Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

New Trends and Challenges in Condition Monitoring Strategies for Assessing the State-of-charge in Batteries

Condition monitoring strategies play an important key role to ensure the proper operation and/or working conditions in electrical, mechanical, and electronic systems; in this sense, condition monitoring methods are commonly implemented aiming to avoid undesired breakdowns and are also implemented to extend the useful life of the evaluated elements as much as possible. Therefore, the objective of this work is to report the new trends and challenges related to condition monitoring strategies for assessing the state-of-charge in batteries under the Industry 4.0 framework. Specifically, this work is focused on the analysis of those signal processing and artificial intelligence techniques that are implemented in experimental and model-based assessing approaches. With this work, important aspects may be highlighted as well as the conclusions and prospects may be included for the development trend of condition monitoring strategies to assess and ensure the state-of-charge in batteries.

A New Hybrid Neural Network Method for State-of-Health Estimation of Lithium-Ion Battery

Accurate estimation of lithium-ion battery state-of-health (SOH) is important for the safe operation of electric vehicles; however, in practical applications, the accuracy of SOH estimation is affected by uncertainty factors, including human operation, working conditions, etc. To accurately estimate the battery SOH, a hybrid neural network based on the dilated convolutional neural network and the bidirectional gated recurrent unit, namely dilated CNN-BiGRU, is proposed in this paper. The proposed data-driven method uses the voltage distribution and capacity changes in the extracted battery discharge curve to learn the serial data time dependence and correlation. This method can obtain more accurate temporal and spatial features of the original battery data, resulting higher accuracy and robustness. The effectiveness of dilated CNN-BiGRU for SOH estimation is verified on two publicly lithium-ion battery datasets, the NASA Battery Aging Dataset and Oxford Battery Degradation Dataset. The experimental results reveal that the proposed model outperforms the compared data-driven methods, e.g., CNN-series and RNN-series. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are limited to within 1.9% and 3.3%, respectively, on the NASA Battery Aging Dataset.

Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment

The energy efficiency of a renewable energy system is inextricably linked to the energy storage technologies used in conjunction with it. The most extensively utilized energy storage technology for all purposes is electrochemical storage batteries, which have grown more popular over time because of their extended life, high working voltage, and low self-discharge rate. However, these batteries cannot withstand the very low temperatures encountered in cold regions, even with these very promising technical characteristics. The cold northern temperatures affect the batteries’ electromotive force and thus decrease their storage capacity. In addition, they affect the conductivity of the electrolyte and the kinetics of electrochemical reactions, thus influencing the capacity and speed of electrons in the electrolyte. In this article, which is intended as a literature review, we first describe the technical characteristics of charge–discharge rate of different electrochemical storage techniques and their variations with temperature. Then, new approaches used to adapt these electrochemical storage techniques to cold climates are presented. We also conduct a comparative study between the different electrochemical storage techniques regarding their performance in the harsh climatic conditions of the Canadian North.

Rapid Model-Free State of Health Estimation for End-Of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy

The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes.

Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles

Thermal management systems of passenger vehicles are fundamental to provide adequate cabin thermal comfort. However, for battery electric vehicles they can use a significant amount of battery energy and thus reduce the real driving range. Indeed, when heating or cooling the vehicle cabin the thermal management system can consume up to 84% of the battery capacity. This study proposes a model-based approach to design an energy-efficient control strategy for heating electric vehicles, considering the entire climate control system at different ambient conditions. Specifically, the study aims at reducing the energy demand of the compressor and water pumps when operating in heat pump mode. At this scope, the climate control system of the reference vehicle is modelled and validated, enabling a system efficiency analysis in different operating points. Based on the system performance assessment, the optimized operating strategy for the compressor and the water pumps is elaborated and the results show that the demand-based control achieves up to 34% energy reduction when compared to the standard control.

A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation

In order to improve the durability and economy of a hybrid power system composed of a battery and supercapacitors, a control strategy that can reduce fluctuations of the battery current is regarded as a significant tool to deal with this issue. This paper puts forwards a disturbance rejection control strategy for a hybrid power system taking into account the degradation of the battery. First, the degradation estimation of the battery is done by the model-driven method based on the degradation model and Cubature Kalman Filter (CKF). Considering the transient and sinusoidal disturbance from the load in such a hybrid system, it is indispensable to smooth the behavior of the battery current in order to ensure the lifespan of the battery. Moreover, the constraints for the hybrid system should be considered for safety purposes. In order to deal with these demands, a cascaded voltage control loop based on a super twisting controller and proportional integral controller with an anti-windup scheme is designed for regulating the DC bus voltage in an inner voltage loop and supercapacitors’ voltage in an outer voltage loop, respectively. The specific feature of the proposed control method is that it operates like a low-pass filter so as to reduce the oscillations on the DC bus.