• Energy Research

Abstract Battery health diagnostics is extremely crucial to guaranty the availability and reliability of the application in which they operate. Data-driven health diagnostics methods, particularly machine learning methods, have gained attention due to their simplicity and accuracy. However, a machine learning method is desired which can cope with the nonlinear behavior of battery cells and yet it avoids high computational complexity to work efficiently in online applications. The accuracy and robustness of machine learning methods strongly depend on the availability of a comprehensive battery degradation dataset that covers a variety of battery aging patterns. While many studies fail to address the aforementioned requirements, this study attempts to address them. Twenty-one nickel manganese cobalt oxide battery cells have been cycled in various operating conditions for more than two years to acquire the data. The partial charging voltage curve is explored to extract the health indicators that describe the health trajectory of the battery. Afterward, a nonlinear autoregressive exogenous (NARX) model is developed to capture the dependency between the health indicators and state of health of battery cells. Finally, the accuracy and robustness of the proposed method are validated. The results demonstrate the ability of NARX to health diagnosis of lithium-ion batteries with a maximum root mean squared error of 0.46 for untrained data. This indicates that the proposed model has high estimation accuracy, low computational complexity, and the ability of battery health estimation regardless of its aging pattern. These features point out the practicability of the proposed technique on online health diagnostics.

lithium-ion batteries are a convenient choice for various energy storage systems (ESS) such as electric and hybrid vehicles. Nevertheless, the characterization of capacity degradation is critical to ensure the proper performance of lithium-ion batteries. This paper presents a data-driven technique based on a recurrent neural network called nonlinear autoregressive exogenous neural network (NARX) to estimate the capacity degradation of lithium-ion batteries. The voltage charging curves, extracted from twelve nickel manganese cobalt oxide (NMC) cells with different aging trends are used to develop a predictive model for capacity estimation. The results demonstrate that the proposed model is able to estimate capacity with high accuracy and low complexity.

Lithium-ion batteries have achieved dominance in energy storage systems. Meanwhile, there is a demand for the reliability of lithium-ion batteries. Battery prognostics and health management (PHM) is a discipline that not only provides accurate, early, and online health diagnosis, but also guarantees a robust and precise prediction of the remaining useful life of lithium-ion batteries, independent of the operating conditions. This paper attempts to develop a novel PHM methodology that addresses the points mentioned above. A large dataset including thirty-eight nickel manganese cobalt oxide battery cells is used. The battery cells have been tested under various test conditions to achieve different aging patterns. Afterward, the health indicators that describe the health trajectory of the battery are extracted from partial charging voltage curves. A recurrent neural network called nonlinear autoregressive with exogenous input is developed to estimate battery state of health (SOH) based on the extracted health indicators. The estimated SOH is used as the prognostic feature to develop a remaining useful life of battery (RUL) prediction model based on the similarity-based model. The proposed methods are validated using untrained data. The results indicate that the proposed PHM methodology can estimate the SOH of untrained battery cells with a maximum RMSE of 0.61. The RUL of battery cells with different aging patterns can be predicted with a maximum absolute error of 110 cycles. It can be concluded that the proposed method has the advantages of high precision in the health diagnosis and prognosis of battery cells regardless of their aging patterns, simplicity, and generalization to untrained data. These advantages point out the feasibility of the proposed method for online prognostics and health management of lithium-ion batteries.

The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns.

A lithium-ion capacitor (LiC) is one of the most promising technologies for grid applications, which combines the energy storage mechanism of an electric double-layer capacitor (EDLC) and a lithium-ion battery (LiB). This article presents an optimal thermal management system (TMS) to extend the end of life (EoL) of LiC technology considering different active and passive cooling methods. The impact of different operating conditions and stress factors such as high temperature on the LiC capacity degradation is investigated. Later, optimal passive TMS employing a heat pipe cooling system (HPCS) is developed to control the LiC cell temperature. Finally, the effect of the proposed TMS on the lifetime extension of the LiC is explained. Moreover, this trend is compared to the active cooling system using liquid-cooled TMS (LCTMS). The results demonstrate that the LiC cell temperature can be controlled by employing a proper TMS during the cycle aging test under 150 A current rate. The cell’s top surface temperature is reduced by 11.7% using the HPCS. Moreover, by controlling the temperature of the cell at around 32.5 and 48.8 °C, the lifetime of the LiC would be extended by 51.7% and 16.5%, respectively, compared to the cycling of the LiC under natural convection (NC). In addition, the capacity degradation for the NC, HPCS, and LCTMS case studies are 90.4%, 92.5%, and 94.2%, respectively.

