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The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved. [ABSTRACT FROM AUTHOR]

Battery manufacturing plays a direct and pivotal role in determining battery performance, which, in turn, significantly affects the applications of battery-related energy storage systems. As a complicated process that involves chemical, mechanical and electrical operations, effective battery property predictions and reliable analysis of strongly-coupled battery manufacturing parameters or variables become the key but challenging issues for wider battery applications. In this paper, an interpretable machine learning framework that could effectively predict battery product properties and explain dynamic effects, as well as interactions of manufacturing parameters is proposed. Due to the data-driven nature, this framework can be easily adopted by engineers as no specific battery manufacturing mechanism knowledge is required. Reliable battery manufacturing dataset particularly for coating (one key stage) collected from a real battery manufacturing chain is adopted to evaluate the proposed framework. Illustrative results demonstrate that three types of battery capacities including cell capacity, gravimetric capacity, and volumetric capacity can be accurately predicted with R 2 over 0.98 at the battery early-manufacturing stage. Besides, information regarding how the variations of coating mass, thickness, and porosity affect these battery capacities is effectively identified, while interactions of these coating parameters can be also quantified. The developed framework makes the data-driven model become more interpretable and opens a promising way to quantify the interactions of battery manufacturing parameters and explain how the variations of these parameters affect final battery properties. This could assist engineers to obtain critical insights to understand the underlying complicated battery material and manufacturing behavior, further benefiting smart control of battery manufacturing. • Interpretable machine learning is designed for battery smart manufacturing. • Designed method can effectively predict three types of battery capacities. • Designed method can quantify dynamic effects and interactions of coating parameters. • Designed framework is validated on a real battery manufacturing chain. [ABSTRACT FROM AUTHOR]

Battery management systems based on electrochemical models could achieve more accurate state estimations and efficient battery controls with access to cell unmeasurable physical variables. As battery electrochemical models are governed by first-principle partial differential equation sets, model complexity and multiple parameter determination are bottlenecks for their wider applications. This paper gives a systematical review of recent advancements in electrochemical model development and parameterization. Specifically, classic pseudo-two-dimensional model and related model order reduction methodologies are first summarized and analyzed. Given that the homogenization hypothesis of the pseudo-two-dimensional model could lead to significant model mismatch under some operational conditions, enhanced models considering cell internal inhomogeneity with multi-particles, multi-scales, aging and thermal dynamics are examined. To facilitate model portability, parameter identification techniques of these models are classified, and solutions for optimizing the parameterization procedure are explored. Finally, current research gaps in the literature and remaining challenges are discussed and highlighted with some suggestions. This review will therefore inform the engineers of battery management and control engineering, whilst boosting the research, design and operation of control-oriented electrochemical models for smarter battery management at different readiness levels. • A systematic review on control-oriented battery electrochemical models is proposed. • P2D model and various order-reduction techniques are investigated. • Enhanced multi-particle, multi-scale, aging, and thermal models are explored. • Different parameterization methods for electrochemical models are summarized. • Future trends, challenges and suggestions are discussed. [ABSTRACT FROM AUTHOR]

As a soft sensor, the state-of-power (SoP) estimator reveals critical information on battery-based energy storage systems. A set of reliable 'referenced values' is the key to evaluate the precision of such soft sensors at their designing stage and could influence the overall reliability of the battery systems. However, experimentally obtaining the 'referenced SoP' is non-trivial since high-current pulse tests (> 10C) are required to charge/discharge the batteries to their cut-off conditions. The associated high-power experimental platforms could be expensive, while frequently applying large current at boundary conditions may leave potential safety issues. Aiming at these problems, this paper focuses on obtaining referenced SoP, rather than onboard SoP estimations. A novel equivalent discharging test is designed to accurately recover the voltage response of high-current pulses from a set of low-current tests, resulting in a 33% reduction of the peak discharging current. In addition, a flexible softmax neural network is further proposed to generate SoP values for the intervals between pulse tests. With these tools, reliable SoP values with errors lower than 0.5% can be readily obtained. The SoP obtained from our approach can be further utilised as a highly accurate benchmark to evaluate the accuracy of other onboard battery SoP estimators. • This paper focuses on obtaining accurate 'referenced state-of-power' in the lab. • The maximum current of our proposed testing procedure can be reduced by 33%. • The error of the generated state-of-power can be bounded by only 0.5%. • The data-driven nature makes our method suitable for general batteries. [ABSTRACT FROM AUTHOR]

With the advent of sustainable and clean energy, lithium-ion batteries have been widely utilised in cleaner productions such as energy storage systems and electrical vehicles, but the management of their electrode production chain has a direct and crucial impact on the battery performance and production efficiency. To achieve a cleaner production chain of battery electrode involving strongly-coupled intermediate parameters and control parameters, a reliable approach to quantify the feature importance and select the key feature variables for predicting battery intermediate products is urgently required. In this paper, a Gaussian process regression-based machine learning framework, which incorporates powerful automatic relevance determination kernels, is proposed for directly quantifying the importance of four intermediate production feature variables and analysing their influences on the prediction of battery electrode mass load. Specifically, these features include three intermediate parameters from the mixing step and a control parameter from the coating step. After deriving four different automatic relevance determination kernels, the importance of these four feature variables based on a regression modelling is comprehensively analysed. Comparative results demonstrate that the proposed automatic relevance determination kernel-based Gaussian process regression models could not only quantify the importance weights for reliable feature selections but also help to achieve satisfactory electrode mass load prediction. Due to the data-driven nature, the proposed framework can be conveniently extended to improve the analysis and control of battery electrode production, further benefitting the manufactured battery yield, efficiencies and performance to achieve cleaner battery production. Image 1 • A data-driven framework is designed for mass load prediction in battery production. • Importance of four production feature variables of interest is directly quantified. • Performance of four ARD kernels is comparatively studied via GPR model. • Effects of these sensitive features on the mass load predictions are analysed. • Designed framework can benefit the optimisation for cleaner battery production. [ABSTRACT FROM AUTHOR]

