Lithium-ion (Li-ion) batteries are the major type of battery used in a variety of everyday applications, including electric vehicles (EVs), mobile devices, and energy storage systems. Predicting the Remaining Useful Life (RUL) of Li-ion batteries is crucial for ensuring their reliability, safety, and cost-effectiveness in battery-powered systems. The materials used for the battery cathodes and their designs play a significant role in determining the degradation rates and RUL, as they lead to distinct electrochemical reactions. Unfortunately, RUL prediction models often overlook the cathode materials and designs to simplify the model-building process, ignoring the effects of these electrochemical reactions. Other reasons are that specifications related to battery materials may not always be readily available, and a battery might consist of a mix of different materials. As a result, the predictive models that are developed often lack generalizability. To tackle these challenges, this paper proposes a novel material-based Mixture-of-Experts (MoE) approach for predicting the RUL of batteries, specifically addressing the complexities associated with heterogeneous battery chemistries. The MoE is integrated into a probabilistic framework, called Multiple Non-crossing Quantile Mixture-of-Experts for Probabilistic Prediction (RUL-QMoE), which accommodates battery operational conditions and enables uncertainty quantification. The RUL-QMoE model integrates specialized expert networks for five battery types: LFP, NCA, NMC, LCO, and NMC-LCO, within a gating mechanism that dynamically assigns relevance based on the battery's input features. Furthermore, by leveraging non-crossing quantile regression, the proposed RUL-QMoE produces coherent and interpretable predictive distributions of the battery's RUL, enabling robust uncertainty quantification in the battery's RUL prediction. Trained on seven real-world datasets, the proposed RUL-QMoE achieves strong predictive performance across all battery types, with MAE = 65 (cycles), MAPE = 9.59%, RMSE = 100 (cycles), and R2=96.84%. Compared to traditional models like XGBoost, Random Forest, CNN, and LSTM, the proposed RUL-QMoE model consistently delivers lower RMSE and superior probabilistic insights, including survival probabilities and prediction intervals. The model has been integrated into our Battery AI platform in collaboration with Toyota Motor Engineering & Manufacturing North America, Inc., as part of a broader Battery Foundation Model initiative. This RUL-QMoE model will serve future Toyota EVs' users and battery system designers.

The economic operation of lithium-ion battery energy storage in electricity markets requires optimally balancing the tradeoff between maximizing the revenue from energy arbitrage and minimizing the capacity loss due to usage. This optimal balance can be achieved by incorporating the stress due to the depth of discharge and battery temperatures in the optimal dispatch framework. However, the stress models are nonlinear and the quantification of partial charge–discharge cycles requires the rainflow cycle counting algorithm, which does not have an analytical form. Considering the challenges, a set of physics-inspired sufficient conditions are developed to handle the nonanalytical form of therainflowalgorithm and to consider cell-level temperatures. The proposed stress cognizant optimal battery dispatch (SC-OBD) framework is applied to a battery participating in both the day-ahead and real-time balancing market. A model predictive control-based framework is proposed to handle uncertain electricity prices in the real-time market and to guarantee the fulfilment of day-ahead market commitments. The numerical results indicate that the proposed SC-OBD can efficiently utilize the cooling to reduce degradation with/without modifying the market-benchmark dispatch.

In order to enhance the diagnosis of turn-to-turn short circuit (TTSC) fault based on machine learning (ML), a novel method to decouple the demagnetization characteristics due to TTSC fault effect on magnet to improve the data integrity is presented in this article. The updated knee point of the magnet based on magnet temperature is obtained in real-time and a fault indicator indicative of the initialization of irreversible demagnetization is used to decouple the influence of rotor MMF from the magnetic field energy stored in the air-gap. An extensive set of data is obtained from analytical modelling of permanent magnet synchronous machine (PMSM) with TTSC fault based on winding function approach. The model fidelity is increased by incorporating the TTSC fault effect on magnet as a look-up-table obtained from finite element analysis. In addition to the simulation data, experimental data of PMSM with TTSC fault are used to train the machine learning algorithms. The results confirm that data integrity is improved and time-domain signal analysis are sufficient for training and diagnosis of TTSC fault based on ML algorithms.

