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Conventional Al-air battery has many disadvantages for miniwatt applications, such as the complex water management, bulky electrolyte storage and potential leakage hazard. Moreover, the self-corrosion of Al anode continues even when the electrolyte flow is stopped, leading to great Al waste. To tackle these issues, an innovative cotton-based aluminum-air battery is developed in this study. Instead of flowing alkaline solution, cotton substrate pre-deposited with solid alkaline is used, together with a small water reservoir to continuously wet the cotton and dissolve the alkaline in-situ. In this manner, the battery can be mechanically recharged by replacing the cotton substrate and refilling the water reservoir, while the thick aluminum anode can be reused for tens of times until complete consumption. The cotton substrate shows excellent ability for the storage and transportation of alkaline electrolyte, leading to a high peak power density of 73 mW cm−2 and a high specific energy of 930 mW h g−1. Moreover, the battery discharge capacity is found to be linear to the loading of pre-deposited alkaline, so that it can be precisely controlled according to the mission profile to avoid Al waste. Finally, a two-cell battery pack with common water reservoir is developed, which can provide a voltage of 2.7 V and a power output of 223.8 mW. With further scaling-up and stacking, this cotton-based Al-air battery system with low cost and high energy density is very promising for recharging miniwatt electronics in the outdoor environment.

The non-convex complementarity constraints present a fundamental computational challenge in energy constrained optimization problems. In this work, we present a new, linear, and robust battery optimization formulation that sidesteps the need for battery complementarity constraints and integers and prove analytically that the formulation guarantees that all energy constraints are satisfied which ensures that the optimized battery dispatch is physically realizable. In addition, we bound the worst-case model mismatch and discuss conservativeness. Simulation results further illustrate the effectiveness of this approach.

The automotive industry is increasingly deploying software solutions to provide value-added features for vehicles, especially in the era of vehicle electrification and automation. However, the ever-increasing cyber components of vehicles (i.e., computation, communication, and control) incur new risks of anomalies, as evident by the millions of vehicle recalls by different automakers. To mitigate these risks, we design the B-Diag, a battery-based diagnostic system detects engine anomalies with a cyber-physical approach. The core idea of B-Diag is to diagnose engines using the physically-induced correlations between battery voltage and engine variables, which is captured as a customized 3-layer correlation graph and a set of data-driven norm models describing the edges thereof. The design of B-Diag is steered by a dataset collected with a prototype system when driving a 2018 Subaru Crosstrek in real-life for over three months. Our evaluation shows B-Diag to detect anomalies of 17 engine variables with a >86% (up to 100%) accuracy.

Defect-engineered carbon materials have been emerged as promising electrocatalysts for oxygen reduction reaction (ORR) in metal-air batteries. Developing a facile strategy for the preparation of highly active nanocarbon electrocatalysts remains challenging. Herein, a low-cost and simple route is developed to synthesize defective graphene by pyrolyzing the mixture of glucose and carbon nitride. Molecular dynamics simulations reveal that the graphene formation is ascribed to two-dimensional layered feature of carbon nitride, and high compatibility of carbon nitride/glucose systems. Structural measurements suggest that the graphene possesses rich edge and topological defects. The graphene catalyst exhibits higher power density than commercial Pt/C catalyst in a primary Zn-air battery. Combining experimental results and theoretical thermodynamic analysis, it is identified that graphitic nitrogen-modified topological defects at carbon framework edges are responsible for the decent ORR performance. The strategy presented in this work can be can be scaled up readily to fabricate defective carbon materials.

Accurate prediction of the lithium-ion battery’s future capacity and remaining useful life (RUL) is of great significance to the battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey-Markov chain model (GMCM) and the improved Gaussian process is proposed in this paper. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multi-output mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multi-step prediction.

The state of health (SOH) estimation is of great importance in the battery management system. However, the accurate real time state of health estimation is still a challenge because the capacity of the battery cannot be precisely monitored and measured in real-time. In response to this problem, this paper proposes an online SOH monitoring method for lithium-ion battery (LIB) based on the charge-discharge feature of battery in real time. Firstly, the relationship between the charge-discharge feature and SOH through principal component analysis (PCA) is analyzed, and the appropriate feature which can precisely describe the battery aging process is then selected. Secondly, as to decompose and denoise the collected charge-discharge feature data, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is applied. The major trend obtained is predicted by logistic regression based on the sliding time window (LR-STW), and the minor fluctuation is predicted by Kalman Filter (KF). Finally, an online SOH estimation method based on RBF neural network is proposed to establish the mapping relationship between feature and SOH. The proposed method was verified by NASA experimental data. The experimental results show that the proposed method can accurately predict the LIB SOH in real time, and the robustness of the proposed method is strengthened and promised.

In this paper, an enhanced thermal model is proposed for pouch batteries, which can be used for a battery management system (BMS) at low temperatures and high current rates. This thermal model integrates a virtual resistance model (VRM) and post resistance model at low temperatures. Firstly, based on the Arrhenius equation, the resistance model at low temperatures (from 0 °C to -10 °C) and high discharge rates (from 3.125C to 12.5C) is extrapolated from the resistance model under room temperature conditions. In this context, the resistance model at low temperatures is regarded as the VRM. In comparison to the experimental results, the maximum error of this VRM is 0.851 mΩ. Additionally, the equivalent post resistance model of the pouch battery, including that of the positive electrode and negative electrode, is also given for low temperatures. Finally, the complete thermal model for pouch batteries is validated at low temperatures and high discharge rates. The temperature evolution at different points on the battery, along with the heat generation properties of the battery, are simulated under cyclic pulse current and constant current working conditions. For cyclic pulse current, the maximum mean error in terms of temperature evolution is 0.72 °C, and for constant current working conditions, the maximum mean error is 0.79 °C.

