Surface or bulk?:Real-time manganese dissolution detection in a lithium-ion cathode

The longevity of lithium-ion batteries is determined by the rate of chemical and electrochemical side reactions that limit their charge storage capacity. In particular, dissolution of transition metals from the cathode accelerates the blockage of LixC6 anodes, but few direct dissolution studies have been made to date. Although LiMn2O4 (LMO) has been frequently used as a model electrode for dissolution studies, the cause and nature of dissolution and dissolution-free states are still unclear. By online inductively coupled plasma analysis, we detect dissolution from LMO electrodes in real time to reveal the role of surface versus bulk structure effects, electrode potential and degree of lithiation on Mn dissolution. We find that fully lithiated LMO, with an average Mn redox state of 3.5, readily dissolves when brought in contact with 0.2 M Li2SO4, but that on initial charging a dissolution–passivation event preceding delithiation abruptly stops further detectable dissolution, until well past fully delithiated λ-MnO2. Dissolution reactivates on returning to the initial potential of pristine LMO, and increases exponentially in the overlithiation region. Our results provide access to much more detailed dissolution information than post-mortem battery analysis allows, enabling targeted materials screening and informing best practices in charging/discharging profiles. In particular, our data suggests that suitable potential conditioning of electrodes may mitigate dissolution, as an alternative or additional measure to the use of protective surface films or incorporation of dopants

Numerical simulations of cyclic voltammetry for lithium-ion intercalation in nanosized systems:finiteness of diffusion versus electrode kinetics

The voltammetric behavior of Li(+)intercalation/deintercalation in/from LiMn(2)O(4)thin films and single particles is simulated, supporting very recent experimental results. Experiments and calculations both show that particle size and geometry are crucial for the electrochemical response. A remarkable outcome of this research is that higher potential sweep rates, of the order of several millivolts per second, may be used to characterize nanoparticles by voltammetry sweeps, as compared with macroscopic systems. This is in line with previous conclusions drawn for related single particle systems using kinetic Monte Carlo simulations. The impact of electrode kinetics and finite space diffusion on the reversibility of the process and the finiteness of the diffusion in ion Li / LiMn2O4(de)intercalation is also discussed in terms of preexisting modeling

Data-driven health estimation and lifetime prediction of lithium-ion batteries:A review

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in data-driven battery health estimation and prediction on all technology readiness levels