Machine management based on battery state

The present disclosure relates to battery health across a fleet of machines (104). Battery life on the machine (104 (1)) varies significantly according to operational applications. The work site (102) has a plurality of identical machines (104 (1)) that work in various applications to meet productivity requirements. More particularly, the present disclosure relates to balancing the state of health (SoH) of batteries across a cluster of machines (104). The severity of various machine applications at a given work site (102) is determined using a full site model. The model actively tracks the operating history and current battery state of health of each machine (104) to determine an appropriate allocation such that the machines (104) periodically alternate through various applications. In addition, the model ensures that the battery has a sufficient state of health to complete the assigned task. The reduction in battery life results in downtime and associated additional costs for troubleshooting perception issue