Battery Discharge Research Articles

Anomaly detection of LiFePO4 pouch batteries expansion force under preload force

Overcharging, overheating and other abnormal conditions are important causes of thermal runaway of Li-ion batteries, and a major safety issue for Li-ion batteries. Therefore, it is critical to detect these faults as soon as they are triggered. In this paper, the actual usage scenario of the battery was simulated by setting the appropriate preloading force. And the characteristics of the changes of the battery expansion force under abnormal conditions were studied by battery charging and discharging, overcharging, overdischarging and thermal runaway experiments. In particular, we proposed a fast and high confidence battery fault detection method by the characteristics of the expansion force change under various faults, which could provide early warning of multiple faults. This work provided a reliable method of detecting battery failures at an early stage and ensured process safety and reduced environmental pollution caused by lithium-ion battery accidents through reasonable fire prevention measures.

Pengaruh Perawatan Preventif Lifetime Baterai Lead Acid Untuk Persinyalan Kereta Api Listrik

Trains operate supported by many factors, one of which is signaling and telecommunications equipment. This equipment must get a continuous supply of electricity, so that train operations are safe and smooth. One way to protect the power supply is by installing a battery uninterrupted power supply (UPS), as a source of electrical energy storage as a backup. To keep the battery in optimal condition, 1 month, 3 months and 12 months preventive maintenance is carried out. Treatment 12 months, tested with the method coulomb counting with discharge against the battery up to 80% total wasted capacity, to get the value state of health battery. There are 54 batteries valve-regulated lead acid (VRLA) with a voltage of 2 V and a nominal capacity of 200 Ah, which is connected in series with the voltage cut off 1.85 V. The results of preventive maintenance are seen by testing discharge battery at station A, namely the SoH condition where all the batteries are above the value of 80%, while at station B there are 5 batteries with SoH values ​​of 78%, 80%, 58%, 74%, and 72%, so the batteries must be replaced at fifth batteries with SoH rating of 80% and below.

Techno-economic optimization of a sustainable system to cogenerate power and water for remote areas

Abstract In this study, a new water and power cogeneration plant, employing photovoltaic (PV) and photovoltaic–thermal (PVT) panels simultaneously, is designed and optimized for a village struggling to provide energy and potable water in Iran. The system includes a solar energy unit to generate clean electricity and heat, and a reverse osmosis unit to produce drinking water. Techno-economic optimization is performed by implementing a genetic algorithm, and a comprehensive water and energy management strategy is designed and presented in detail, expandable for future works. A new method, the logarithmic model, is used to calculate the depth of discharge (DOD) of lithium-ion batteries, which was previously a fixed and predetermined value in previous papers. Various indices, the constraints of the optimization process, are also introduced to measure the reliabilities of different units. The effects of the system components on total cost are investigated and a comprehensive sensitivity analysis is performed to find the best solution to increase the penetration of renewable-energy systems. The results reveal that considering the depth of discharge of batteries and water storage tank capacity as decision variables reduces the system’s life-cycle cost (by 5.1% for changeable DODs). Furthermore, the simultaneous use of PV and PVT panels decreases the life-cycle cost considerably by ≤19% compared with the use of only PVT panels. Additionally, the cost of the battery causes a decrease in the cost of electricity storage and the cost of producing and storing fresh water.

Optimization of the Cooling Performance of Symmetric Battery Thermal Management Systems at High Discharge Rates

An air cooling battery thermal management system (BTMS) is widely used in electric vehicles (EVs). In this work, numerical simulation is used to provide a greater understanding of the system efficiency of T-type symmetric air cooling BTMS (T-BTMS) and its coupled systems. For T-BTMS, the method of the coefficient of variation (MCV) is applied to evaluate the schemes with different inlet flow rates and battery clearances. It is found that optimum cooling performance and energy consumption are achieved at an internal clearance of 3 mm and an inlet air velocity of 6 m·s–1. Further improvements in cooling performance can be realized by introducing heat transfer fins into the module. Increasing the number of fins can improve cooling performance but results in higher energy consumption. To further improve the cooling performance at high battery discharge rates, a coupled system with air cooling and liquid cooling is proposed. It is found that, at high discharge rates, the coupled system gives better cooling performance and lower energy consumption compared to uncoupled systems. When the battery module is fully discharged at the rate of 4 C, the maximum temperature of the coupled system is below the critical value of 45 °C and the maximum temperature difference is less than 5 °C.

