However, the development of Li-S batteries is still facing many challenges, mainly including the insulating properties of S/Li 2 S 2 /Li 2 S, the huge volume changes during cycles as well as the dissolution and shuttle of lithium polysulfides (LiPSs) in the electrolyte. Additionally, the complex multi-electron and multi-phase reactions of sulfur species cause sluggish redox kinetics, which severely limits the battery performance. [2] Furthermore, for the purpose of taking full advantages of high-energy-density of Li-S batteries in practical applications, high sulfur loadings and low amount of electrolyte are necessary. It further increases the electrochemical polarization and aggravates the loss of LiPSs, leading to the inferior cycling stability and low specific capacity. [3] Nowadays, many researches concentrate on the design and optimization of sulfur host to solve the above problems. [4] Carbon materials and metal compounds are generally combined as the sulfur host to construct channels for transferring electrons and ions, adsorb LiPSs and catalyze the conversions of LiPSs. [5] Among the metal compounds, transition metal phosphides (TMPs) have drawn increasing interests because of their excellent electronic conductivity, easily adjustable electronic structure and high catalytic activity. [6] Some studies have shown that TMPs can chemically immobilize LiPSs through M-S and PLi bonds to restrain the shuttle effect and expedite redox kinetics in electrochemical conversions of LiPSs. Chen et al. found that CoP nanoparticles can effectively capture LiPSs and reduce the overpotential of Li 2 S nucleation. [7] Wang et al. demonstrated that MoP nanoparticles can inhibit the formation of "dead sulfur" under lean electrolyte conditions. [8] Qian et al. revealed the best catalytic behavior of CoP among several cobalt-based metal compounds (Co 3 O 4 , CoS 2 , Co 4 N, and CoP). [9] Although some progress has been made in the application of TMPs in Li-S batteries, it is still a challenge to further restrain the shuttle effect and improve the electrochemical kinetics through regulating its electronic structures.Since defect engineering has been shown to effectively tailor the electronic structure of metal compounds, the possibility of tuning the adsorption and electrochemical conversions of LiPSs on the surface of sulfur hosts through anion vacancy Lithium-sulfur batteries have aroused great interest in the context of rechargeable batteries, while the shuttle effect and sluggish conversion kinetics severely handicap their development. Defect engineering, which can adjust the electronic structures of electrocatalyst, and thus affect the surface adsorption and catalytic process, has been recognized as a good strategy to solve the above problems. However, research on phosphorus vacancies has been rarely reported, and how phosphorus vacancies affect battery performance remains unclear. Herein, CoP with phosphorus vacancies (CoP-Vp) is fabricated to study the enhancement mechanism of phosphorus vacancies in Li-S chemistry...

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Partnership for a new generation of vehicles (PNGV) model is a conventional battery equivalent circuit model (ECM). However, identifying the best parameters for this model is a challenge. In this study, the PNGV model is transformed into a directly identifiable difference equation to identify its parameters. Subsequently, the model reference adaptive system (MRAS) is used to realize the real-time identification of the model parameters. The identification accuracy of the MRAS is found to be superior to that of the recursive extended least square algorithm. For a single hybrid pulse power characterization (HPPC), the PNGV model identified by the MRAS can achieve a high-precision terminal voltage estimation. For lithium iron phosphate, lithium titanate, and nickel-metal hydride batteries, the root mean square errors are 0.024, 0.048, and 0.020 V, respectively. Besides, the real-time state of charge (SOC) estimation can be realized by the identified open-circuit voltage (OCV). The average errors of the three batteries are only −0.02, −0.01, and −0.01, respectively. Since the PNGV model has a capacity of describing the change of OCV with the current accumulation effect, the model is only suitable for simulating single HPPC or positive-negative pulses with equal amplitude and not for other current pulses. This is a major drawback of the PNGV model. The real-time PNGV model parameters identification method proposed in this study can provide a solid foundation for various state estimation of a battery. Novelty Statement• Transformation of the PNGV model into a difference equation that can be directly identified.• Real-time identification of the PNGV model parameters via the MRAS.• The identified OCV realized the real-time SOC estimation and discussed the deficiencies of the PNGV model.