Automatic method of estimation of the capacitance of a cell of a battery, this method involving: a) the acquisition, at a time k, of a measured charging or discharging current intensity ik of the cell, b) the estimation of the capacitance C n,k3 of the cell at a time k3 by using a Kalman filter, this estimation involving: • the calculating of a prediction of the capacitance C n,k3 with the help of a model of state which relates the capacitance C n,k3 to the capacitance C n,k3-1 of this same cell at a previous time k3-1, then • the acquisition, at time k3, of a measurable physical quantity z k3 defined by the following relation: z k 3 = ∑ m = k − N m = k − 1 i m where k is the closest time to the time k3 and N is an integer greater than or equal to one, z k3 being equal to i k-1 when N is equal to one, • the calculating of a prediction ẑ k3 of the measurable physical quantity z k3 with the help of a model of observation, and • the correction of the prediction of the capacitance C n,k3 as a function of the difference between the acquired physical quantity z k3 and the prediction ẑ k3 calculated from the model of observation, the method also involves: - the automatic learning (102, 212) of a function G SVM that returns the capacitance C n,k3 as a function of the capacitance C n,k3-1 and at least one physical characteristic cc 1,k3 correlated with the change over time of the capacitance of the cell and measured at time k3, this automatic learning involving the execution of a supervised learning algorithm on a learning database, this learning database containing values of the capacitance and, for each value of the capacitance, the value of the physical characteristic measured at the same time and the preceding value of the capacitance, and - once the function G SVM has been learned, during the execution of step b) at a later time k3+1, the calculating of the prediction of the capacitance C n,k3+1 utilizes the model of state defined by the following relation C n,k3+1 = G SVM (C n,k3, cc 1,k3+1). a) the acquisition, at a time k, of a measured charging or discharging current intensity ik of the cell, b) the estimation of the capacitance C n,k3 of the cell at a time k3 by using a Kalman filter, this estimation involving: • the calculating of a prediction of the capacitance C n,k3 with the help of a model of state which relates the capacitance C n,k3 to the capacitance C n,k3-1 of this same cell at a previous time k3-1, then • the acquisition, at time k3, of a measurable physical quantity z k3 defined by the following relation: z k 3 = ∑ m = k − N m = k − 1 i m where k is the closest time to the time k3 and N is an integer greater than or equal to one, z k3 being equal to i k-1 when N is equal to one, • the calculating of a prediction ẑ k3 of the measurable physical quantity z k3 with the help of a model of observation, and • the correction of the prediction of the capacitance C n,k3 as a function of the difference between the acquired physical quantity z k3 and the prediction ẑ k3 calculated from the model of observation, the method also involves: - the automatic learning (102, 212) of a function G SVM that returns the capacitance C n,k3 as a function of the capacitance C n,k3-1 and at least one physical characteristic cc 1,k3 correlated with the change over time of the capacitance of the cell and measured at time k3, this automatic learning involving the execution of a supervised learning algorithm on a learning database, this learning database containing values of the capacitance and, for each value of the capacitance, the value of the physical characteristic measured at the same time and the preceding value of the capacitance, and - once the function G SVM has been learned, during the execution of step b) at a later time k3+1, the calculating of the prediction of the capacitance C n,k3+1 utilizes the model of state defined by the following relation C n,k3+1 = G SVM (C n,k3, cc 1,k3+1). • the calculating of a prediction of the capacitance C n,k3 with the help of a model of state which relates the capacitance C n,k3 to the capacitance C n,k3-1 of this same cell at a previous time k3-1, then • the acquisition, at time k3, of a measurable physical quantity z k3 defined by the following relation: z k 3 = ∑ m = k − N m = k − 1 i m where k is the closest time to the time k3 and N is an integer greater than or equal to one, z k3 being equal to i k-1 when N is equal to one, • the calculating of a prediction ẑ k3 of the measurable physical quantity z k3 with the help of a model of observation, and • the correction of the prediction of the capacitance C n,k3 as a function of the difference between the acquired physical quantity z k3 and the prediction ẑ k3 calculated from the model of observation, - the automatic learning (102, 212) of a function G SVM that returns the capacitance C n,k3 as a function of the capacitance C n,k3-1 and at least one physical characteristic cc 1,k3 correlated with the change over time of the capacitance of the cell and measured at time k3, this automatic learning involving the execution of a supervised learning algorithm on a learning database, this learning database containing values of the capacitance and, for each value of the capacitance, the value of the physical characteristic measured at the same time and the preceding value of the capacitance, and - once the function G SVM has been learned, during the execution of step b) at a later time k3+1, the calculating of the prediction of the capacitance C n,k3+1 utilizes the model of state defined by the following relation C n,k3+1 = G SVM (C n,k3, cc 1,k3+1).