In this article, a model predictive control (MPC) energy management strategy is proposed to distribute power flows of proton exchange membrane fuel cell (PEMFC)-based hybrid power systems consisting of PEMFC, battery, and waste heat recovery system such as TEG and CHP. To optimally meet the demand of load power balancing as well as protect PEMFC from lifetime degradation, a novel objective function by considering fuel consumption, state-of-charge (SOC) of battery, as well as power slope and temperature of PEMFC is constructed and solved in the states prediction horizon within the defined lifetime constraints and SOC limitations. In particular, temperature effects are newly introduced by adding a state-variable to the energy management model and formulating a penalty function. Simulations with mobility and stationary application scenarios are presented. In the automobile case, the hydrogen consumption of the constraints MPC is reduced by 9.98% compared with the rule-based strategy, and the same results can be achieved in the household application. A hardware in the loop experiment was carried out to verify the real-time performance of the MPC strategy which occupied a 2.21% average CPU load rate. The proposed MPC strategy has a promising fuel consumption optimization, lifetime extension, and real-time capability.