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Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications. [ABSTRACT FROM AUTHOR]

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]

Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available. [ABSTRACT FROM AUTHOR]

Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme. [ABSTRACT FROM AUTHOR]

Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies; a feasible solution that can analyze feature variables within manufacturing chain and achieve reliable classification is, thus, urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag predictions, Gini changes, as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analyzed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties, but also leads to the effective quantification of both manufacturing feature importance (FI) and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production FI and correlations by three various quantitative indicators, including the unbiased FI, gain improvement FI, and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing. [ABSTRACT FROM AUTHOR]

A series of integrated equalizers based on joint buck-boost (BB) and switched-capacitor (SC) converters are proposed for balancing the voltages of series-connected battery packs. All these equalizers realize the any-cells-to-any-cells (AC2AC) equalization mode without increasing the count of MOSFETs and drivers. Corresponding operational principles are analyzed and the expressions of balancing currents are derived by analytical methods and verified by experimental waveforms. According to the comparative balancing experiments for four and six series-connected Li-ion cells, one proposed CBB-PCSC equalizer, which achieves the dual AC2AC balancing modes through the integration of both coupled buck-boost (CBB) and parallel-connected switched-capacitor (PCSC) converters, leads to the highest balancing speed and efficiency. Moreover, compared with several conventional equalizers, this CBB-PCSC topology also has the compact size and low cost, making it become a well-performing integrated topology for automotive battery voltages equalization. [ABSTRACT FROM AUTHOR]

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance. [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]