Triboelectric nanogenerators (TENGs) provide a simple and effective method to collect low-frequency mechanical energy. TENGs combined with the human body can collect biomechanical energy and convert it into electrical signals to monitor human body activities through four basic working modes. At present, TENGs have made great progress in the application of motion sensing, man−machine interfaces, medical care, and so on. This paper summarizes the basic working modes and principle of TENGs, the factors affecting the output performance of TENGs, as well as the research progress of TENGs as self-powered human body sensors at present and makes a prospect for the future.
Despite
their promising potential, the real performance of lithium-sulfur
batteries is still heavily impeded by the notorious shuttle behavior
and sluggish conversion of polysulfides. Complex structures with multiple
components have been widely employed to address these issues by virtue
of their strong polarity and abundant surface catalytic sites. Nevertheless,
the tedious constructing procedures and high cost of these materials
make the exploration of alternative high-performance sulfur hosts
increasingly important. Herein, we report an intrinsic defect-rich
hierarchically porous carbon architecture with strong affinity and
high conversion activity toward polysulfides even at high sulfur loading.
Such an architecture can be prepared using a widely available nitrogen-containing
precursor through a simple yet effective in situ templating
strategy and subsequent nitrogen removal procedure. The hierarchical
structure secures a high sulfur loading, while the intrinsic defects
strongly anchor the active species and boost their chemical conversion
because of the strong polarity and accelerated electron transfer at
the defective sites. As a result, the lithium-sulfur batteries with
this carbon material as the sulfur host deliver a high specific capacity
of 1182 mAh g–1 at 0.5 C, excellent cycling stability
with a capacity retention of 70% after 500 cycles, and outstanding
rate capability, one of the best results among pure carbon hosts.
The strategy suggested here may rekindle interest in exploring the
potential of pure carbon materials for lithium-sulfur batteries as
well as other energy storage devices.
Solid nanogenerators often have limited charge transfer due to their low contact area. Liquid–liquid nanogenerators can transfer a charge better than the solid–solid and solid–liquid counterparts. However, the precise manipulation of the liquid morphology remains a challenge because of the fluidity limits of the liquid. In this work, using the surface tension of a droplet to fix its shape, a liquid-liquid triboelectric nanogenerator in Contact-Separation mode is designed using an immiscible aqueous-aqueous interface, achieving a contact surface charge transfer of 129 nC for a single droplet. The configuration is proven to be applicable in humid environments, and the two-phase materials have good biocompatibility and can be used as an effective drug carrier. Therefore, this nanogenerator is useful for designing future implantable devices. Meanwhile, this design also establishes the foundation of aqueous electronics, and additional applications can be achieved using this route.
This paper studies the state of charge (SOC) estimation of supercapacitors and lithium batteries in the hybrid energy storage system of electric vehicles. According to the energy storage principle of the electric vehicle composite energy storage system, the circuit models of supercapacitors and lithium batteries were established, respectively, and the model parameters were identified online using the recursive least square (RLS) method and Kalman filtering (KF) algorithm. Then, the online estimation of SOC was completed based on the Kalman filtering algorithm and unscented Kalman filtering algorithm. Finally, the experimental platform for SOC estimation was built and Matlab was used for calculation and analysis. The experimental results showed that the SOC estimation results reached a high accuracy, and the variation range of estimation error was [−0.94%, 0.34%]. For lithium batteries, the recursive least square method is combined with the 2RC model to obtain the optimal result, and the estimation error is within the range of [−1.16%, 0.85%] in the case of comprehensive weighing accuracy and calculation amount. Moreover, the system has excellent robustness and high reliability.
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