All-solid-state batteries (ASSBs) have been considered as the next generation of lithium-ion batteries. Physics-based models have the advantage of providing internal electrochemical information. To promote physics-based models in real-time applications, in this study, a series of model reduction methods are applied to obtain a reduced-order model (ROM) for ASSBs. First, analytical solutions of the partial differential equations (PDEs) are derived by the Laplace transform. Then, the Padé approximation method is used to convert the transcendental transfer functions into lower-order fractional transfer functions. Next, the concentration distributions in electrodes and electrolyte are approximated by parabolic and cubic functions, respectively. Thanks to the fast calculation of concentration distributions in real time, the equilibrium potential, overpotentials, and battery voltage can now be directly calculated. Compared with the original PDE-based model, the voltage errors of the proposed ROM are less than 2.6 mV. Compared with the voltage response of experimental data, a good agreement can be observed for the ROM under three large C-rates discharging conditions. The calculation time of ROM per step is within 0.2 ms, which means it can be integrated into a battery management system. The proposed ROM achieves excellent performance and a better tradeoff between model fidelity and computational complexity.

All-solid-state batteries (ASSBs) are considered to be the next generation of lithium-ion batteries. Physics-based models (PBMs) can effectively simulate the internal electrochemical reactions and provide critical internal states for battery management. In order to promote the onboard applications of PBMs for ASSBs, in this article, the parameter sensitivity of a typical PBM is analyzed, and a joint estimation method for states and parameters based on sigma-point Kalman filtering (SPKF) is proposed. First, to obtain accurate sensitivity analysis results, approaches from different principles, including local sensitivity, elementary effect test, and variance-based methods, are applied. Then, for the battery model based on partial differential equations, a nonlinear state-space model is constructed by using the finite-difference discretization method. Finally, the SPKF algorithm is employed to conduct the joint estimation of model parameters and lithium-ion concentrations. The results from constant current and dynamic cycles show that two parameters, namely maximum lithium-ion concentration and minimum lithium-ion concentration, have the most influence on the model results. The joint estimation method is validated in three different cases, and the mean absolute errors of the estimated voltage and state of charge (SOC) are below 2.1 mV and 1.5%, respectively.

High-performance and safe operation of a serially connected lithium-ion battery pack in the electric vehicle necessitates effective cell equalization to maintain the state-of-charge of each cell at the same level. In this work, an improved module-based cell-to-pack-to-cell (CPC) equalization system is developed, where the module-level (ML)/cell-to-module-to-cell (CMC) equalizers are utilized for equalization of the battery modules/cells in each module. Compared with the conventional CPC balancing system, it has the advantages of simple modular structure and convenient maintainability. Then, a two-layer model predictive control (MPC) strategy is proposed, in which the ML equalizers are controlled by the top-layer MPC and the controlled CMC equalizing currents in each module are designed by the bottom-layer MPC algorithms in parallel. Its computational complexity is much less than the centralized MPC, which makes it more feasible for real-time cell equalization implementation in practical applications. A rigorous mathematical convergence proof of the proposed equalization control strategy is provided based on the Lyapunov stability theorem. Finally, extensive results are provided to verify the proposed improved module-based CPC equalization system and the two-layer MPC-based equalization control approach with excellent equalization performance being demonstrated.

State/temperature monitoring is one of the key requirements of battery management systems that facilitates efficient and intelligent management to ensure the safe operation of batteries in electrified transportation. This paper proposes an online end-to-end state monitoring method based on transferred multi-task learning. Measurement data is directly used for sharing information generation with the convolutional neural network. Then, the multiple task-specific layers are added for state/temperature monitoring. The transfer learning strategy is designed to improve accuracy further under various application scenarios. Experiments under different working profiles, temperatures, and aging conditions are conducted to evaluate the method, which covers the wide usage ranges in electric vehicles. Comparisons with several benchmarks illustrate the superiority of the proposed method with better accuracy and computational efficiency. The monitoring results under extremely current working profiles and variable internal and external conditions are evaluated. Results show that the mean absolute error and root mean square error of state of charge and state of energy estimation are less than 2.31% and 3.31%, respectively. The above errors in the prediction of future temperature five steps ahead are less than 0.89℃ and 1.29℃, respectively. The framework is also suitable for monitoring second-life batteries retired from electric vehicles. This paper illustrates the potential application of data-driven multi-state monitoring throughout the entire battery life.

Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively.

Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error.