For the online implementation of infinite horizon optimal control for continuous linear systems, an online Q-learning algorithm is designed under the condition that the system dynamics are completely unknown.Based on the Hamiltonian function and the optimal cost function in the infinite horizon optimal control theory, the Q function of the continuous linear system is constructed.An Actor/Critic approximator structure is designed by using the integral reinforcement learning method.With asymptotic stability of the closed-loop system and convergence to the optimal solution, the parameters of the Q function are estimated online.The 6th-order linear system model of the turbocharged engine is numerically simulated, and the results show that both the Critic weight and the Actor weight asymptotically converge to the optimal value, and the model-free optimal control is realized.