In this paper, a novel error-tolerant iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal battery control and management problems in smart home environments with renewable energy. A main contribution for the iterative ADP algorithm is to implement with the electricity rate, home load demand, and renewable energy as quasi-periodic functions, instead of accurate periodic functions, where the discount factor can adaptively be regulated in each iteration to guarantee the convergence of the iterative value function. A new analysis method is developed to guarantee the iterative value function to converge to a finite neighborhood of the optimal performance index function, in spite of the differences of the electricity rate, the home load demand, and the renewable energy in different periods. Neural networks are employed to approximate the iterative value function and control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Numerical results and comparisons are given to illustrate the performance of the developed algorithm.