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Accurate state-of-charge (SoC) estimation is crucial to guarantee the safety and reliability of lithium-ion batteries. This paper aimed to develop an advanced battery estimation method for electric vehicles based on the grey model without the need of a high-fidelity battery model demanding high computation power. The metabolic grey model (MGM) introduced metabolism mechanism to adjust the model parameters according to the evolving operating status and conditions and estimate the state of charge. To further validate the feasibility of the proposed method, the analog acquisition, communication system, and SoC estimation algorithms were programmed to embed within a LabVIEW platform. The performance of the proposed SoC estimation with MGM algorithm was finally investigated with a battery-in-loop platform under different dynamic loading profiles. The experimental results indicated that the MGM can estimate SoC that involved small samples and poor information in real time, with the maximum errors of no over 4% under various loading conditions.

Abstract Free-piston engine is a kind of linear internal combustion engine, and shows advantages on simple mechanical structure, low frictional losses, high thermal efficiency and operational flexibility. In this research, an experimental test rig of a dual-piston air-driven free-piston linear expander (FPLE) is established using the FPE concept. A linear generator is used to convert the mechanical work of the pistons into electricity during the expansion process. The piston dynamics, the output voltage of the generator, and the expander operation frequency, as well as the system energy conversion efficiency are identified. It is observed that the piston displacement profile is similar with a sinusoidal wave. The piston is found to run at relative high speed during the middle stroke, and peak velocity is usually achieved when the piston approaches the middle stroke. The output voltage of the generator is sensitive with the piston velocity. With higher driven pressure, the expander frequency is higher. The energy conversion efficiency increases with higher driven pressure and can reach up to 55% with a driven pressure of 3.75 bar. This research presents a fundamental analysis of a FPLE prototype, which can be used as a guidance for the future design of this FPLE type.

With the increasing adoption of lithium-ion batteries in energy storage systems, accurately monitoring the State of Health (SoH) of retired batteries has become a pivotal technology for ensuring their safe utilization and maximizing their economic value. In response to this need, this paper presents a highly efficient estimation model based on the multi-input metabolic gated recurrent unit (MM-GRU). The model leverages constant-current charging time, charging current area, and the 1800 s voltage drop as input features and dynamically updates these features through a metabolic mechanism. It requires only four cycles of historical data to reliably predict the SoH of subsequent cycles. Experimental validation conducted on retired Samsung and Panasonic battery cells and packs under constant-current and dynamic operating conditions demonstrates that the MM-GRU model effectively tracks SoH degradation trajectories, achieving a root mean square error of less than 1.2% and a mean absolute error of less than 1%. Compared to traditional machine learning algorithms such as SVM, BPNN, and GRU, the MM-GRU model delivers superior estimation accuracy and generalization performance. The findings suggest that the MM-GRU model not only significantly enhances the breadth and precision of SoH monitoring for retired batteries but also offers robust technical support for their safe deployment and asset optimization in energy storage systems.

In order to guarantee safe and reliable operation of electric vehicle batteries and to optimize their energy and capacity utilization, it is indispensable to estimate their state-of-charge (SoC). This study aimed to develop a novel estimation approach based on the grey model (GM) and genetic algorithms without the need of a high-fidelity battery model demanding high computation power. A SoC analytical model was established using the grey system theory based on a limited amount of incomplete data in contrast with conventional methods. The model was further improved by applying a sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the genetic algorithms were introduced to identify the optimal adjustment coefficient λ in a traditional grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of lithium-ion batteries were used as the device-under-test that underwent typical passenger car driving cycles. The proposed SoC estimation method were verified under diverse battery discharging conditions and it demonstrated superior accuracy and repeatability compared to the benchmarking GM method.

An accurate remaining useful life (RUL) prediction method is significant to optimize the lithium-ion batteries' performances in an intelligent battery management system. Since the construction of battery models and the initialization of algorithms require a large amount of data, it is difficult for conventional methods to guarantee the RUL prediction accuracy when the available data are insufficient. To solve this problem, a synergy of sliding-window grey model (SGM) and particle filter (PF) is exploited to build an innovative framework for battery RUL prediction. The SGM is adopted to explore the modelling of battery capacity degradation, and it characterizes the capacity changes during the battery's life-time with a few data (eg, 8 data points). To promote the accuracy and traceability of prediction, the development coefficient of the SGM, which can dynamically reflect the capacity degradation, is extracted to update the state variables of state transition function in PF. Accordingly, the fusion of SGM and PF (SGM-PF) can extrapolate the changes of the capacity and realize RUL prediction using fewer data. Furthermore, the performances of SGM-PF are comprehensively validated using two types of batteries aged under different conditions. The RUL prediction results reveal that the SGM-PF framework can achieve precise and reliable predictions in different prediction horizons with as few as 8 data points, and it has prominent performance in accuracy and stability over contrastive methods, especially in long-term prognosis.

Accurate and effective battery state-of-health (SoH) monitoring is significant to guarantee the security and dependability of electrical equipment. However, adapting SoH estimation methods to diverse battery kinds and operating conditions is a challenge because of the intricate deterioration mechanisms of batteries. To solve the issue, in this article, a novel multi-input metabolic long short-term memory (MM-LSTM) framework is developed. A degradation state model is created with the LSTM network to describe the intricate deterioration mechanisms. To convey more information about battery aging, the capacity degradation, sample entropy of discharge voltage, and ohmic internal resistance increment are extracted as the inputs of the model. To estimate SoH with a few data, the metabolic mechanisms are introduced to update the inputs and reflect the latest developments in aging. The accuracy and robustness of the proposed MM-LSTM framework are verified in different aspects using two kinds of batteries, and the maximum estimation error of SoH is within 1.98%. The findings indicate that the MM-LSTM framework implements the transfer application of SoH estimate successfully, and the framework’s versatility has been proven.