—Batteries are acknowledged as an important carrier for next-generation energy and future electrification. Capacity degradation is an inevitable process that will eventually lead to a decline in battery performance. This paper presents a battery degradation trajectory prognostics approach under storage conditions based on the data-driven method with necessary knowledge inspiration. This data-driven approach is based on Support Vector Regression (SVR), incorporating mechanisms and empirical knowledge of battery calendar aging as the kernel. The developed data-driven approach is evaluated using two real but different capacity degradation datasets: Dataset A has two storage temperatures (25 °C and 45 °C) and storage SoCs (50% and 90%), while Dataset B has the same storage temperatures but four different storage SoCs (20%, 50%, 70% and 95%). The illustrative results show that the developed SVR-based data-driven approach with the battery knowledge-motivated kernel can make accurate future capacity prognostics under different storage conditions. Due to the superiority of its flexibility and knowledge-data-motivation, the developed approach could be extended and help engineers monitor or capture the future capacity degradation trajectory of other types of batteries when their relevant data become available, which in turn will benefit the life management of battery-based energy storage applications. • Knowledge-motivated data-driven method for battery degradation is achieved. • Mechanism knowledge and empirical knowledge are completely coupled. • A long-term capacity degradation trajectory prediction method is proposed. • The methodologies are completely validated based on experiments. [ABSTRACT FROM AUTHOR]

Accurate estimations of battery state-of-charge (SOC) for energy storage systems are popular research topics in recent years. Numerous challenges remain in several aspects, especially in dealing with the conflict of high model accuracy and complex model structure with heavy computational cost. This paper proposes a compact and optimized SOC estimation model, integrating a fast input selection algorithm to choose important terms as input variables, followed by a simple and efficient JAYA optimization scheme to tune the key parameters of neural network functions. From the real-system experiment results, it can be seen that the estimation model errors are greatly reduced by applying optimization method, and the model performance is validated through statistical error values including root mean square error, mean absolute error, mean absolute percentage error and SOC error. The experimental results demonstrate that the SOC estimations can be greatly improved after optimization of neural network parameters under different charging and discharging process. • A novel compact and optimized radial basis function neural network is proposed. • The method is adopted for the SOC estimation of energy storage systems. • Simulation results validate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Batteries are presently pervasive in portable electronics, electrified vehicles, and renewable energy storage. These indispensable engineering applications are all safety-critical and energy efficiency-demanding such that batteries must be meticulously monitored and manipulated, where effectively estimating the internal battery states is a key enabler. The primary goal of this paper is to present a concise, understandable overview of existing methods, key issues, technical challenges, and future trends of the battery state estimation domain. More specifically, for the first time, the state of the art in State of Charge (SOC), State of Energy (SOE), State of Health (SOH), State of Power (SOP), State of Temperature (SOT), and State of Safety (SOS) estimation is all elucidated in a tutorial yet systematical way, along with existing issues exposed. In addition, from six different viewpoints, some future important research opportunities and evolving trends of this prosperous field are disclosed, in order to stimulate more technologically innovative breakthroughs in SOC/SOE/SOH/SOP/SOT/SOS estimation. • Critical battery internal states are defined and explained. • Battery state estimation methods are systematically overviewed. • Key issues, technical challenges and opportunities are elucidated. • Future research directions and trends are presented. [ABSTRACT FROM AUTHOR]

High energy density has made Li-ion battery become a reliable energy storage technology for transport-grid applications. Safely disposing batteries that below 80% of their nominal capacity is a matter of great concern to reduce overall carbon footprint. As battery typically accounts for 40% of the total cost of an electrical vehicle, it becomes necessary to combine reutilization and recycling for extending the lifetime of retired automotive batteries and make the overall battery supply chain profitable rather than dumping them directly. From an economic, technical, and environmental standpoint, this paper provides a comprehensive overview of the present state of second-life Li-ion batteries through exploring relevant literature. Specifically, the fundamental of Li-ion battery degradation and experimental approaches are first surveyed. After examining the obstacles and methods of reusing and recycling Li-ion battery, related applications, cost issues, and business models of second-life Li-ion batteries are discussed. By offering a systematical survey of current status of recycled Li-ion battery, this review could inform commercial technology selections and academic research agendas alike, thus boosting progress in Li-ion battery second-life applications. • Second-life battery is economically benefitting and environmentally sustainable. • An elaborate discussion on the identification of battery degradation is presented. • Profitable economics of the second-life battery is discussed. • Cost analysis and business model for second-life battery application are discussed. • Challenges and potential solutions for second-life battery application are identified. [ABSTRACT FROM AUTHOR]