Modulation of the Discharge and Corrosion Properties of Aqueous Mg-Air Batteries by Alloying from First-Principles Theory

Mg-air batteries have been rapidly developed with the proposal of several corrosion inhibition strategies for magnesium anodes. However, researchers have been puzzled by the limited improvement in discharge voltage caused by alloying and the theoretical mechanism of alloying on corrosion. Here, a detailed thermodynamic study was carried out combined with the stepwise hydroxide-assisted mechanism for metal dissolution in alkaline media and the Mg7B (B is the alloying elements) alloy model using first-principles theory. The effects of alloying on the discharge voltage and corrosion of magnesium anodes were simulated for alloying elements intensively investigated. The results indicate that within the range of electrochemical polarization, the discharging of Mg-air batteries at high current densities has a large overpotential while the impact of alloying on the voltage of Mg-air batteries is less than 0.15 V at high current densities. Consequently, without considering other polarization reductions, alloying cannot profoundly change the discharge voltage of Mg-air batteries; contrarily, an increase in corrosion resistance will result in a decrease in voltage for elements with an electrode potential higher than magnesium.

Elman neural network-based temperature prediction and optimization for lithium-ion batteries

Reliable and precise temperature prediction is one of the most crucial challenges for improving battery performance and preventing thermal runaway. This paper uses a highly adaptive Elman neural network (Elman-NN) to construct a temperature prediction model for lithium-ion batteries in a metal foam aluminum thermal management system. Numerical modeling methods obtain experimental data sets for model training and testing. The input parameters of the neural network prediction model are ambient temperature, battery discharge rate, cooling air flow rate, and state of charge; the output parameters are the maximum, minimum, and average battery temperature. However, due to the limitations of the gradient descent algorithm, the training process of the Elman neural network tends to fall into local optimum solutions. To further improve the prediction accuracy, the Elman-NN structure was optimized using the PSO algorithm, and the model performance was tested and validated. Compared with the original Elman-NN, the hybrid PSO-Elman-NN has smaller MSE and MAE values, with a maximum reduction of 43% and 25%, respectively. For the three test conditions, the maximum predicted temperature difference does not exceed 1.5 K, and the temperature difference decreases further as the discharge time increases. Moreover, the hybrid model’s prediction accuracy is significantly improved, with the coefficients of determination ( R2) increasing by 1.736%, 0.706%, and 1.851%, respectively. The PSO-Elman-NN performed well in terms of compatibility and accuracy of the battery temperature prediction.

Joint Estimation of State of Charge and State of Health of Lithium Ion Battery

Abstract Overcharge and discharge of power battery not only increase the battery loss but also lead to fire and other accidents under harsh environmental conditions. Accurate estimation of battery parameters and status is an important reference in the battery management system to prevent battery overcharge and discharge. In this article, the following studies are carried out by focusing on the time separation scale and estimating parameters and state values online based on the improved particle filter: (1) The unscented transform and multi-innovation were applied to the particle filter to optimize the particle distribution and update the status value from the historical information, and the multi-innovation unscented particle filter was formed to estimate the state of battery charge. (2) Considering the influence of parameter variation on the estimation of battery state of charge (SOC). Due to the slow change characteristics of parameters and fast change characteristics of states, the parameters and states are jointly estimated from macro and micro time scales, respectively. The capacity change estimated by the unscented particle filter is used to characterize the battery health state, and finally, the joint estimation of battery SOC and state of health (SOH) is formed; (3) Three different working conditions are used to verify the algorithm. The joint algorithm accurately estimates the real-time changes of SOC and SOH, and the average error of SOC is less than 0.5%, which confirms the high accuracy of the joint algorithm.

