smart energy management approaches can improve the economy and performance of residential homes integrated with photovoltaic array (PV) and plug-in electric vehicle (PEV). The key novelty of this paper is improving the real-time operation of the smart home using advanced stochastic forecast techniques and stochastic control methods. In this paper, an optimal model predictive control is formulated for a smart home to minimize the electricity cost under time-varying electricity price signals. In addition, the PEV charging and home power demand requirements have to be satisfied in a smart and optimal way. Stochastic forecast model is developed for PV, home load demand and PEV to consider the effect of the different uncertainties on their performance. Furthermore, a fundamental trade-off between PEV lithium-ion battery aging and economic performance of the energy management system is implemented through an appropriate cost function formulation. In this paper, the PEV departure time and required energy consumption during driving are modeled by Markov chain and conditional probability. In addition, the PV performance and home load demand are modeled by PVWatt model and adaptive neuro-fuzzy inference system (ANFIS) respectively. Afterward, a Model Predictive control (MPC) is designed to minimize the cost of energy as well as make increase the lifetime of the PEV battery by avoiding unnecessary charging/discharging schemes. The results demonstrate the effectiveness and enhancement of the proposed method.