Lithium-ion capacitor technology (LiC) is well known for its higher power density compared to electric double-layer capacitors (EDLCs) and higher energy density compared to lithium-ion batteries (LiBs). However, the LiC technology is affected by a high heat generation problem in high-power applications when it is continuously being charged/discharged with high current rates. Such a problem is associated with safety and reliability issues that affect the lifetime of the cell. Therefore, for high-power applications, a robust thermal management system (TMS) is essential to control the temperature evolution of LiCs to ensure safe operation. In this regard, developing accurate electrical and thermal models is vital to design a proper TMS. This work presents a detailed 1D/3D electro-thermal model at module level employing MATLAB/SIMULINK® coupled to the COMSOL Multiphysics® software package. The effect of the inlet coolant flow rate, inlet coolant temperature, inlet and outlet positions, and the number of arcs are examined under the cycling profile of a continuous 150 A current rate without a rest period for 1400 s. The results prove that the optimal scenario for the LCTMS would be the inlet coolant flow rate of 500 mL/min, the inlet temperature of 30 °C, three inlets, three outlets, and three arcs in the coolant path. This scenario decreases the module’s maximum temperature (Tmax) and temperature difference by 11.5% and 79.1%, respectively. Moreover, the electro-thermal model shows ±5% and ±4% errors for the electrical and thermal models, respectively.

Prognostics and health management (PHM) has emerged as a vital research discipline for optimizing the maintenance of operating systems by detecting health degradation and accurately predicting their remaining useful life. In the context of lithium-ion batteries, PHM methodologies have gained significant attention due to their potential for enhancing battery maintenance and ensuring safe and reliable operation. Among the various approaches, data-driven methodologies, particularly those leveraging machine learning (ML) models, have gained interest for their accuracy and simplicity. To develop an optimized data-driven PHM system for batteries, a comprehensive understanding of each step involved in the PHM process is crucial. This review paper aims to address this need by providing a thorough analysis of the different phases of battery PHM, encompassing data acquisition, feature engineering, health diagnosis, and health prognosis. In contrast to previous review papers that primarily focused on battery health diagnosis and prognosis methods, this work goes beyond by encompassing all essential steps necessary for developing a tailored PHM methodology specific to lithium-ion batteries. By covering data acquisition methods, feature engineering techniques, as well as health diagnosis and prognosis methods, this paper fills a significant gap in the existing literature. It serves as a comprehensive roadmap for researchers and practitioners aiming to develop PHM systems for lithium-ion batteries using ML techniques. With its in-depth analysis and critical insights, this review paper constitutes a substantial contribution to the field. It provides valuable guidance for designing effective PHM methodologies and paves the way for further advancements in battery maintenance and management.

This paper presents the concept of a passive thermal management system (TMS), including natural convection, heat pipe, and phase change material (PCM) for electric vehicles. Experimental and numerical tests are described to predict the thermal behavior of a lithium-titanate (LTO) battery cell in a high current discharging process. Details of various thermal management techniques are discussed and compared with each other. The mathematical models are solved by COMSOL Multiphysics®, the commercial computational fluid dynamics (CFD) software. The simulation results are validated against experimental data with an acceptable error range. Results indicate that the maximum cell temperature for the cooling strategies of natural convection, heat pipe, and PCM assisted heat pipe reaches 56 °C, 46.3 °C, and 33.2 °C, respectively. It is found that the maximum cell temperature experienced a 17.3% and 40.7% reduction by heat pipe and PCM assisted heat pipe cooling system compared with natural convection.