F− competitive attack decomposing parasitic product Al(OH)3 of hydrogel-based Al-air battery

Wearable Al-air batteries are regarded as the potential power systems for flexible electronics due to the ultra-high capacity and energy density of Al-based materials. However, the battery failure caused by the accumulation of parasitic product Al(OH)3 upon the anode surface has hindered the commercialization. Herein, we report a polyacrylic acid hydrogel integrating KF and KOH (F@PAA), which decompose Al(OH)3 for ameliorating discharge performance of wearable Al-air battery. The ions channels upon the anode surface are dredged by a competitive attack of F− on Al-O, thus improving the battery durability. The results show that the binding of Al3+ with F− is more stable than that with O2−. The formed complex AlF63− corrodes the passivation layer, and then ensures the continuous anodic oxidation. When 1.0 M F− is introduced into F@PAA hydrogel, the effect of byproduct decomposition and battery discharge are optimal. Hence, A wearable Al-air battery using the proposed hydrogel achieves a maximum power density of 58.28 mW/cm2. A high capacity of 2199.10 mAh/g and anode efficiency of 73.80% for the battery can be obtained at 10 mA/cm2. Moreover, the key performance of the battery is improved by up to 104.08%, developing interface cleaning technology in wearable Al-air batteries.

Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid

Power quality is the main problem with the power system network. Poor electricity quality may cause disruptions and financial challenges for consumers. Additionally, it could cause electronic gadgets to overheat, be damaged, or operate inadvertently. Transformers and other components of the power distribution system may also overheat and experience core saturation. This study investigates potential problems with the quality of the electricity in a photovoltaic linked power system. This paper suggests a new optimization method for day-ahead trading and control in DC microgrid power management (MG). The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. Using the weighted sum approach, the multiobjective optimization problem is reduced to a single optimization problem. The analytical hierarchy process (AHP) method is then used to calculate the weight coefficients while accounting for each objective function’s preferences. Power balancing, high levels of renewable energy penetration, the most effective scheduling of battery charging and discharging, control of load curtailment, and the technical limitations of the system are all taken into account when evaluating the system’s performance in both grid-connected and standalone operation modes. The ant lion optimizer (ALO) technique is taken into account to tackle MOOD, comparing the effectiveness of the proposed method to other well-known heuristic optimization techniques. The simulation’s results demonstrate how effectively the proposed strategy can address the coordinated control and optimization dispatch difficulties. They also found that operating the MG system economically in grid-connected mode can save overall costs by roughly 4.70% compared to doing it in independent mode.

PV System with Battery Storage Using Bidirectional DC-DC Converter

With the increase in demand for generating power using renewable energy sources, energy storage and interfacing the energy storage device with the load has become a major challenge. Energy storage using batteries is most suitable for renewable energy sources such as solar, wind etc. A bi-directional DC-DC converter provides the required bidirectional power flow for battery charging and discharging mode. The duty cycle of the converter controls charging and discharging based on the state of charge of the battery and direction of the current. In this paper, a nonisolated bi-directional DC-DC converter is designed and simulated for energy storage in the battery and interfacing it with the DC grid. The power extracted from the solar panel during the daytime is used to charge the batteries through the DC-DC converter operating in buck mode and when solar power is unavailable, the battery discharges to supply power to DC load through the converter operating in boost mode. Solar arrays connected through a DC bus to a load. Due to the instantaneous changes of solar irradiance and temperature, maximum power point tracking (MPPT) is integrated in the inverter control. The technique of maximum power point tracking (MPPT) is used in photovoltaic systems to extract the maximum power. The most popular MPPT techniques are reviewed and studied, such as: Perturb and Observe, Increment of Conductance and control based on fuzzy logic (LF). The simulation is done in matlab/simulink and results are presented.

Learning Linear Temporal Properties for Autonomous Robotic Systems

The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized for execution speed. We apply it to the use-case of a robot moving in an unstructured environment as its battery discharges, both in simulation and in the real world. The results of our experiments confirm that our approach can learn temporal formulas explaining task failures in a case of practical interest.

