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In many applications Wireless Sensor Networks (WSNs) operate on batteries. As the network lifetime depends on the amount of available energy, each node must balance its limited resources in order to extend the network lifetime. The requirement of managing network resources, in an intelligent and autonomous way, makes the knowledge of the amount of available energy a key factor for the implementation of auto-management techniques. This work aims at modeling, using a quantitative and qualitative approach, a new model of alkaline batteries for WSN, which incorporates the discharge rate, capacity retention, and relaxation effect. The results show that the Behavioral Model of Alkaline Batteries (BMAB) has an estimate error inferior to 2% of the real value, whereas the best result in the literature is superior to 21%. Under real WSN applications, with duty cycle of 5% or less, BMAB error is below 0.6%, lower than 18% reported in the literature.