Advanced safe battery storage systems with health prognostic performance are vital for electric vehicles. Various faults of lithium-ion batteries are usually undetectable in their early stage due to their concealment and graduality. This paper presents a real-time fault diagnosis and isolation scheme for real-scenario batteries using the normalized discrete wavelet decomposition. The early frequency-domain features of the fault signals are extracted utilizing the high-frequency detail wavelet components, and a multi-level fault prognosis strategy is developed considering complex charging/driving characteristics under real-vehicle operating conditions. The verification results, implemented on loose wire connection batteries and real-scenario thermal runaway batteries, demonstrate that the proposed method can accurately extract and locate the hidden fault signals even under small magnitudes, as well as effectively detecting and isolating battery faults before thermal runaway. Furthermore, significant reliability and stability of the proposed method are verified on more real-vehicle operation data, enabling online monitorable and traceable of battery faults before triggering thermal runaway, safeguarding drivers and passengers in real-world vehicular operation.

Although Li-ion batteries are widely used, recent catastrophic accidents have seriously hindered their widespread application. In this study, a novel acoustic-signal-based battery fault warning and location method is proposed. This method requires only four acoustic sensors at the corners of the energy storage cabin. It captures the venting acoustic signal when a fault occurs in the cell and calculates the spatial location of the cell. The maximum spatial error is 0.1 m. Considering that the effective range of a fire-fighting facility is significantly greater than 0.1 m, such a location error is acceptable. Furthermore, a wavelet-transform-based anti-misjudgment method that ensures the reliability of the fault warning and location is proposed. Thus, a nonintrusive, timely, and effective solution to ensure the safety of the battery energy storage system is provided.

Aqueous zinc-air battery (ZAB) has attractive features as the potential energy storage system such as high safety, low cost and good environmental compatibility. However, the issue of dendrite growth on zinc metal anodes has seriously hindered the development of ZAB. Herein, the N-doped carbon cloth (NC) prepared via magnetron sputtering is explored as the substrate to induce the uniform nucleation of zinc metal and suppress dendrite growth. Results show that the introduction of heteroatoms accelerates the migration and deposition kinetics of Zn2+ by boosting the desolvation process of Zn2+, eventually reducing the nucleation overpotential. Besides, theoretical calculation results confirm the zincophilicity of N-containing functional group (such as pyridine N and pyrrole N), which can guide the nucleation and growth of zinc uniformly on the electrode surface by both promoting the redistribution of Zn2+ in the vicinity of the surface and enhancing its interaction with zinc atoms. As a result, the half-cell assembled with magnetron sputtered carbon cloth achieves a high zinc stripping/plating coulombic efficiency of 98.8% and long-term stability of over 500 cycles at 0.2 mA cm-2. And the Coulombic efficiency reached about 99.5% at the 10th cycle and maintained for more than 210 cycles at a high current density of 5.0 mA cm-2. The assembled symmetrical battery can deliver 220 plating/stripping cycles with ultra-low voltage hysteresis of only 11 mV. In addition, the assembled zinc-air full battery with NC-Zn anode delivers a high special capacity of about 429 mAh gZn-1 and a long life of over 430 cycles. The effectiveness of surface functionalization in promoting the transfer and deposition kinetics of Zn2+ presented in this work shows enlightening significance in the development of metal anodes in aqueous electrolytes.

This paper shows how to exploit the degree of freedom represented by the common-mode voltage, which is inherent in three-phase Cascaded H-Bridge (CHB) converters, to minimize the total Root Mean Square (RMS) current value of the distributed dc sources. An optimal common-mode voltage injection law is derived, constituting a Maximum Power Per Ampere (MPPA) modulation strategy with respect to the currents of the dc sources. The potential benefits introduced by the proposed algorithm are analyzed in the context of battery-fed CHB converters and validated experimentally with a three-time-constant Randles model of a battery cell. The MPPA strategy is compared with traditional common-mode voltage injection methods, and battery loss reduction is demonstrated. The obtained battery energy saving is dependent on the converter modulation index and power factor. Experimental tests on a 36-cell full-bridge CHB converter validates the simulation and numerical derivation. To demonstrate the efficacy of the proposed method in practical applications, a 3 MW Energy Storage System (ESS) and a 110 kW Battery Electric Vehicle (BEV) undergoing standard drive cycles are presented as case studies. Compared to traditional modulation strategies, the MPPA strategy reduces the battery losses by up to 10.9% in the ESS and 27.5% in the BEV application.

This paper proposes a method to evaluate and model the power consumption of modern virtual reality playback and streaming applications on smartphones. Due to the high computational complexity of the virtual reality processing toolchain, the corresponding power consumption is very high, which reduces operating times of battery-powered devices. To tackle this problem, we analyze the power consumption in detail by performing power measurements. Furthermore, we construct a model to estimate the true power consumption with a mean error of less than 3.5%. The model can be used to save power at critical battery levels by changing the streaming video parameters. Particularly, the results show that the power consumption is significantly reduced by decreasing the input video resolution., 6 pages, 5 figures

High-energy-density lithium-sulfur batteries has attracted substantial attention as competitive candidates for large-scale energy storage technologies. Still, the adverse “shuttle effect” and sluggish sulfur conversion reaction kinetics immensely obstruct their commercial viability. Herein, a two-dimensional metallic 1T phase WS2 (1T-WS2) nanosheets modified functional separator is developed to improve the electrochemical performance. Meanwhile, the semiconducting bulk-WS2 crystals, and 2H phase WS2 (2H-WS2) nanosheets with more basal-plane S-vacancy defects are also prepared to probe the contributions of the crystal structure (phase), S-vacancy defects, and edges to the Li–S batteries performance experimentally and theoretically. In merits of the synergistic effect of high ion and electron conductivity, enhanced binding ability to lithium polysulfides (LiPSs), and sufficient electrocatalytic active sites, the 1T-WS2 shows highly efficient electrocatalysis of LiPSs conversion and further improves Li–S battery performance. As expected, thus-fabricated cells with 1T-WS2 nanosheets present superior cycle stability that maintain capacity decline of 0.039% per cycle after 1000 cycles at 1.0 C. The strategy presented here offers a viable approach to reveal the critical factors for LiPSs catalytic conversion, which is beneficial to developing advanced Li–S batteries with enhanced properties.