A Total Optimization Model and the Evaluation of EV Taxi Operation and Electric Power Charge and Discharge

We propose a total optimization model of electric power networks for EV taxi services. Planning of charging and discharging of the EV batteries are optimized in conditions of not only the transportation demand but also the PV generation. This optimization problem is modeled into a mixed integer programming model, in which each EV movement in a day is expressed. The optimal solution obtained by this model can provide performance limits for the assumed system performance and will provide important information for the design policy of the system designer or relevant stakeholders. Through some numerical experiments using actual demand data, we show the potential of the target transportation services.

Grid Sychronization of Wind Based Microgrid

Abstract: This paper presents the methods for synchronization of a wind based micro grid with main grid. The wind power is generated using the permanent magnet brushless DC generator (PMBLDCG) and the MPPT (Maximum Power Point Tracking) is executed with P&O (Perturb & Observe) approach to extract the maximum power from the wind generation. This power is fed to the AC load using a voltage source inverter (VSI) through the battery bank (BSS) at DC link. The control algorithm operates in standalone mode and grid connected mode. It can switch between islanded and grid connected modes seamlessly according to the wind conditions. In standalone mode, this wind power is fed to the load through a VSI, during low wind conditions; the battery discharges itself to fulfill the load demand. After that, the grid is connected to give power to the load during low wind conditions, and for this, control algorithm operates in grid connected mode. Therefore, the control algorithm switches from voltage control to current control mode to provide smooth synchronization and vice versa at de-synchronization. A MATLAB Simulink model is developed to simulate the system performance during wind variations and load, and during synchronization and de-synchronization.

Loss Minimization of Distribution Systems by Coordinated Operation of Battery and EVs in the Presence of DGs

The rapidly increasing demand for electrical power and difficulties in providing the same using traditional power generating sources provide a motivation to integrate distributed generator (DG) in the radial distribution network. In this work, the optimal arrangement of DG using genetic algorithm (GA) is obtained based on fixed penetration level (PL). The state of charge (SoC) value for each hour before placing the battery based on the demand was estimated, then battery will be placed in the optimal location of the fixed capacity. This will help in reducing the power loss and simultaneous improvement of voltage profile of the network. Furthermore, electric vehicle (EV) is incorporated in the system. In the presence of E V , the loads are variable due to the charging and discharging of batteries in the EV. The variation of network power losses in the presence of EVs along with DGs and battery have been investigated on standard 33 and 69 bus systems for 2 different topologies and 2 different loading scenarios.

Vacancy and doping engineering of Ni-based charge-buffer electrode for highly-efficient membrane-free and decoupled hydrogen/oxygen evolution

The realization of the membrane-free two-step water electrolysis is particularly important yet challenging for the low-cost and large-scale supply of hydrogen energy. In this effort, Co-doped Ni(OH)2 nanosheets were successfully anchored onto the nickel foam (NF) substrate through the in-situ growth of metal-organic frame material and the subsequent alkali-etching technique. Using the well-regulated Co-doping Ni(OH)2@NF electrodes as a charge mediator, electrochemical hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) were decoupled on time scales, thus affording a membrane-free two-step route for H2 and O2 productions. In this architecture, the first HER process on the cathode could be maintained for 1300 s at a current of 100 mA, while the corresponding Ni(OH)2 charge mediator was simultaneously oxidized to NiOOH, with a decent cell voltage of 1.542 V. The subsequent OER process involved a reduction/regeneration of Ni(OH)2 (from NiOOH to Ni(OH)2) and an anodic O2-production, with an operating voltage of 0.291 V. Moreover, the Ni-Zn battery assembled through the combination of NiOOH and Zn sheet could replace the second step of OER to achieve the coupling of continuous H2-production and battery discharge, thus also providing a new way for hydrogen production without an external power supply. Experiment and theoretical calculations have shown that the cobalt-doping not only improved the conductivity of the charge-buffer electrode, but also shifted its redox potential cathodically and boosted the adsorption affinity of the buffer medium to OH– ions, both contributing to promoted HER and OER activity. Therefore, this decoupled water electrolysis device affords a promising pathway to support the efficient conversion of renewables to hydrogen.

