A system for estimating long term characteristics of a battery, comprising: a learning data input unit configured to receive initial characteristics learning data (P t) and long term characteristics learning data (T t) for a battery designated as a learning object, wherein the initial characteristics and the long term characteristics are parameters capable of defining an electrical or a physical charging or discharging characteristics of the battery, the initial characteristics learning data (P t) and the long term characteristics learning data (T t) being obtained at cycles conducted in an opening part and a latter part, respectively, of a cycle charging and discharging process, the cycle charging and discharging process means repeating a process of periodically charging and discharging the battery to a predetermined numbers of cycles, and one cycle refers to one charging and one discharging of the battery; a measurement data input unit configured to receive initial characteristics measurement data (P m) for a battery designated as an estimating object, the initial characteristics measurement data (P m) being obtained at cycles conducted in the opening part of the cycle charging and discharging process; and an artificial neural network operation unit configured to receive the initial characteristics learning data (P t) and the long term characteristics learning data (T t) from the learning data input unit, to allow an artificial neural network to learn a correlation of both learning data (P t, T t), to receive the initial characteristics measurement data (P m) from the measurement data input unit, to apply the learned artificial neural network thereto, thus calculating long term characteristics estimation data (T e) as estimated data predicted at cycles in the latter part of the cycle charging and discharging process from the initial characteristics measurement data (P m) of the battery and to output the long term characteristics estimation data (T e) a learning data input unit configured to receive initial characteristics learning data (P t) and long term characteristics learning data (T t) for a battery designated as a learning object, wherein the initial characteristics and the long term characteristics are parameters capable of defining an electrical or a physical charging or discharging characteristics of the battery, the initial characteristics learning data (P t) and the long term characteristics learning data (T t) being obtained at cycles conducted in an opening part and a latter part, respectively, of a cycle charging and discharging process, the cycle charging and discharging process means repeating a process of periodically charging and discharging the battery to a predetermined numbers of cycles, and one cycle refers to one charging and one discharging of the battery; a measurement data input unit configured to receive initial characteristics measurement data (P m) for a battery designated as an estimating object, the initial characteristics measurement data (P m) being obtained at cycles conducted in the opening part of the cycle charging and discharging process; and an artificial neural network operation unit configured to receive the initial characteristics learning data (P t) and the long term characteristics learning data (T t) from the learning data input unit, to allow an artificial neural network to learn a correlation of both learning data (P t, T t), to receive the initial characteristics measurement data (P m) from the measurement data input unit, to apply the learned artificial neural network thereto, thus calculating long term characteristics estimation data (T e) as estimated data predicted at cycles in the latter part of the cycle charging and discharging process from the initial characteristics measurement data (P m) of the battery and to output the long term characteristics estimation data (T e)