Digital Twin–Driven Joint State Estimation Method for Lithium-Ion Battery States

To address the drift of equivalent parameters in lithium-ion batteries during operation due to varying operating conditions and aging, which degrades model adaptability, and the difficulty of accurately tracking multiple coupled states simultaneously, this article proposes a digital twin–driven joint state estimation method to enable coordinated updating of electrothermal parameters and multiple states in a battery pack. First, an online-oriented digital twin–driven joint state estimation framework is established, where real-time measured voltage, current, and temperature data are used for model-parameter updating and state correction, ensuring real-time consistency between the twin model and the physical counterpart. Second, an embeddable electrothermal coupled equivalent model is developed, and an online parameter identification strategy based on an improved metaheuristic algorithm is proposed. By using a sliding window and a parameter-variation penalty term, parameter fluctuations induced by noise are suppressed, thereby improving the tracking performance of time-varying parameters. Finally, a multitime-scale joint state estimation algorithm is designed to achieve coordinated state of charge (SOC)–state of health (SOH) updating at the sampling scale and the cycle scale, enabling accurate joint estimation under multistate coupling. Experimental results show that the proposed method can track parameter variations more stably and improve the accuracy of joint state estimation, while the computational time satisfies the requirements for online operation, providing theoretical and technical support for online battery state sensing and management.