The demand for high-density, high-efficiency bi-directional battery chargers is driven by the fast development of energy storage system in renewable energy system, micro-grid, and transportation electrification. Isolated dc-dc converter that interfaces a battery with a variable voltage range is one of the critical components. Input-parallel output-series (IPOS) partial power (PP) converter is considered a promising high-efficiency, high density solution because only a fraction of power is processed via multi-stage converters to regulate the output voltage. However, due to the tight coupling between two dc transformers (DCXs), the design of PP converter is complicated. To solve this issue, a design procedure for a bi-directional soft-switching resonant type PP converter is proposed. In the parameters design part, a decoupled design method is proposed to simplify the DCXs design. With this method, two DCXs can be designed separately, and the typical optimization method can be easily applied. As for the hardware design part, to minimize the ac loop inductance and resistance, a 2-directions (2-D) flux cancelation method and an “intra-leaving” winding structure are proposed for circuit layout and high-frequency transformer to obtain high operation efficiency and power density. Finally, the whole design procedure is verified by an 18 kW rated, 25 kW peak, prototype operating at 500 kHz. The realized PP converter features a peak efficiency of 98. 8% and a power density of 142 W/in3. This article is accompanied by two videos demonstrating the dynamic voltage regulation test and the efficiency measurement.

Accurate and robust state of charge (SOC) estimation of lithium-ion batteries (LIBs) is very important to prolong battery life and prevent catastrophic failures. However, the accuracy of SOC estimation is seriously affected by unknown noise, uncertain interference and temperature. In this paper, a novel model fusion method is presented to achieve precise SOC estimation in the case of non-Gaussian noise and outliers. A state-space model of battery system is first developed to conduct SOC estimate. Then, a novel robust kernel Takagi Sugeno (T-S) fuzzy method to minimize the mean and variance of model error is developed to characterize the electrical performance of battery. This modeling strategy uses local nonlinear modeling mechanism, which the nonlinear relationship between data can be well represented, thus it can obtain a superior modeling performance. Finally, a multi-innovation unscented Kalman filter (UKF) algorithm considering the historical state information is designed to incorporate with the robust fuzzy model to filter out the noise in the observation and update the SOC estimation. Additional stability analysis shows the convergence of the proposed multi-innovation UKF algorithm. Experiments and verifications show that the presented estimation architecture is effective and has better modeling ability compared with several common methods.

Fuel cell electric vehicles (FCEVs) have batteries to mitigate problems such as the inability to store energy, poor response speed, and low power density. In the conventional power conversion system, the fuel cell (FC) and battery can be connected to the output load via two independent converters, namely a unidirectional converter for powering with the FC and a bidirectional converter for powering and regenerating via the battery. The conventional converter has a simple structure; it achieves specific operating modes that are required in various driving situations. However, it has two inductors and many operating switches when the FC and battery are simultaneously used. In addition, all the switches have high voltage stresses. These factors can degrade the overall power density and efficiency. Thus, an integrated converter with single inductor is proposed in this paper. By employing a battery mode selection cell (BMSC), the two converters can be integrated with only one inductor. Moreover, the number of operating switches is reduced during powering, and low voltage-rated semiconductor devices can be used. Therefore, the proposed converter can achieve high power density and low conduction and core losses. The validity of the proposed converter was demonstrated by the experiments under various operating conditions.

This paper proposes a new concept of Aviation-to-Grid (A2G) that utilizes electric aircraft (EA) charging to provide flexibility to the power grid. Smart EA charging system with battery swap method is developed using PV, gas turbine, and grid electricity. Hourly energy dispatch strategy is produced based on the mixed integer linear programming method to meet electrified aviation and provide A2G frequency response. Case studies are conducted in 8 major UK airports considering seasonal flight schedules and power system scenarios. Results show that the EA charging system can provide effective primary and secondary frequency response to improve the frequency nadir by 0.2 - 0.3 Hz under grid disturbance. The total A2G frequency response capacity across the 8 UK airport can reach up to 1,300 MW overnight and up to 900 MW daytime. Annual A2G frequency response revenue is 46.58 million pounds, which can cover 19.8% to 30% of EA charging costs.

We have developed a comprehensive 3D heating test simulation model of a Li-ion battery cylindrical cell and module, taking gas flow and combustion into account. The model is based on the equivalent circuit model and includes not only the heating of a cell (joule heating and thermal decomposition) but also gas ejection flow from the cell and combustion. First, the validation of the cell model for surface temperature and voltage profiles was conducted for a commercial 18650-type cell with an actual heating experiment. Subsequently, a simulation model of the battery module, composed of total 98 cells heated by a burner, was developed, and the behavior of thermal runaway was evaluated. This model makes it possible to evaluate the events that occur from the start of heating the battery module to gas ejection, including housing melting, separator shutdown, and the propagation of thermal decomposition reactions between the cells, and might be a helpful tool for designing battery cells and modules.

