Display omitted •Build a method for joint estimation of both state-of-charge and state-of-energy.•Data of an IFP1865140-type battery have been analyzed comprehensively in order to better understand the cell character.•The particle filter is used for simultaneous SOC and SOE estimation to improve the accuracy.•Dynamic temperature experiments are performed to verify the robustness of the new method. The state-of-charge (SOC) is a critical index in battery management system (BMS) for electric vehicles (EVs). However in the energy storage systems, the available energy also acts as a significant role. Through the estimating result of state-of-energy (SOE), we can further estimate how long the battery is going to last if we apply a low power demand, a high power demand, or even a dynamic power demand. Unlike the SOC, the SOE is not only the integral of current but also the integral of voltage which include the nonlinearity of Li-ion batteries. Since there are accumulated errors caused by current or voltage measurement noise, a joint estimator based on particle filter is proposed for the estimation of both SOC and SOE. Validation experiments are carried out based on IFP1865140-type batteries under both constant and dynamic current conditions. To further verify the robustness of the proposed method, experiments are performed under dynamic temperatures. The experiment results have verified that accurate and robust SOC and SOE estimation results can be obtained by the proposed method.