Accurately estimating the State of Health (SOH) of batteries in field applications is critical for timely maintenance and secondary utilization. Although considerable studies are conducted using data-driven techniques, these methods often face challenges in interpretability and integrating physical knowledge. To address this issue, this paper proposes an accurate SOH estimation method using a physics-informed neural network (PINN) with spatio-temporal feature extraction. The proposed model utilizes multi-sensor data as an input and employs a spatio-temporal attention mechanism to automatically extract effective features from both the time step dimension and the sensor dimension. Subsequently, PINN is utilized to regulate the convolutional neural network training process and oversee the degradation trajectory of SOH estimation. By integrating the attention mechanism and physical information, the model achieves higher accuracy and more interpretable predictions. The proposed method is validated on a field dataset with 20 on-road vehicles. Experimental results indicate that the proposed method achieves a root mean square error of 1.599%, which is a relative reduction of 42.32% compared to the baseline model.

Accurate and rapid state-of-health (SOH) estimation of retired lithium-ion batteries is critical for sustainable recycling and second-life applications. However, data-driven methods face challenges due to data scarcity and heterogeneity under random retirement conditions, such as varying states of charge (SOC). This study proposes a generative learning framework based on a vector quantized-variational autoencoder (VQ-VAE) to generate synthetic battery pulse voltage response data, enabling robust SOH estimation without exhaustive physical measurements. The VQ-VAE model leverages cross-attention mechanisms to capture dependencies between SOC conditions and voltage responses, generating high-fidelity data for unseen retirement scenarios. Experimental results demonstrate that the generated data achieve a mean absolute percentage error (MAPE) below 6% for SOH estimation across diverse battery types, including nickel manganese cobalt oxide (NMC), lithium iron phosphate (LFP), and lithium manganese oxide (LMO).

Lithium-ion batteries are the crucial energy source for electric vehicles. However, they experience capacity degeneration when used in low-temperature environments. It is necessary to preheat them before using. In this paper, a core temperature-aware optimal preheating strategy, featuring a multi-stage constant-current discharge heating method, is proposed to heat lithium-ion batteries in low-temperature environments. Firstly, this paper builds an internal battery temperature distribution model based on Fourier’s law of heat conduction. Secondly, the temperature distribution model is coupled within the battery model to display the comprehensive performance of the battery. Thirdly, decreasing heating time and reducing capacity loss jointly formulate a multi-objective optimization problem solved by dynamic programming(DP) algorithm. Judging by simulation results, heating time is downsized and the capacity loss is reduced at the same time, proving the progressiveness of the proposed strategy.

Accurate modeling of open circuit voltage hysteresis for LiFePO4batteries is crucial for establishing an advanced battery model. However, existing hysteresis modeling methods often yield suboptimal results due to inadequate parameterization. This paper proposes an optimal modeling method for open circuit voltage hysteresis based on the Prandtl-Ishlinskii model and an associated parameterization method. First, an asymmetric operator with cubic envelope functions is designed to enhance the classical Prandtl-Ishlinskii model, which originally features a symmetric and linear operator. This modification enables the proposed model to accurately capture intricate hysteresis. Second, a hierarchical parameterization method is proposed to identify optimal parameters. Specifically, an improved grey wolf optimizer is employed to determine the operator-related parameters. Then, the remaining parameters are calculated using the least squares algorithm, enhancing computational efficiency. Finally, the proposed model is validated on the experimental hysteresis data from three distinct scenarios. The modeling error of the proposed model decreased by 66.57 % and 32.51 % compared with two other benchmark models.

Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. Firstly, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.

With the rapid development of electric vehicle industry, the battery management system of electric vehicle is the focus of research. Battery management is not only related to the safe driving of electric vehicles, but also the basis of intelligent driving of electric vehicles. The state-of-charge (SoC) estimation of battery is very important in battery management system. The battery is in a state of power consumption when the electric vehicle is running, but when the electric vehicle is braked, the kinetic energy will also be converted into electric energy to charge the battery. The acceleration and braking of electric vehicles are frequently switched. Therefore, the working conditions of electric vehicle batteries are complex, and the influence of battery hysteresis on the accuracy of SoC estimation cannot be ignored. In this paper, a lithium ion battery model considering hysteresis effect based on machine learning is proposed. The experiment was designed to collect the data of small cycle charge and discharge of the battery. The data were used to train the long short-term memory (LSTM) neural network model, and a battery model with hysteresis effect was obtained. It is verified that the model performs well in the test set, and the error of hysteresis voltage can be reduced to 0.002V. This model can be used for SoC estimation considering hysteresis effect.