Vehicle to Vehicle (V2V) energy sharing is emerging as an alternate solution to range anxiety and limited charging infrastructure challenges associated with electric vehicles (EVs). The existing off-board DC fast charging options for V2V ap- plication found in the literature are not effective due to the additional weight, size and cost of the external charger or interface. In this paper, a new V2V interface is proposed in which both EVs motor winding neutrals and negative rails of on-board drivetrains are directly connected to each other. This further helps in forming an integrated dual bidirectional DC-DC converter to control the direction of power flow. The proposed approach does not require reconfiguration of motor winding or mechanical clutch to arrest the motor from rotating and/or the reconfiguration of drivetrain inverter connections. The major hardware requirement for the proposed approach is, access to the already existing neutral connection of the EV motor phase windings. Furthermore, Finite Element Analysis is presented for 150 kW EV motor model and its performance validation with the high frequency DC current flowing in the motor windings during the V2V operation. A scaled lab prototype is developed to demonstrate the energy sharing between two Lithium-ion battery banks through proposed direct V2V power transfer concept.

Accurate remaining useful life (RUL) of batteries plays an imperative role in ensuring safe operations and avoiding catastrophic accidents. However, in practice, complicated working conditions bring challenges to accurate battery prognostics. In this study, an accelerated stress factors based nonlinear Wiener process model is proposed to enrich inadequate battery prognostic works at various operating conditions. To realize online individual battery prognostics, once a new measurement is available, the parameters of a state-space model constructed by the proposed model are posteriorly updated. Then, based on the Peukert law and the Arrhenius equation, two specific accelerated stress-relevant drift functions and their associated degradation models at different discharge rates and temperatures are respectively designed. Subsequently, RUL predictions are conducted using the proposed method. RUL predictions at different discharge rates and different temperatures demonstrate the accuracy and robustness of the proposed prognostic models. According to some general prognostic metrics, the proposed method is proved to be superior to four existing RUL prediction approaches.

As a typical mechatronics system, the battery manufacturing chain becomes a hot research topic because it directly determines electrode quality, further affecting manufactured battery performance. Due to the complexity of battery manufacturing, an effective sensitivity analysis solution that could quantify variable importance or correlations and explore impact variables toward resulting the electrode quality is urgently needed. This article scrutinizes the effects of component parameters from the mixing stage on the manufactured results of Li-ion battery electrode via classification modeling. Specifically, an effective RUBoost-based ensemble learning framework is proposed to compensate for class imbalance issue and well classify three key quality indicators including the electronic conductivity, thickness, and half-cell capacity for both LiFePO $_4$- and Li$_4$ Ti$_5$O$_{12}$-based electrode. Experimental results reveal that the proposed models could well handle the class imbalance issues and accurately classify/predict the qualities of the manufactured electrode. Moreover, the importance weights of variables and the correlations of variable pairs could be effectively quantified. Due to the superiority in terms of accuracy, interpretability, and data-driven nature, the proposed ensemble learning approach could not only help to conduct reliable multiclassification of manufactured electrode but also benefit smarter battery manufacturing.

As one of the most common cathode materials for aqueous zinc-ion batteries (AZIBs), manganese oxides have the advantages of abundant reserves, low cost, and low toxicity. However, the electrochemical mechanism at the cathode of aqueous zinc–manganese batteries (AZMBs) is complicated due to different electrode materials, electrolytes and working conditions. These complicated mechanisms severely limit the research progress of AZMBs system and the design of cells with better performance. Hence, the mechanism of AZMBs currently recognized by most researchers according to the classification of the main ions involved in faradaic reaction are introduced in the review. Then a series of reasons that affect the electrochemical behavior of the battery are summarized. Finally, the failure mechanisms of AZMBs over prolonged cycling are discussed, and the current insufficient research areas of the system are explained, along with the direction of further research being prospected.

In the inductive charging scenario of an electric vehicle (EV), the charger should have the capability to flexibly regulate the output power regardless of the misalignment and EV battery voltage. This paper proposes a hybrid voltage and frequency tuning strategy for a series-series (SS) inductive power transfer (IPT) system to regulate power through 0 to 3.3kW at any battery voltages and misalignment conditions without using additional converters. A voltage tuning is used to achieve full-power of 3.3kW and partly contribute to reducing the power. When the input voltage achieves the lower limit, a frequency tuning is used to further decrease the power to 0W, achieving the regulation through 0 to full power. The practical charging region is specified by 110mm airgap, 100mm misalignment in both x and y directions, and 180V-240V battery voltage range. Experiments are carried out at various conditions, and it shows that with an input voltage range of 180V-380V and a frequency of 82.5kHz~87.9kHz, the charging power can be regulated between 100W and 3.3kW at any case in the specified working region. In addition, a peak dc-dc efficiency of 95.7% is achieved at 3.29 kW.

The state-of-charge (SOC) estimation and remaining-dischargeable-time (RDT) prediction are critical and challenging questions to safe operation of lithium-ion batteries. The main challenges are the limited accuracy of traditional equivalent circuit model and computation-inefficiency of electrochemical battery models. To address this problem, this paper proposes a Lebesgue-sampling-based extended Kalman filter (LS-EKF) approach that integrates the high-fidelity of a simplified first principle (SFP) model with the low computation of Lebesgue sampling (LS) in a Bayesian estimation framework. In this framework, the SFP battery model is first introduced along with its design and validation. The LS-EKF is employed with the SFP model to estimate the SOC and predict the RDT. The proposed method is verified with a series of experiments under different operating conditions. The results and comparison with the traditional Riemann-sampling-based extended Kalman filter demonstrate the effectiveness of the proposed method in terms of the accuracy of estimation and prediction, as well as the computational efficiency.

