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We have simulated the effect of changing the end groups in BTP core with five organic units of 1,3-Indandione (IN), 2-thioxothiazolidin-4-one (Rhodanine), propanedinitrile (Malononitrile), (2-(6-oxo-5,6-dihydro-4H-cyclopenta[c]thiophen-4-ylidene)malononitrile) (CPTCN) and 2-(3-oxo-2,3-dihydroinden-1-ylidene (IC), and two halogenated units of (4F) IC and (4Cl) IC on the optical and photovoltaic properties of the BTP DA’D core molecular unit. Thus modified, seven molecular structures are considered and their optical properties, including HOMO and LUMO energies and absorption spectra are simulated in this paper. On the basis of HOMO and LUMO energies, it is found that two of the seven molecules, BTP-IN and BTP-Rhodanine, can act as donors and the other four, BTP-(4F) IC, BTP-(4Cl) IC, BTP-CPTCN and BTP-IC, as acceptors in designing bulk heterojunction (BHJ) organic solar cells (OSCs). Using these combinations of donors and acceptors in the active layer, eight BHJ OSCs, such as BTP-IN: BTP-(4F) IC, BTP-IN: BTP-(4Cl) IC, BTP-IN: BTP-CPTCN, BTP-IN: BTP-IC, BTP-Rhodanine: BTP-(4F) IC, BTP-Rhodanine: BTP-(4Cl) IC, BTP-Rhodanine: BTP-CPTCN and BTP-Rhodanine: BTP-IC, are designed, and their photovoltaic performance is simulated. The photovoltaic parameters Jsc, Voc and FF for all eight BHJ OSCs and their power conversion efficiency (PCE) are simulated. It is found that the BHJ OSC of the BTP-IN: BTP-CPTCN donor–acceptor blend gives the highest PCE (14.73%) and that of BTP-Rhodanine: BTP-(4F) IC gives the lowest PCE (12.07%). These results offer promising prospects for the fabrication of high-efficiency BHJ OSCs with the blend of both donor and acceptor based on the same core structure.

Many problems arise in the operation of photovoltaic systems. Each of these problems affects the operation of photovoltaic systems by reducing the power of the entire system. Some problems can be avoided during the design of photovoltaic systems. For example, when designing photovoltaic systems, it is possible to eliminate the shading of photovoltaic panels from surrounding objects. It is also necessary to look at the shading from neighboring photovoltaic panels when designing photovoltaic systems. It is necessary to calculate the inclination of the sunlight based on the position of the sun and calculate the distance between the two panels accordingly. However, some problems appear during the operation of photovoltaic systems. These problems can be partially eliminated. The magnitude of the series resistance can be eliminated by reducing the transition resistance throughout the system. The paper describes these problems and their possible solutions in practice. In the practical part, we focused on simulations and experiments in the field of photovoltaic systems (PV). The simulations focused on both the influence of temperature and the series resistance of photovoltaic panels on the production of electricity. The experiments were focused on the influence of various faults/defects on the power and V-A characteristics of photovoltaic panels connected in strings. The paper also discusses the impact of bypass diodes on the operation of photovoltaic systems and determines the need to use bypass diodes.

Anaerobic digestion (AD) can produce renewable energy and reduce carbon emissions, but the energy conversion efficiency is still limited in some waste streams. This study tested the effect of applied voltage removal for microbial electrolysis cells (MECs) treating primary sewage sludge. Two MECs were operated in parallel: a MEC-0.3 V with an applied voltage of 0.3 V and a MEC-OCV with open circuit voltage. Both reactors were inoculated with seed sludge originating from a MEC at 0.3 V applied voltage, and three batch cycles were operated for 36 d. The methane production of the MEC-OCV was 3759 mL/L in the first cycle and 2759 mL/L in the second cycle, which was similar (105% and 103%, respectively) to that of the MEC-0.3 V. However, in the third cycle, the methane production of the MEC-OCV (1762 mL/L) was 38.8% lower than that of the MEC-0.3 V (4545 mL/L). The methane contents in the biogas were 68.6–74.2% from the MEC-OCV, comparable to those from the MEC-0.3 V (66.6–71.1%). These results indicate that not only the MEC-0.3V but also the MEC-OCV outperformed AD in terms of methane yield and productivity, and the promotion using MEC-derived inoculum persisted equally with the MEC-OCV for two batch cycles after removing the applied voltage. Therefore, a MEC operation with cycled power supply may be beneficial in reducing the electric energy usage and improving the biogas production performance, compared to conventional AD.

Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%.

