Obtaining an estimation of the parameters and state of charge (SoC) of a lithium-ion battery is crucial for an electric vehicle. The parameters of a battery model are usually different throughout the battery lifetime. To obtain an accurate SoC and parameters and reduce the computational cost, a double-scale dual adaptive particle filter for online parameters and SoC estimation of lithium-ion batteries is proposed. First, the lithium-ion battery is modeled using the Thevenin model. Second, a double-scale dual particle filter is proposed and applied to the battery parameter and SoC estimation. To improve the accuracy and convergence ability to the initial environmental offset, a double-scale dual adaptive particle filter is proposed. Finally, the effectiveness and applicability of the two algorithms are verified by Lithium Nickel Manganese Cobalt Oxide (NMC) batteries of different ages. •A novel double adaptive particle filter was proposed to adapt to environmental variety.•Battery parameters and SoC can be renewed adaptively with two time scales.•Battery SoC is renewed every 1 s, and the parameters every 1 min.•The effectiveness and applicability of the methods are verified by aged batteries.