The battery state of energy and state of power are two important parameters in battery usage. The state of energy represents the residual energy storage in battery and the state of power represents the ability of battery discharge/charge. To estimate the two states with high accuracy, the characteristics of battery maximum available capacity and open-circuit voltage are analyzed under different working temperatures. Meanwhile, the equivalent circuit model of the battery is employed to embody the battery dynamic performance. To improve the accuracy of the battery states estimation, the multi-time-scale filter is applied in battery model parameters identification and battery states prediction. Besides, the state of power is analyzed by multi-constrained conditions to ensure battery work with safety. The proposed approach is verified by experiments operated on lithium-ion battery under new European driving cycle profiles and dynamic test profiles. The experimental results indicate the proposed method can estimate the battery states with high accuracy for actual application. In addition, the factors affecting the change of battery states are analyzed.