Detecting Abnormality of Battery Lifetime from First-Cycle Data Using Few-Shot Learning.

The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early-stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few-shot learning and using only the first-cycle aging data. Verified with the largest known dataset with 215 commercial lithium-ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance-based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via "big data" analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost-benefit, and improved environmental friendliness.

Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate.

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.

Conic Input Mapping Design of Constrained Optimal Iterative Learning Controller for Uncertain Systems.

In this article, we study the optimal iterative learning control (ILC) for constrained systems with bounded uncertainties via a novel conic input mapping (CIM) design methodology. Due to the limited understanding of the process of interest, modeling uncertainties are generally inevitable, significantly reducing the convergence rate of the control systems. However, huge amounts of measured process data interacting with model uncertainties can easily be collected. Incorporating these data into the optimal controller design could unlock new opportunities to reduce the error of the current trail optimization. Based on several existing optimal ILC methods, we incorporate the online process data into the optimal and robust optimal ILC design, respectively. Our methodology, called CIM, utilizes the process data for the first time by applying the convex cone theory and maps the data into the design of control inputs. CIM-based optimal ILC and robust optimal ILC methods are developed for uncertain systems to achieve better control performance and a faster convergence rate. Next, rigorous theoretical analyses for the two methods have been presented, respectively. Finally, two illustrative numerical examples are provided to validate our methods with improved performance.

A Secure Control Learning Framework for Cyber-Physical Systems Under Sensor and Actuator Attacks.

In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.

Effects of azo dye on simultaneous biological removal of azo dye and nutrients in wastewater.

The potential disrupting effects of Azo dye on wastewater nutrients removal deserved more analysis. In this study, 15 days exposure experiments were conducted with alizarin yellow R (AYR) as a model dye to determine whether the dye caused adverse effects on biological removal of both the dye and nutrients in acclimated anaerobic-aerobic-anoxic sequencing batch reactors. The results showed that the AYR removal efficiency was, respectively, 85.7% and 66.8% at AYR concentrations of 50 and 200 mg l-1, while higher AYR inlet (400 mg l-1) might inactivate sludge. Lower removal of AYR at 200 mg l-1 of AYR was due to the insufficient support of electron donors in the anaerobic process. However, the decolorized by-products p-phenylenediamine and 5-aminosalicylic were completely decomposed in the following aerobic stage at both 50 and 200 mg l-1 of AYR concentrations. Compared with the absence of AYR, the presence of 200 mg l-1 of AYR decreased the total nitrogen removal efficiency from 82.4 to 41.1%, and chemical oxygen demand (COD) removal efficiency initially decreased to 68.1% and then returned to around 83.4% in the long-term exposure time. It was also found that the inhibition of AYR, nitrogen and COD removal induced by a higher concentration of AYR was due to the increased intracellular reactive oxygen species production, which caused the rise of oxidation-reduction potential value and decreased ammonia monooxygenase and nitrite oxidoreductase activities.