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Severe capacity degradation at low temperatures (<−20°С) hampers wide applications of lithium‐ion batteries (LIBs) in consumer electronics and electric vehicles. Existing works are dedicated to electrolyte modification because that electrolyte controls both Li+ transportation and interfacial reaction. However, the efforts on electrolytes are always hard to balance rate performance and low‐temperature capacity due to their high viscosity. Herein, a binary coating layer for Li3V2(PO4)3 cathode material without changing electrolyte formulation is proposed, which significantly improves the high‐rate capability and low‐temperature performance of batteries. YPO4 nanoparticles are in situ formed in the amorphous surface carbon layer under the reaction between Li3V2(PO4)3 and Y(NO3)3 during post‐thermal treatment. The C+YPO4 binary coating reduces the side reactions of Li3V2(PO4)3 at high voltage. In addition, the binary surface coating also improves the interfacial kinetics of the electrode at low temperatures. Benefiting from these advantages, the Li3V2(PO4)3 cathode material can cycle stably at ultra‐high rates up to 50 C. In addition, the capacity retention at −20 and −40 °С are improved to 89.1% and 75.7%, respectively. This binary surface‐coated Li3V2(PO4)3 cathode material shows promising application potential in low‐temperature LIBs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]