The mapping between open circuit voltage (OCV) and state of charge (SOC) is critical to the lithium-ion battery management system (BMS) for electric vehicles. In order to solve the poor accuracy in the local SOC range of most OCV models, an OCV model fusion method for SOC estimation is proposed. According to the characteristics of the experimental OCV–SOC curve, the method divides SOC interval (0, 100%) into several sub-intervals, and respectively fits the OCV curve segments in each sub-interval to obtain a corresponding number of OCV sub-models with local high precision. After that, the OCV sub-models are fused through the continuous weight function to obtain fusional OCV model. Regarding the OCV curve obtained from low-current OCV test as the criterion, the fusional OCV models of LiNiMnCoO2 (NMC) and LiFePO4 (LFP) are compared separately with the conventional OCV models. The comparison shows great fitting accuracy of the fusional OCV model. Furthermore, the adaptive cubature Kalman filter (ACKF) is utilized to estimate SOC and capacity under a dynamic stress test (DST) at different temperatures. The experimental results show that the fusional OCV model can effectively track the performance of the OCV–SOC curve model.

Lithium-ion batteries are deployed in a range of modern applications. Their utilization is evolving with the aim of achieving a greener environment. Batteries are costly, and battery management systems (BMSs) ensure long life and proper battery utilization. Modern BMSs are complex and cause a notable overhead consumption on batteries. In this paper, the time-varying aspect of battery parameters is used to reduce the power consumption overhead of BMSs. The aim is to use event-driven processing to realize effective BMSs. Unlike the conventional approach, parameters of battery cells, such as voltages and currents, are no longer regularly measured at a predefined time step and are instead recorded on the basis of events. This renders a considerable real-time compression. An inventive event-driven coulomb counting method is then presented, which employs the irregularly sampled data information for an effective online state of charge (SOC) determination. A high energy battery model for electric vehicle (EV) applications is studied in this work. It is implemented by using the equivalent circuit modeling (ECM) approach. A comparison of the developed framework is made with conventional fixed-rate counterparts. The results show that, in terms of compression and computational complexities, the devised solution surpasses the second order of magnitude gain. The SOC estimation error is also quantified, and the system attains a ≤4% SOC estimation error bound.

The performances of Intermediate Temperature Solid Oxide Fuel Cell (IT-SOFC) anode-supported planar cells with a 10 cm2 active surface were studied versus the combination of cathode thickness and the presence of an Anode Functional Layer (AFL). The temperature range was 500 to 650 °C, and Gd0.1Ce0.9O2−x (GDC) was used as the electrolyte material, Ni-GDC as the anode material, and La0.6Sr0.4Co0.2Fe0.8O3−d (LSCF48) as the cathode material. The power density, conductivity, and activation energy of different samples were determined in order to investigate the influence of the cathode thickness and AFL on the performance. These results showed an improvement in the performances when the AFL was not present. The maximum power density reached 370 mW·cm−2 at 650 °C for a sample with a cathode thickness of 50 µm and an electrolyte layer that was 20 µm thick. Moreover, it was highlighted that a thinner cathode layer reduced the power density of the cell.

The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.

The open circuit voltage of lithium ion batteries in equilibrium state, as a vital thermodynamic characteristic parameter, is extensively studied for battery state estimation and management. However, the time-consuming relaxation process, usually for several hours or more, seriously hinders the widespread application of open circuit voltage. In this paper, a novel voltage relaxation model is proposed to predict the final open circuit voltage when the lithium ion batteries are in equilibrium state with a small amount of sample data in the first few minutes, based on the concentration polarization theory. The Nernst equation is introduced to describe the evolution of relaxation voltage. The accuracy and effectiveness of the model are verified using experimental data on lithium ion batteries with different kinds of electrodes (LiCoO2/mesocarbon-microbead and LiFePO4/graphite) under different working conditions. The validation results show that the presented model can fit the experimental results very well and the predicted values are quite accurate by taking only 5 min or less. The satisfying results suggest that the introduction of concentration polarization theory might provide researchers an alternative model form to establish voltage relaxation models.

This paper proposes a new maximum power point tracking (MPPT) algorithm for thermoelectric generators (TEG). The new-presented method is based on implementing an indirect open circuit voltage detection and short circuit current estimation methods, which will be used to directly control the TEG interface power converter, resulting in reaching the maximum power point (MPP) in minimal number of steps. Two modes of operation are used in the proposed algorithm, namely the perturb and observe (P&O) method for fine-tuning and the transient mode for coarse tracking of the MPP during fast changes that occur to the temperature gradient across the structure. A novel voltage sensing technique as well is proposed in this work, to reduce the number of voltage sensors used to control and monitor the power converter. The proposed strategy employs a novel approach to sense two different voltages using the same voltage sensor. The input and output voltage information is collected from an intermediate point in the converter. The reconstructed voltages are used in the control loops as well as for monitoring the battery output or load voltages. Simulation and experimental results are provided to validate the effectiveness of the proposed algorithm and the sensing technique.

Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.