A Review of Mechanical and Chemical Sensors for Automotive Li-Ion Battery Systems

Functional Optical Fiber Sensors Detecting Imperceptible Physical/Chemical Changes for Smart Batteries

The battery technology progress has been a contradictory process in which performance improvement and hidden risks coexist. Now the battery is still a "black box", thus requiring a deep understanding of its internal state. The battery should "sense its internal physical/chemical conditions", which puts strict requirements on embedded sensing parts. This paper summarizes the application of advanced optical fiber sensors in lithium-ion batteries and energy storage technologies that may be mass deployed, focuses on the insights of advanced optical fiber sensors into the processes of one-dimensional nano-micro-level battery material structural phase transition, electrolyte degradation, electrode-electrolyte interface dynamics to three-dimensional macro-safety evolution. The paper contributes to understanding how to use optical fiber sensors to achieve "real" and "embedded" monitoring. Through the inherent advantages of the advanced optical fiber sensor, it helps clarify the battery internal state and reaction mechanism, aiding in the establishment of more detailed models. These advancements can promote the development of smart batteries, with significant importance lying in essentially promoting the improvement of system consistency. Furthermore, with the help of smart batteries in the future, the importance of consistency can be weakened or even eliminated. The application of advanced optical fiber sensors helps comprehensively improve the battery quality, reliability, and life.

Electronic properties of lithium-ion battery cathodes studied in ion-gated transistor configuration

Electronic and ionic transport governs lithium-ion battery (LIB) operation. The in operando study of electronic transport in lithium-ion transition metal oxide (LMOx) cathodes at different states of charge enables the evaluation of the state of health of LIBs and the optimization of their performance. We report on electronic transport in LIB cathode materials at different states of charge controlled in operando in ion-gated transistor (IGT) configuration. We considered LiNi_0.5Mn_0.3Co_0.2O₂ (NMC532)- and LiMn_1.5Ni_0.5O₄ (LNMO)-based composite materials formulated like in conventional LIB cathodes and operated in the organic electrolyte LP30 (1M LiPF₆ in ethylene carbonate:dimethyl carbonate 1:1 v/v). NMC532- and LNMO-based cathode materials were used as the transistor channel materials and LP30 as the ion gating medium. Beyond its impact on the field of LIBs, our work advances the design of novel devices based on mixed ionic and electronic transport, including neuromorphic computing.

Study on Co-Estimation of SoC and SoH for Second-Use Lithium-Ion Power Batteries

Lithium-ion batteries are an ideal power supplier for electric vehicles (EVs) due to their high-power density and wide operating voltage, but their performance decays to 80% before retirement from EVs. Nevertheless, they still have a particular use value after decommissioning, so recycling the retired power battery in cascade can be considered. Therefore, accurate estimation of battery state-of-charge (SoC) and state-of-health (SoH) is crucial for extending the service life and echelon utilization of power lithium-ion battery packs. This paper proposes a comprehensive co-estimation scheme of battery SoC/SoH for the second-use of lithium-ion power batteries in EVs under different cycles using an adaptive extended Kalman filter (AEKF). First, according to the collected battery test data at different aging cycle levels, the external battery characteristics are analyzed, and then a cycle-dependent equivalent circuit model (cECM) is built up. Next, the parameter estimation of this battery model is performed via a recursive least square (RLS) algorithm. Meanwhile, the variations in internal battery parameters of the cycle numbers are fitted and synthesized. Moreover, validation of the estimated parameters is further carried out. Based on this enhanced battery model, the AEKF algorithm is utilized to fulfill battery SoC/SoH estimation simultaneously. The estimated results of SoC/SoH are obtained for a LiCoO2 cell in the case of CCC (constant current condition) under different cycle times. The results show that this proposed co-estimation scheme can predict battery SoC and SoH well, wherein the peak values of the SoC errors are less than 2.2%, and the peak values of SoH, calculated by the estimated capacity and internal resistance, are less than 1.7% and 2.2%, respectively. Hence, this has important guiding significance for realizing the cascade utilization of lithium-ion power batteries.