Adaptive station-keeping strategy for wave gliders considering uncertain environmental disturbances

The wave glider (WG) is a new type of marine mobile observation platform, which has the ability to conduct long-term and large-scale ocean observations. Due to the characteristics of low speed and weak maneuverability, it is difficult to have a good control effect on the wave glider, especially in the case of large environmental disturbance. An adaptive station-keeping strategy integrated with a modified weather optimal positioning control (MWOPC) and an adaptive dead circle control (ADCC) is put forward for wave gliders in this paper, which can adaptively adjust the radius of the dead circle to resist time-varying environmental disturbance. More specifically, focusing on dynamically balancing the actuator energy consumption and station-keeping accuracy, a simplified battery discharge level function is designed to adjust the ratio gain between the actuator energy consumption and distance error in the ADCC. A closed-loop control is put forward with a dynamic model of the “Black Pearl” wave glider and a station-keeping strategy is given. The simulation and sea trial results show that the proposed station-keeping strategy can achieve good station-keeping effect under different environmental disturbances and adaptively change the energy consumption based on the battery charge level.

Battery Lifetime Extension in a Stand-Alone Microgrid With Flexible Power Point Tracking of Photovoltaic System

In stand-alone dc microgrids (dcMGs), battery energy storage systems (BESSs) are conventionally used for regulating the dc-link voltage, causing a continuous battery operation. Though operating the photovoltaic (PV) system at its maximum power point (MPP) yields minimum battery discharge current, the opposite is true for battery charging current. Therefore, reducing the battery charging current based on its state-of-charge (SoC) and the amount of available PV surplus power (which can be treated as virtually stored energy) is an opportunity for improving the battery life. The main objective of the control strategy proposed in this article is to prolong the battery lifetime by reducing the charging current and keeping the battery SoC at lower values if the PV power is enough to supply the loads. Additionally, the PV system is used as the primary asset to regulate the microgrid voltage. The dynamic performance of the proposed control strategy is validated with experimental tests under various operating conditions. Furthermore, its effectiveness in prolonging the battery lifetime is evaluated using an aging model of a lithium-ion (Li-ion) battery (without loss of generality) by simulated case studies.

Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm

With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health (SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation (Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity (IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%.

Assessing the Limits of Equivalent Circuit Models and Kalman Filters for Estimating the State of Charge: Case of Agricultural Robots

The battery State of Charge (SoC) is critical information to overcome agricultural robots’ limitations related to battery and energy management. Although several SoC estimation methods have been proposed in the literature, the performance of these methods has not been validated for different battery chemistries in agricultural mobile robot applications. Compared to previous work, this paper evaluates the limits of the SoC estimation using the RC model and the Thevenin model for a Lithium Iron Phosphate (LFP) battery and a Sealed Lead Acid (SLA) battery. This evaluation used a custom agricultural robot in a controlled indoor environment. Consequently, this work assessed the limitations of two ECM-based SoC estimation methods using battery packs, low-cost sensors and discharge cycles typically used in agricultural robot applications. Finally, the results indicate that the RC model is not suitable for SoC estimation for LFP battery; however, it achieved a mean absolute error (MAE) of 2.2% for the SLA battery. On the other hand, the Thevenin model performed properly for both chemistries, achieving MAE lower than 1%.

Propulsion and photovoltaic charging system parameter computation for an all-electric boat

ABSTRACT The study aims to evaluate system combinations including batteries and electric motors for the all-electric training ship and to develop a shore facility with photovoltaic solar panels for battery charging. To achieve the objectives, the ship’s power requirements were calculated using Savitsky and Wyman methods. Specifications of the propulsion units were decided by utilizing three different driving cycles and a battery discharge simulation for these cycles was ensured. Then, time series analyses forecasted the discharge durations, and lifespans of the batteries. Finally, economic performance comparison and the ranking of the configurations were provided. Results indicate that nominal battery capacities are detected as 21.39, 39.22, and 7.51 kWh, having 4.86, 9.13, and 11.53-hours’ usage range, and payback periods of the configurations are 8.19, 6.88, and 8.05 years for each driving cycle. The configuration designed regarding the third driving cycle is the most feasible according to the multi-criteria decision-making analysis.