Ageing diagnosis and time-frequency characteristic prediction for lithium ion batteries used in electric vehicles

Lithium ion batteries are critical energy storage devices for electric vehicles. They face the challenges of battery ageing. This study investigates the diagnosis of battery ageing mechanisms and proposes the methods to predict the time-frequency characteristics, such as the open circuit voltage and impedance spectra. The outcomes of this thesis can contribute to battery ageing evaluation and guide battery management strategies to avoid catastrophic failures and prolong battery life

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life (RUL). However, this task is challenging due to the diverse ageing mechanisms, various operating conditions, and limited measured signals. Although data-driven methods are perceived as a promising solution, they ignore intrinsic battery physics, leading to compromised accuracy, low efficiency, and low interpretability. In response, this study integrates domain knowledge into deep learning to enhance the RUL prediction performance. We demonstrate accurate RUL prediction using only a single charging curve. First, a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data. The parameters inform a deep neural network (DNN) to predict RUL with high accuracy and efficiency. The trained model is validated under 3 types of batteries working under 7 conditions, considering fully charged and partially charged cases. Using data from one cycle only, the proposed method achieves a root mean squared error (RMSE) of 11.42 cycles and a mean absolute relative error (MARE) of 3.19% on average, which are over 45% and 44% lower compared to the two state-of-the-art data-driven methods, respectively. Besides its accuracy, the proposed method also outperforms existing methods in terms of efficiency, input burden, and robustness. The inherent relationship between the model parameters and the battery degradation mechanism is further revealed, substantiating the intrinsic superiority of the proposed method.</p

Exploiting domain knowledge to reduce data requirements for battery health monitoring

Rechargeable batteries are becoming increasingly significant in decarbonising the world. For their widespread usage, to monitor and predict the battery health status has been essential. Although machine learning has the potential to tackle this issue, considerable degradation tests are required for model training, leading to prohibitive costs and labour. Here, we introduce a novel approach to constructing health monitoring models by fusing battery degradation knowledge with deep learning, using a substantially reduced amount of degradation data. We employ a lightweight and interpretable model to produce synthetic charging curves from highly limited realistic data. Subsequently, a transfer learning technique is implemented to train a convolutional neural network using both types of data and alleviate their gap. By employing only 8 realistic charging curves to develop the model, the method can precisely estimate the maximum and remaining capacities from 300 mV charging segments. The root mean square errors for these estimations are below 12.42 mAh. Additional 50 validation cases confirm that the proposed method can not only reduce the required degradation data but also shorten the input window length. Furthermore, it can be generalised and applied to different battery types under different operating conditions. This work highlights the promise of employing domain expertise to significantly decrease the amount of battery testing required for monitoring battery health

Rail-to-Rail MoS₂ Inverters

Two-dimensional semiconductors are considered as promising candidates for future electronic circuits thanks to the atomic thickness and no dangling bond surface. Additionally, as one of the most fundamental logic gates, high-performance inverters are crucial for integrated circuits. Here we design rail-to-rail MoS2 inverters by using bilayer MoS2 and MoO3 doped monolayer MoS2 transistors as load and driver transistors, respectively. The inverters exhibit a good rail-to-rail operation with a switching threshold voltage VM ≈ 2 V at VDD = 4 V, a high peak gain of 344 V/V, and a large noise margin NM ≈ 0.98 × (VDD/2)

Scaling of MoS₂ Transistors and Inverters to Sub-10 nm Channel Length with High Performance

Two-dimensional (2D) semiconductors such as monolayer molybdenum disulfide (MoS2) are promising building blocks for ultrascaled field effect transistors (FETs), benefiting from their atomic thickness, dangling-bond-free flat surface, and excellent gate controllability. However, despite great prospects, the fabrication of 2D ultrashort channel FETs with high performance and uniformity remains a challenge. Here, we report a self-encapsulated heterostructure undercut technique for the fabrication of sub-10 nm channel length MoS2 FETs. The fabricated 9 nm channel MoS2 FETs exhibit superior performances compared with sub-15 nm channel length including the competitive on-state current density of 734/433 μA/μm at VDS = 2/1 V, record-low DIBL of ∼50 mV/V, and superior on/off ratio of 3 × 107 and low subthreshold swing of ∼100 mV/dec. Furthermore, the ultrashort channel MoS2 FETs fabricated by this new technique show excellent homogeneity. Thanks to this, we scale the monolayer inverter down to sub-10 nm channel length