Accurate prediction of the remaining useful life (RUL) of batteries plays a crucial role in ensuring their reliable operation and optimizing maintenance strategies. In this paper, we propose a censored-variational Gaussian Process (C-VGP) model for probabilistic RUL prediction, taking into account right-censored data. The performance of the C-VGP model is compared with the typical Gaussian process regression (GPR) and GPR with right-censored data removed (GPR*). Numerical experiments on battery testing datasets demonstrate that neglecting right-censored data in the GPR model leads to larger errors, while the GPR* model sacrifices information. In contrast, our proposed C-VGP model outperforms both models in terms of accuracy and robustness. It achieves a high coefficient of determination$R^{2}$of 93.56% even with a significant proportion of censored data (57.67%) in the training set. Moreover, the proposed C-VGP model enables time-varying conditional survival analyses, providing valuable insights into the probabilities of the battery's remaining life exceeding certain thresholds at different time points. The proposed model offers a comprehensive solution for accurate battery RUL prediction, encompassing the handling of right-censored data, capturing time-varying patterns, and providing reliable predictions. Its practicality and effectiveness can make it a valuable tool for battery management and maintenance applications.

State synchronization is crucial in matching digital twins to physical objects dynamically in order to have trustworthy modeling and reliable prediction. This work proposes two approaches to ensure state synchronization on the introduced Dual Digital Twin (DDT). DDT is deployed on both the cloud and the edge ends for better realtime monitoring and control of EVs' batteries, especially when Internet communication is limited. In this paper, the state synchronization problem is cast as a stability problem and also a contraction problem to understand systems' evolution over time. The proposed approaches leverage the Lyapunov stability condition and contraction theory to enforce the synchronization between the twin models and real battery states with streaming data. The contraction analysis framework does not require the knowledge of initial points. The derived stability and contraction conditions that reflect the physical properties of dynamical systems can be embedded into the training process of the reduced order model (ROM) neural network. Simulation results demonstrate the effectiveness of the proposed approaches. The average ROM prediction error is 1.16% and has a 30.39% accuracy improvement with the proposed algorithm.

The online small-signal stability assessment of electrical power grids is typically a challenging problem due to uncertainties and parameter variations of power system dynamics as well as the incurred high computational complexity. This paper proposes a novel theoretical framework for dynamic small-signal stability assessment of power grids by estimating the region of attraction (ROA) for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage time series of phasor measurement unit (PMU) measurements including voltage/current magnitude, phase and frequency (i.e., PMU data) of real power grids for validating the online GP approach. Moreover, validations are taken to substantiate the proposed approach by using PMU data of real smart-grid infrastructure and IEEE test cases. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies. Note to Practitioners—This paper was motivated by the problem of online assessment of power systems security but it also applies to other industrial control systems that have stable state trajectories. Existing approaches to security assessment of power systems generally focus on the adoption of various machine-learning algorithms by treating power grid as a “black box,” which ignores the intrinsic characteristics of power systems and thus restricts the inference performance. This paper proposes a new approach using limited sampling data and system dynamics to construct a domain of stability for power grids, which can reflect the evolution of security zones and provide more accurate predictions. In this work, we mathematically characterize the domain of stability for a practical power grid by analyzing and learning its state trajectories. Then we show how the proposed approach can be efficiently implemented online, which can timely alert human operators to abnormalities. Preliminary validations suggest that this approach is feasible and effective. In future research, we will incorporate it into an energy management system and test it in other industrial processes.

