Lithium dendrites, with their high reactivity, pose a critical challenge to the safety and longevity of lithium-based batteries. Effective regulation strategies are crucial for mitigating battery degradation and enhancing reliability. Conventional approaches, such as relaxation following lithium plating or regulated discharging, often fail to simultaneously address the formation of solid electrolyte interface and isolated lithium. Here, we demonstrate that rational utilization of electric field relaxation following dendrite growth can reduce defective solid electrolyte interface and isolated lithium by balancing solid electrolyte interface growth and dendrite morphology smoothing near the relaxation time constant. Building upon the mechanism, we propose a short-term relaxation method to manipulate lithium plating, which achieves an enhancement of capacity retention from 80% up to 95% at 3 C-rate (20 min) fast-charging on commercial batteries. These findings highlight the importance of relaxation after dendrites formation for safe, long-life and fast-charging batteries, particularly where dendrite growth is the limiting factor.

The morphology of plated lithium (MPL) metal on graphite anodes, traditionally described as “moss‐like” and “dendrite‐like”, exert a substantial negative influence on the performance of lithium‐ion batteries (LIBs) by modulating the metal‐electrolyte interface and side reaction rates. However, a systematic and quantitative analysis of MPL is lacking, impeding effective evaluation and manipulation of this detrimental issue. In this study, we transition from a qualitative analysis to a quantitative one by conducting a detailed examination of the MPL. Our findings reveal that slender lithium dendrites reduces the lifespan and safety of LIB by increasing the side reaction rates and promoting the formation of dead lithium. To further evaluate the extent of the detrimental effect of MPL, we propose the specific surface area (SSA) as a critical metric, and develop an in situ method integrating expansion force and electrochemical impedance spectroscopy to estimate SSA. Finally, we introduce a pulse current protocol to manipulate hazardous MLP. Phase field model simulations and experiments demonstrate that this protocol significantly enhances the reversibility of plated lithium. This research offers a novel morphological perspective on lithium plating, providing a more detailed fundamental understanding that facilitates effective evaluation and manipulation of plated lithium, thereby enhancing the safety and extending the cycle life of LIBs. image

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.