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The state-of-charge and health prognosis are important factors for electric vehicles. The long short-term memory (LSTM) is used to estimate battery states, and it attracts a lot of attention. However, the traditional LSTM network has limited ability of feature extraction and battery states prediction in long time series. To solve this problem, an integrated attention mechanism is proposed to improve the performance of bidirectional LSTM networks, and multiple-dimensional temperature compensation is proposed to enhance prediction under changing temperatures. In experiments, the maximum errors of the proposed method are less than 1%, and the accuracy is improved by 9.39% and 22.36% at two current conditions. In the battery health prognosis, the proposed method improves the accuracy by 21.45% compared with that of bidirectional LSTMs.