Accurately estimating the capacity of lithium-ion batteries in electric vehicles (EVs) is critical for making correct management decisions. However, the randomness of the charging voltage range of EVs can lead to missing observations or reduced accuracy of capacity estimation methods. This paper proposes an adaptive battery capacity estimation method suitable for arbitrary charging voltage range based on incremental capacity (IC) analysis and data-driven techniques. All charging conditions of EVs are divided into three categories according to the charging voltage range. Three data-driven estimation sub-methods with sequential application priority are designed for the three charging conditions separately, including back-propagation neural network with IC peak coordinates as input, ensemble learning with local high IC curve as input, and linear regression with ampere-hour coordinate transformation. The method is based on a priori knowledge to select a suitable estimation sub-method under different charging conditions, so as to improve the adaptability. Experimental data is collected from eight commercial lithium-ion battery modules for model establishment and verification. Over 250,000 experimental samples at different states of health and random charging ranges show that the method can accurately estimate battery capacity under arbitrary charging conditions, with a maximum error of 2%.

In order to improve the power quality and decrease the consumed power of an induction motor (IM) which supplied by an alternate battery-powered system, an algorithm that can be used to save its energy consumption has been proposed. Control mechanism is to reduce the wasted energy which caused by IMs start-up current based on bounding its optimal starting voltage. Proposed algorithm includes two parts: 1) use Branch and Bound (BB 2) according to the control rule to adjust on-line voltage of the zinc bromine flow battery which is used to feed the IM. Due to proposed approach, the minimum start-up voltage of the battery-powered system can be predicted and by which one the IMs surge current has been suppressed on line. Moreover, the relationship of the output voltage of battery and inverter supply to the load current has been derived by modelling the battery-powered system. Experiments demonstrate that compared with before optimization, amount of energy consumption of the IM supplied by zinc bromine flow battery decreases by 8.5% and corresponding indicator parameters of the power quality can reach the standard IEC TS62749-2015.

Rational design of hybrid electrocatalysts of nanostructured non-precious metals/metal compound on highly conductive substrates is significant to enhance oxygen reduction activity for clean energy conversion. However, the limited exposure of active sites and sluggish mass transport on electrode trade-off the intrinsic performance of electrocatalyst. Herein, we report a strategic method to fabricate electrocatalyst with well-dispersed iron-based mixed nanoparticles embedding in nitrogen-doped carbon nanofibers (Fe-Fe3C/Fe3N@NCNFs) and demonstrate desired properties of the material, such as hierarchically porous structure, large specific surface area, and well-dispersion of mixed nanoparticles. The Fe-Fe3C/Fe3N@NCNFs electrocatalyst also exhibits high-performed activity in oxygen reduction reaction (ORR) with high onset potential of 0.998 V and half wave potential of 0.85 V, as well as high stability and methanol tolerance, which is even superior to benchmark performance of commercial Pt/C. In addition, a zinc-air battery assembled with Fe-Fe3C/Fe3N@NCNFs as the air cathode shows high open-circuit voltage (1.466 V), high specific capacities (778 mA h g−1 at 5 mA cm−2), excellent reversibility and stability. The remarkable improvement of electrocatalytic performance validates paramount advantages, attributing from the inter-connected conduction network and well-dispersed active sites on carbon nanofiber surface, over mass transfer process and maximizing exposure of catalytic sites

In this paper, an integrated drive converter of segmented-double-stator switched reluctance machine (SDS-SRM) with isolation and non-isolation charging capabilities for electric vehicle (EV) is presented. The proposed converter can not only reduce the number of the power switches, but also implement multiple operation modes. In non-isolation charging mode, the motor windings and existing power switches constitute a buck/boost converter to charge the battery. The appropriate rotor positions are obtained to achieve torque-free control, and the interleaved phase currents control method is introduced to reduce the charging current ripples. In isolation charging mode, the inner-stator is powered by the utility grid to drive the middle-rotor, and converts electrical energy into mechanical energy. While, the outer-stator is operated in generation mode to convert mechanical energy into electrical energy. The SDS-SRM is served as a rotating transformer to transfer the electric power to charge the battery with the galvanic isolation function. A low power proof-of-concept prototype platform is built and experiments are carried out to verify the effectiveness of the proposed integrated drive converter and the corresponding control schemes.

One of the most important part of a stand-alone photovoltaic system is energy management. This part uses several sensors to measure solar radiations, load power and battery energy to determine operation of the system components. Reduction the number of sensors and optimization of energy management system are two key steps to improve the efficiency and reliability of the system without additional cost. Hence, in this paper, a comprehensive energy management algorithm along with a complementary algorithm to estimate radiation under uniform and partial shading conditions is proposed. According to the system state, the energy management algorithm commands to activate/ deactivate a part or entire of photovoltaic system which can increase the system lifetime and thus increase the system reliability. Another feature of the proposed algorithm is a new mode in the absence of radiation for parallelizing and sharing the battery’s current to the load by unidirectional and bidirectional converters. Furthermore, a sensor-less complementary algorithm to determine the amount of radiation and also the partial shading condition is proposed. To prove the feasibility of the proposed algorithms, the system was simulated and constructed. The simulation and experimental results are found closely comparable and confirm the effectiveness of the proposed methods.