Circular economy in power systems involves new circular management of battery energy storage systems, featuring sustainable operation with lifespan extension. Proper daily operations of batteries can prolong their lifetime and vice versa. However, the time scale difference between the service time and the short-term operation makes it challenging to engage the targeted lifespan into daily economic operations. This article proposes a dynamic framework for health-informed optimal power flow (OPF) to reach the battery expected lifespan by offering the optimal feasible operation space. The expected service lifespan is achieved if the battery’s daily working condition is confined within such evolving feasible domains throughout its service time. Economical operation of the battery is scheduled based on OPF that integrates such feasible domains upon convexification for higher computational efficiency. A Monte Carlo-based data-driven method is developed to unveil the correlation between the remaining useful life (RUL) and operational states as well as the battery health indicator. The proposed method and constructed regions have been effectively validated under multiple scenarios with different operating modes. With the IEEE 39-bus test case, numerical results show that the constructed health-informed OPF can boost the mean value of the battery’s RUL up to 48.79%.

The energy consumption of Internet Data Centers (IDCs) is rapidly increasing and may account for 20.9% of global energy consumption in 2030. This huge demand will change the power system operations significantly in the future because IDCs are different from the traditional loads in power systems [1] . Specifically, IDCs are energy-intensive loads that can dominate and alter the nearby power flow directions, thus posing challenges to the regulation of power systems. Also, working loads migration across IDCs at different locations and time slots can disturb the real-time power balance in power systems. Besides, IDCs’ intensive electricity demand rising following the expansion of IDCs might not be met due to supply limits of the power infrastructure. Moreover, IDCs scattered in a power grid can introduce stress and overload "weak" power transmission lines as well as cause other operational violations in power systems, such as voltages and frequency. All these effects will become more pronounced with more and larger IDCs.

The future distribution systems expose to an unprecedented level of uncertainties due to renewable resources, nontraditional loads, aging infrastructure, etc., posing potential risks to secure operation of the system.This article proposes a new technique to estimate the voltage stability margin of the distribution systems with high penetration of renewables.Its convergence and robustness under complex and stressed working conditions are guaranteed in theory. This technique is handy for the operation as it features self-adaptive step size and is applicable to general system topology. It leverages a newly derived analytical solvability certificate based on the Kantorovich fixed-point theorem. A fast version of the proposed technique is duly proposed to speed up the computation up to 8 times while maintaining high accuracy, which lends itself to online and time-sensitive emergency tasks. Numerical simulations with various IEEE test feeders verify the performance of the techniques.

Control systems are significant to the microgrid as they regulate performance parameters such as frequency, active power, and voltage. Distributed control systems allow direct communication between the secondary controllers and controls the parameters efficiently. To secure each distributed control process and ensure a good quality of control results, a proof-of-authority private blockchain is applied in this article to defend the distributed control system against various types of cyber-attacks such as false data injection. A four-distributed generation islanded microgrid is tested with the implementation of the blockchain. Smart contracts are created to calculate the control feedback and return the value to corresponding secondary controllers. All of the four nodes are initially assigned as the authority nodes to share the mining burden, but according to the proof-of-authority consensus protocol, the authority role could be excluded if the node behaves illegally and causes damage to the control system. In addition, different attacking scenarios are categorized and analyzed with their respective solutions. Finally, a case study is introduced to verify the corresponding solutions and proves that the proposed method is able to secure the distributed control system while ensuring the control quality. Numerical results show the effectiveness and feasibility of the proposed approach.

In this article, an optimal steady-state voltage control framework is developed based on a novel linear voltage-power dependence deducted from Gaussian process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and, thus, suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control, which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions, which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of effective voltage control source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.

The drive for more electric aircraft (MEA) architecture has resulted in the introduction of high power density electric machines for aircraft propulsion applications. The reliability of the electrical machines in such harsh environments is a challenge that is seldom addressed. The paper proposes, through simulation, the use of aging model to accurately estimate the lifetime of a machine with turn-to-turn short-circuit (TTSC) condition. The proposed aging model considers all the aging factors due to electrical, mechanical and thermal phenomena. The paper also puts forth a cumulative stress curve for various loading & environmental constraints and uses the stress curve with winding temperature to create an aging model to accurately predict and estimate the lifetime of the machine. A bottom-up approach through the developed model can be used to estimate the lifetime of the machine in real-time.