Layered lithium nickel-rich oxides, Li[Ni(1-x)M(x)]O(2) (M=metal), have attracted significant interest as the cathode material for rechargeable lithium batteries owing to their high capacity, excellent rate capability and low cost. However, their low thermal-abuse tolerance and poor cycle life, especially at elevated temperature, prohibit their use in practical batteries. Here, we report on a concentration-gradient cathode material for rechargeable lithium batteries based on a layered lithium nickel cobalt manganese oxide. In this material, each particle has a central bulk that is rich in Ni and a Mn-rich outer layer with decreasing Ni concentration and increasing Mn and Co concentrations as the surface is approached. The former provides high capacity, whereas the latter improves the thermal stability. A half cell using our concentration-gradient cathode material achieved a high capacity of 209 mA h g(-1) and retained 96% of this capacity after 50 charge-discharge cycles under an aggressive test profile (55 degrees C between 3.0 and 4.4 V). Our concentration-gradient material also showed superior performance in thermal-abuse tests compared with the bulk composition Li[Ni(0.8)Co(0.1)Mn(0.1)]O(2) used as reference. These results suggest that our cathode material could enable production of batteries that meet the demanding performance and safety requirements of plug-in hybrid electric vehicles.

High-energy cathode material for long-life and safe lithium batteries

https://hal-cea.archives-ouvertes.fr/cea-01791260/document

A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

Cycle-life model for graphite-LiFePO4 cells

Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems. Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

/pdf/data-driven-prediction-of-battery-cycle-life-before-capacity-2mi557zw0q.pdf

Data-driven prediction of battery cycle life before capacity degradation

Future generations of electric vehicles require driving ranges of at least 300 miles to successfully penetrate the mass consumer market. A significant improvement in the energy density of lithium batteries is mandatory while also maintaining similar or improved rate capability, lifetime, cost, and safety. The vast majority of electric vehicles that will appear on the market in the next 10 years will employ nickel-rich cathode materials, LiNi1–x–yCoxAlyO2 and LiNi1–x–yCoxMnyO2 (x + y < 0.2), in particular. Here, the potential and limitations of these cathode materials are critically compared with reference to realistic target values from the automotive industry. Moreover, we show how future automotive targets can be achieved through fine control of the structural and microstructural properties.

Nickel-Rich Layered Cathode Materials for Automotive Lithium-Ion Batteries: Achievements and Perspectives

Calendar- and cycle-life studies of advanced technology development program generation 1 lithium-ion batteries

Power fade and capacity fade resulting from cycle-life testing of Advanced Technology Development Program lithium-ion batteries

High-Power Battery Testing Procedures and Analytical Methodologies for HEV's

Novel testing procedures and analytical methodologies to assess the performance of hybrid electric vehicle batteries have been developed. Tests include both characterization and cycle life and/or calendar life, and have been designed for both Power Assist and Dual Mode applications. Analytical procedures include a battery scaling methodology, the calculation of pulse resistance, pulse power, available energy, and differential capacity, and the modeling of calendar and cycle life data. Representative performance data and examples of the application of the analytical methodologies including resistance growth, power fade, and cycle and calendar life modeling for hybrid electric vehicle batteries are presented.

Raymond A. Sutula

Papers

Calendar- and cycle-life studies of advanced technology development program generation 1 lithium-ion batteries

Power fade and capacity fade resulting from cycle-life testing of Advanced Technology Development Program lithium-ion batteries

High-Power Battery Testing Procedures and Analytical Methodologies for HEV's

PNGV Battery Testing Procedures And Analytical Methodologies For HEV's

Advanced Automotive Technologies Energy Storage R&D Programs at the U.S. Department of Energy-Recent Achievements and Current Status