Energy management strategy (EMS) of hybrid energy storage systems has an essential mission of ensuring safety, enhancing reliability and improving system efficiency. This paper focuses on optimizing sizing of HESS and parameters of EMS simultaneously. Firstly, an improved model is employed in adaptive predictive model control (AMPC). Secondly, in order to minimize the cost of supercapacitors and the capacity degradation of batteries at the same time, the multiple objective optimization problems are solved by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The nine variables are decided including seven parameters of AMPC and series-parallel number of supercapacitors. Subsequently, the support vector machine combined with the gray wolf optimization algorithm (GWO-SVM) is employed to enhance the applicability of EMS. Finally, in order to prove the superiority of the proposed EMS, a combined cycle and a real cycle are performed for validation. The results illustrate that the proposed method can improve the efficiency of the system and enhance the life span of the battery. Comparing with AMPC-only, the total energy loss can be reduced by at least 17.9%. A reduction of not less than 8.8% in RMS of battery current is obtained. The Ah-throughput can be reduced by at least 2.5%. In further discussions, the superiority of MOEA/D and GWO-SVM is proved by comparing with other methods.

High temporal-spatial resolution measurement of the air temperature is important for human health and research on the urban heat island. Since conventional methods can hardly meet the demands, a novel measurement of the air temperature has been proposed by using the battery temperature sensor of the smartphone, which can provide a refined data in a crowdsourcing way. Firstly, a heat transfer retrieval model among the air temperature, battery temperature, and the pocket of the clothing or the desk is established. Then coefficients of the retrieval model are obtained by the dataset measured through the smartphone and the thermometer in the linear fitting method. Finally, we retrieve the air temperature in a specific site with time resolution of one minute based on the retrieval model by three independent smartphones. Retrieval air temperature of four models strongly correlates to the true value, indicating that the retrieval model can sense the variation of the air temperature. Meanwhile, root mean square errors of the 1st and 4th model are 0.35 and 0.45∘, implying the high retrieval accuracy. The results suggest that the retrieval model can provide a short timescale (minute) and robust estimation of the air temperature by smartphone temperature sensors.

Lithium-ion capacitor (LIC), which combines the advantages of lithium-ion battery (LIB) and electrical double layer capacitor (EDLC), has a rapid development during last decade, however, the poor low temperature performance still limits its application. In this paper, three electrolyte additives including vinylene carbonate (VC), fluoroethylene carbonate (FEC) and 1,3,2-dioxathiolane 2,2-dioxide (DTD) have been utilized and their effects on the rate performance of hard carbon (HC) anode of LIC at various temperatures ranging from 25°C to -40°C have been well evaluated. The cell containing FEC shows the best rate performance at various temperatures and has the charge and discharge capability even at -40°C. For HC anode, the charge transfer impedance (RCT) increases exponentially at low temperature, while the equivalent series resistance (Rs) and the impedance of solid electrolyte interface (SEI) increase relatively few. At low temperatures, the effect of FEC may be mainly reflected in its effect on the charge transfer process.

The population of electric vehicles (EVs) has grown rapidly over the past decade due to the development of EV technologies, battery materials, charger facilities, and public charging services. Many governments have implemented plans to ban fossil fuel vehicles considering the significance of EVs in reducing greenhouse gas emissions. However, due to the battery material characteristics and charger power limitations, the EV charging process requires more time than is needed to fill a non-EV with fuel at a gasoline station, causing drivers to experience range anxiety and impeding the promotion of EVs. Hence, the battery swapping station (BSS) model has been proposed as an alternative method. Recently, researchers have studied the BSS approach by proposing various operation systems and optimization methods, and BSS service operators have successfully implemented swapping at commercial and private stations. This paper reviews the state-of-the-art BSS literature and business models, where the BSS offers a recharged battery to an incoming EV with a low state-of-charge. First, four operation modes are presented: a single BSS, multiple BSSs, an integrated BSS and battery charging station (BCS), and multiple BSSs and BCSs. Then, the BSS decision scenarios are surveyed in relation to five operational areas, i.e., charging schedule, service policy, construction and planning, dispatching and routing, and power management, where the scenarios are compared in terms of the BSS mode, decision maker, EV category, number of battery types, vehicle to grid, and focus and objective. Finally, the survey concludes with a discussion of several future research directions for EV BSSs.

The fast decomposition of peroxides (M2O2, where M = H+, Li+) at room temperature has gained increased attention given its relevance, from propulsive systems to metal-O2 conversion batteries. This study describes, by a rational design of experiment, the influence of selected parameters such as: pH, initial concentration of K2CO3, precipitate aging, precipitate washing, and thermal treatment for the hydrotalcite co-precipitation, characterizing it for each synthesis variable. The results show that hydrotalcites can efficiently be converted in Co-Mn-Al spinels and as a result, a new highly active Co2Mn0.5Al0.5O4 spinel oxide was synthesized after thermal treatment. Its activity and mechanical resistance were tested in a micro-thruster reactor where it showed total decomposition (> 98%) of H2O2 and in a Li-O2 battery, with increased cyclability, been able to reduce the charging potential in ~200 mV. These encouraging results enhance the importance not only of this class of material, but also, the importance of robust synthesis methodologies for reproducible results.

Lithium–sulfur (Li–S) battery is labeled as a promising high-energy-density battery system, but some inherent drawbacks of sulfur cathode materials using relatively complicated techniques impair the practical applications. Herein, an integrated approach is proposed to fabricate the high-performance rGO/VS4/S cathode composites through a simple one-step solvothermal method, where nano sulfur and VS4 particles are uniformly distributed on the conductive rGO matrix. rGO and sulfiphilic VS4 provide electron transfer skeleton and physical/chemical anchor for soluble lithium polysulfides (LiPS). Meanwhile, VS4 could also act as an electrochemical mediator to efficiently enhance the utilization and reversible conversion of LiPS. Correspondingly, the rGO/VS4/S composites maintain a high reversible capacity of 969 mAh/g at 0.2 C after 100 cycles, with a capacity retention rate of 82.3%. The capacity fade rate could lower to 0.0374% per cycle at 1 C. Moreover, capacity still sustains 795 mAh g–1 after 100 cycles in the relatively high-sulfur-loading battery (6.5 mg/cm2). Thus, the suggested method in configuring the sulfur-based composites is demonstrated a simple and efficient strategy to construct the high-performance Li–S batteries.

