Mitigating thermal runaway hazard of high-energy lithium-ion batteries by poison agent

Lithium-ion batteries with high-energy density are extensively commercialized in long-range electric vehicles. However, they are poor in thermal stability and pose fire or explosion, which has attracted the global attention. This study describes a new route to mitigate the battery thermal runaway(TR) hazard by poison agents. First, the self-destructive cell is built using the embedded poison layer. Then, the poisoning mechanism and paths are experimentally investigated at the material, electrode, and cell levels. Finally, the proposed route is verified by TR tests. The results show the TR hazard can be significantly reduced in the self-destructive cell based on a new reaction sequence regulation. Specifically, the maximum temperature of the self-destructive cell is more than 300℃ lower than that of the normal cell during TR. The drop in maximum temperature can reduce total heat release and the probability of TR propagation in the battery system, significantly improving battery safety.

Development of a roll-to-roll high-speed laser micro processing machine for preparing through-holed anodes and cathodes of lithium-ion batteries

Aiming to improve the battery performance of lithium-ion batteries(LIBs),modification of the cathodes and anodes of LIBs using laser beams to prepare through-holes,non-through-holes or ditches arranged in grid and line patterns has been proposed by many researchers and engineers.In this study,a laser processing system attached to rollers,which realizes this modification without large changes in the present mass-production system,was developed.The laser system apparatus comprises roll-to-roll equipment and laser equipment.The roll-to-roll equipment mainly consists of a hollow cylinder with openings on its circumferential surface.Cathode and anode electrodes for LIBs are wound around the cylinder in the longitudinal direction of the electrodes.A pulsed beam reflected from the central axis of the cylinder can continuously open a large number of through-holes in the thin electrodes.Through-holes were formed at a rate of 100000 holes per second on lithium iron phosphate cathodes and graphite anodes with this system.The through-holed cathodes and anodes prepared with this system exhibited higher C-rate performance than nontreated cathodes and anodes.

Review of the structure and performance of through-holed anodes and cathodes prepared with a picosecond pulsed laser for lithium-ion batteries

To move the performance of lithium-ion batteries into the next stage,the modification of the structure of cells is the only choice except for the development of materials exhibiting higher performance.In this review paper,the employment of through-holing structures of anodes and cathodes prepared with a picosecond pulsed laser has been proposed.The laser system and the structure for improving the battery performance were introduced.The performance of laminated cells constructed with through-holed anodes and cathodes was reviewed from the viewpoints of the improvement of high-rate performance and energy density,removal of unbalanced capacities on both sides of the current collector,even greater high-rate performance by hybridizing cathode materials and removal of irreversible capacity.In conclusion,the points that should be examined and the problem for the through-holed structure to be in practical use are summarized.

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.