Nowadays, lithium-ion batteries are the most promising candidate as the power source for electric vehicles and energy storage system. Unfortunately, the performance of lithium-ion batteries tends to degrade and their capacity declines after a number of charging and discharging process. Most of the lithium-ion batteries are considered to replaced or discard when the capacity or voltage drops 20% of its initial state. In order to predict the state, prognosis algorithms are necessary to screen out highly degraded batteries. In this paper, life prediction system equipped with prognosis algorithm is proposed for estimating the battery health and predicting it remaining useful life based on the non-linear slope of charge-voltage curve. In particular, the prediction of state-of-charge(SOC) is modeled by Backpropagation Neural Network algorithm, which is conducted by 6 coefficients and the prediction of state-of-health (SOH) is modeled by particle-filter algorithm in form of a probability distribution, which is carried out by Arrhenius electrochemical modeling. 10 sets of lithium-ion batteries were initially prepared with 18650 cells and degraded by repeating the 100 times of charging and discharging process in order to compare the experimental data with the modeled data. Both SOC and SOH error rate tend to decline when the gather input experimental data is increased. The average SOC and SOH error rate of 10 samples at 100th cycle with 4 input cycle data was 6.75% and 3.30% respectively, which correspond to the algorithm efficiency above 95%. For conclusion, the particle filtering method for predicting the residual life (SOL) is not only more accurate than the prediction by simple curve fitting, but also is very effective because it reflects the lack of data and uncertainty due to measurement in the confidence interval. In addition, the remaining useful life prediction of SOC and SOH by particle filtering and backpropagation neural network algorithm is a method of continuously updating the deterioration model coefficient in real time based on the measured data of the battery and modeling data. Figure 1

We present a novel thermal energy harvesting system using triboelectric effect. Recently, there has been intensive research efforts on energy harvesting using triboelectric effect, which can produce surprising amount of electric power (when compared to piezoelectric materials) by rubbing or touching (i.e, electric charge by contact and separation) two different materials together. Numerous studies have shown the possibility as an attractive alternative with good transparency, flexibility and low cost abilities for its use in wearable device and smart phone applications markets. However, its application has been limited to only vibration source, which can produce sustained oscillation with maintaining contact and separation states repeatedly for triboelectric effect. Thus, there has been no attempt toward thermal energy source. The proposed approach can convert thermal energy into electricity by pairing triboelectric effect and active ferromagnetic materials The objective of the research is to develop a new manufacturing process of design, fabrication, and testing of a Magneto-Thermo-Triboelectric Generator (MTTG). The results obtained from the approach show that MTTG devices have a feasible power energy conversion capability from thermal energy sources. The tunable design of the device is such that it has efficient thermal capture over a wide range of operation temperature in waste heat.

Recently, novel concepts of electrochemical cells have been spotlighted for ultra-charge energy storage system. Among these cells, magnesium alloy (AZ31) air batteries have attracted much attention as promising energy conversion devices due to their high theoretical energy density, eco-friendly materials and low fabrication cost. Although AZ31–air battery is a primary battery, the AZ31–air battery can be re-used or recharged mechanically by replacing the consumed AZ31 anode and turbid electrolyte with a fresh AZ31 anode and electrolyte, refering to “re-fuelable” During the discharge process, the AZ31 anode is oxidized to ionized Mg, producing two electrons, while at the opposite carbon air-cathode, oxygen molecules pass through the gas diffusion layer and is then reduced to OH− by reaction with H2O and electrons. The theoretical voltage of the AZ31–air battery is 1.2 V and the specific energy density is 2.2 kW h kg−1. Though AZ31–air batteries have a relative high voltage and energy density, there are still scientific problems limiting their pilot scale application. The main issue of cell is the high polarization and low coulombic efficiency. These problems are caused by the corrosion of the metal anode arising from the reaction of metal ions and the electrolyte, and the sluggish by-product kinetics in the air-cathode. In this work, we combined Mg-air batteries with electrolyte flow system, called metal-air flow battery (MAFB) in order to enhance the discharge properties and lifetime by solving the sluggish by-product problems. The components of anode and electrolyte were AZ31 (magnesium alloy) as a fuel and 18wt% saline aqueous solution. The circulation of electrolyte flow efficiently reduces the coagulation of Mg(OH)2 by-product in the unit-cells. As a result, the anode surface efficiency has increased from 72 to above 90 % after the use of flow system. In addition, we have successfully design and developed the metal-air flow battery from unit-cell to rack system. The rack system is made up of 25 series of electrically wired unit-cells for modules and 4-level of modules with electrolyte flow system. The flow system conducted simultaneously in each unit-cells by electrolyte supply tank in the top and by-product filter tank in the bottom. From the rack system, a pilot scale MAFB with 400 pieces of unit-cells was developed and fabricated for ultra-large energy storage system. The 704 kWh of energy storage system demonstrated by 25s-16p electrical wiring with 4-rack flow systems. As a result, it is the first report about the MAFB using AZ31 anodes in pilot-scale energy storage system. It has shown a large potential for future energy conversion devices for smart grid applications. Figure 1