Batteries used for providing backup power in renewable energy sources (RES) fed standalone power systems often suffer from a limited lifetime because of high charging and discharging currents arising from sudden changes in load and/or generation. To prevent this, supercapacitors are used to supply the high-frequency power surges. In this paper, a new way of interfacing Hybrid Energy Storage Systems (Battery+ Supercapacitor) with RES and loads by employing a Current Fed Dual Active Bridge converter based Multi-Port Converter (MPC) is proposed. The proposed approach enables the MPC to regulate the load voltage while tracking the maximum power point (MPP) of the RES and protecting the battery from transients arising from variations in load and/or renewable generation. The Supercapacitor is used for tracking MPP so that the transient changes in RES generation do not cause a sudden rise/fall in the battery current. The employed MPC structure is designed in such a way that most of the power processing takes place in one stage only resulting in lower losses. The number of switches is reduced by merging the power conversion stages, which helps in keeping the cost low. Experimental results on a laboratory prototype under various static and dynamic conditions are presented.

The battery thermal management (BTM) system plays an increasingly important role today in the safety of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as the battery capacity and power ratings keep growing. The BTM system is commonly coupled with the vehicle passenger cabin HVAC system. This integrated thermal system is a major onboard energy consumer, and its complexity brings challenges to its control. With the planned-ahead speed profile and the corresponding power trajectory obtained from the connected and automated vehicle (CAV) technology, predictive control makes a desirable option for the integrated system to maintain battery safety and passenger comfort while lowering energy consumption. However, in order to achieve both high accuracy and low cost in a wide control range, a very long prediction horizon and high sampling rate are both necessary. This will overwhelm the processing capacity of the onboard electronic control unit (ECU) if using conventional predictive control. In this paper, a two-stage predictive control strategy for the BTM and HVAC coupled system is proposed to solve this problem. In stage 1, based on the integrated cooling system efficiency features, a hierarchical and iterative dynamic programming (HIDP) scheme is designed to derive the optimal battery temperature trajectory to reach the set point at the end of the horizon with a modest computation burden. In stage 2, a control-oriented model is constructed for the cooling system and a model predictive controller (MPC) is accordingly built to track the trajectory from Stage 1 while enforcing the energy saving. A high set-point-tracking performance and as high as 10.61% energy saving for the cooling system in the UDDS cycle are verified by simulation results. The real-time implementation capability of the proposed strategy is demonstrated by the vehicle emulator experiments based on hardware in the loop (HIL) and a rapid control prototyping (RCP) platform.

The lithium dendrite growth is still a serious challenge and impeding the realistic applications of all-solid-state lithium batteries. In view of the amide containing sediment layer can be stable on lithium/cathodes, a composite polymer electrolyte with amide-based matrix is in-situ built on porous electrodes. With the introduction of amide, the polymer electrolyte presents excellent ability to inhibit lithium dendrite growth and makes the Li/Li symmetric battery stably work for 500 hours with a good ionic conductivity of 4.25 × 10−5 S/cm at 40 °C. The solid electrolyte also shows a wide electrochemical stable window and good interface contact with the porous cathode. Utilizing this composite polymer electrolyte, the all-solid-state Li/LiFePO4 battery shows an initial discharge capacity of 146.5 mAh/g at 0.1 C under 40 oC and remains 81.4% in 100 cycles. The polymer electrolyte also can present better properties after modification. These results demonstrate that the presented PA-based composite polymer electrolyte could be served as a good electrolyte candidate for all-solid-state lithium-ion batteries.

The position sensor is a major cost contributing factor of switched reluctance motor (SRM) drive and it is highly unreliable in case of submersible pumps. Therefore, this paper aims at the development of sliding mode observer (SMO) based position sensorless control for SRM drive for PV-battery standalone submersible pumps. Unlike look-up table-based methods or artificial intelligence-based algorithms, SMO-based algorithm offers simple and robust architecture and does not require any pre-stored magnetic data. In this paper, SMO algorithm employs instantaneous online calculation of phase inductance, constructed as a function of both rotor position and measured phase current and uses the derivative of the same to serve as an error function for the observer. Besides implementing SMO for position estimation, a two-phase excitation-based algorithm is integrated to SRM control to guarantee the unidirectional operation during starting. A laboratory-scale prototype is developed and the performance of the drive is discussed to justify its suitableness for submersible pumps.

With the increasing number of controllable devices in the grid, devices owned by different entities are supposed to cooperatively support the grid operation. To balance the various interests of the customers and the utility, this paper proposes a two-level scheduling algorithm to address voltage regulation by coordinating the customer-owned battery energy storage system (BESS) and utility-owned load tap changers (LTCs) in power distribution systems. The objective is to find the optimal operation scheduling for both BESSs and LTCs that simultaneously maximize the economic payback and satisfy system operation rules. For BESSs, a local optimization problem is formulated in a chance-constrained manner in major consideration of the economic performance. For the utility interest, a Markov decision process (MDP) and a computationally efficient reinforcement learning (RL) algorithm are utilized to solve the optimal tap setting problem. To coordinate BESSs and LTCs, a global voltage adjustment is applied. The effectiveness of the proposed algorithm is validated via simulations on the IEEE 13-node and 34-node distribution test feeders.

