiring one of a voltage and a capacity of a storage battery and a characteristic corresponding to a capacity change and a voltage change of the storage battery under a plurality of conditions during at least one of charging and discharging of the storage battery; and a learning unit for learning a relationship between one of the voltage and the capacity of the storage battery and the characteristic under each condition of the plurality of conditions to generate an estimation model for estimating at least one of a maximum capacity and a remaining capacity of the storage battery from one of the voltage and the capacity of the storage battery and the characteristic, characterized in that the characteristic is a differential capacity characteristic calculated by differentiating the capacity of the storage battery by the voltage or a differential voltage characteristic calculated by differentiating the voltage of the storage battery by the capacity, and the learning unit is configured to use a modular network Self Organizing Map, wherein a module is a neural network, to generate the estimation model, and the estimation model comprises a plurality of modules, and the learning unit is configured to learn each module using a neural net, where a voltage V of the storage battery is an input vector and a differential capacity characteristic dQ/dV of the storage battery is an output vector or where a capacity Q of the storage battery is an input vector and a differential voltage characteristic dV/dQ of the storage battery is an output vector, to generate the estimation model. a characteristic acquisition unit for acquiring one of a voltage and a capacity of a storage battery and a characteristic corresponding to a capacity change and a voltage change of the storage battery under a plurality of conditions during at least one of charging and discharging of the storage battery; and a learning unit for learning a relationship between one of the voltage and the capacity of the storage battery and the characteristic under each condition of the plurality of conditions to generate an estimation model for estimating at least one of a maximum capacity and a remaining capacity of the storage battery from one of the voltage and the capacity of the storage battery and the characteristic, characterized in that the characteristic is a differential capacity characteristic calculated by differentiating the capacity of the storage battery by the voltage or a differential voltage characteristic calculated by differentiating the voltage of the storage battery by the capacity, and the learning unit is configured to use a modular network Self Organizing Map, wherein a module is a neural network, to generate the estimation model, and the estimation model comprises a plurality of modules, and the learning unit is configured to learn each module using a neural net, where a voltage V of the storage battery is an input vector and a differential capacity characteristic dQ/dV of the storage battery is an output vector or where a capacity Q of the storage battery is an input vector and a differential voltage characteristic dV/dQ of the storage battery is an output vector, to generate the estimation model. <EOS>