Remarkable Li-ion battery(LIB) anode materials need to have long cycle life and fast charge/discharge rate, however they are difficult to be realized in the monolayer anode materials. The monolayer β-Bi has the stiffness of only 33.0 N/m, thus the Bi/G heterostructure is proposed to improve the electronic and mechanical properties and to produce better LIB anode performance in this paper. The calculated results show that Bi/G heterostructure has excellent thermodynamic, dynamical and mechanical stability. The band gap is only 0.04 eV, which ensures remarkable electrical conductivity. In addition, the Bi/G heterostructure has higher stiffness (369.2 N/m) than that of monolayer β-Bi and graphene. The diffusion barrier (Ebarrier) of 0.32 eV and volume expansion ratio(VER) of only 4% can ensure the rapid transport of Li+ ions in the charge/discharge cycling process and long life of the LIB. These calculated theoretical results for describing the detail properties of Li storage and diffusion in the Bi/G heterostructure can supply adequate conclusive evidence for the prediction of remarkable properties of Bi/G heterostructure as an anode material for LIBs.

Fe-N-C structures have been considered as a candidate to replace noble metal catalysts towards oxygen reduction reaction (ORR) due to their excellent electrocatalytic activity and durability. Herein, a zinc-mediated synthesis strategy is proposed for N-doped graphitic porous carbon encapsulated uniform dispersed Fe3C nanoparticles coupled with atomically dispersed Fe-Nx moieties (NPC/Fe-N-C) derived from biomass coconut shell. The introduction of zinc species could be conductive to the dispersion of iron species and formation of porous structures. Density functional theory calculations demonstrate that the N-doped carbon coating structures can weaken the oxygen intermediates adsorption energy barrier of Fe3C. Besides, the graphitic carbon could promote the electron transfer during the electrochemical reaction. These special structures enable NPC/Fe-N-C to have excellent ORR activity with an Eonset of 1.0 V, which is much better than Pt/C. Furthermore, the zinc-air battery assembled by pairing NPC/Fe-N-C with a high-efficiency oxygen evolution reaction (OER) catalyst can continuously and stably operate a charge-discharge potential gap of 0.8 V at 10 mA/cm2 for more than 600 hours. More importantly, the assembled batteries could drive overall water splitting device, realizing the effective energy conversion.

Remote areas electrification is usually achieved through costly and eco-unfriendly diesel generator (DG) systems with additional cost-obstacles arising through diesel–fuel transportation. Hybridization of renewable energy systems (RESs) with DG units offers a dynamic approach of electrification for the remote areas. A techno-economic study is required to assess the viability of RES in such locations. This research work examines the case of Al-Shumaisi cluster, located in the west of Makkah Province, Kingdom of Saudi Arabia (KSA). This is where the Makkah transport company (MTC) is located. A techno-economic investigation is performed for a commercial load profile of MTC with a hybrid wind/DG/fuel cell/battery/electrolyzer system. In this study, two hybrid configurations are proposed: 1) wind/DG/FC/battery system; 2) wind/FC/battery system. Moreover, another configuration that uses only DG units is also investigated. A comparative study is achieved for the most optimal combination, depending on the gross present cost (GPC) and levelized cost of energy (LCOE) as the essential parameters. Furthermore, other financial parameters like operation and maintenance (O&M) costs, capital costs, and replacement costs are investigated. The well-recognized simulation tool, HOMER (Hybrid Optimization of Multiple Energy Resources) developed by the National Renewable Energy Laboratory, is used to fulfill this study. The sensitivity investigation is carried out by optimally scaling the configuration capacities as well as by monitoring the future changes like weather, load demand, and fuel price uncertainties. The results of the sensitivity investigation and HOMER simulation confirm that the wind/DG/FC/battery hybrid off-grid configuration is the most economic and eco-friendly solution with a total GPC of 7.045 million USD, COE of 0.271 USD/kWh, and CO2 emissions of 566 tons. This signifies the 13.84% reduction of net present cost compared to DG-based units, with a 64.2% decrease of pollutants and zero unmet load.

For power batteries used in the electric vehicle, accurate state-of-charge estimation is important. However, as one of the most commonly used estimation method, the least square support vector regression is hard to balance the accuracy and efficiency. To solve this problem, the fuzzy information granulation based on asymmetric Gaussian membership function is proposed to improve the utilization efficiency of the effective data and enhance the prediction accuracy of battery states. In addition, the performance of the proposed method is compared with that of other commonly used member -ship functions. In experiments, the dynamic stress test condition and the urban dynamometer driving schedule condition are used to verify the effectiveness. Compared with the most commonly used triangle membership function, the proposed method improves the accuracy of estimation by 6.47% and 2.18% under two current conditions, respectively.

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users' behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

State of health (SOH) is crucial to the maintenance of various kinds of energy storage systems, including power batteries. Relevant researches are mostly based on battery external information, such as current, voltage and temperature, which are susceptible to fluctuation and ultimately affects the SOH estimation accuracy. To solve these problems, a fast impedance calculation based battery SOH estimation method for lithium-ion battery is proposed from the perspective of electrochemical impedance spectroscopy (EIS). The relationship between EIS and state of charge (SOC) and that between EIS and degraded capacity are first studied by experimental tests. Some impedance features called health factors effectively indicating battery aging states are selected. Secondly, an improved fast Fourier transform (FFT) utilizing the conversion relationship between real and complex signals is proposed to realize online fast EIS acquisition. Compared with ordinary FFT, such treatments reduce computation complexity. Then, the SOH evaluation model is built by the extreme learning machine (ELM) with regularization mechanism, further reducing the computational burden. The relationship between health factors and aging capacity of batteries is established. Finally, an experimental bench is established. Results indicate that the SOH can be obtained within 35 s and the estimation errors are less than 2%.