• Energy Research

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.

Abstract Given the popularity of Lithium-ion batteries in EVs (electric vehicles), predicting the capacity quickly and accurately throughout a battery's full life-time is still a challenging issue for ensuring the reliability of EVs. This paper proposes an approach in predicting the varied capacity with discharge cycles based on metabolic grey theory and consider issues from two perspectives: 1) three metabolic grey models will be presented, including MGM (metabolic grey model), MREGM (metabolic Residual-error grey model), and MMREGM (metabolic Markov-residual-error grey model); 2) the universality of these models will be explored under different conditions (such as various discharge rates and temperatures). Furthermore, the research findings in this paper demonstrate the excellent performance of the prediction depending on the three models; however, the precision of the MREGM model is inferior compared to the others. Therefore, we have obtained the conclusion in which the MGM model and the MMREGM model have excellent performances in predicting the capacity under a variety of load conditions, even using few data points for modeling. Also, the universality of the metabolic grey prediction theory is verified by predicting the capacity of batteries under different discharge rates and different temperatures.

The lithium-ion battery plays a crucial role in the power supply of the electric vehicles (EVs). Battery remaining useful life (RUL) is critically vital to ensure the vehicles’ safety and reliability. Due to the complicated aging mechanism, predicting RUL for the battery management systems (BMSs) is challenging. In this article, a novel degradation indicator was constructed using the information extracted from the discharge voltage. The indicator reflected the complete and effective energy information from the voltage signals to reveal battery degradation characteristics. Additionally, an innovative fractional grey model (FRGM) unscented particle filter (UPF) framework was developed for RUL prediction in this article. To improve the accuracy and traceability of prediction, the framework adopted a novel FRGM to update the state transition equation in UPF. Meanwhile, the UPF was employed to extrapolate trends of the indicator and achieve the RUL prediction. The performances of FRGM-UPF with the degradation indicator were synthetically verified by the data from various types of batteries under different aging tests. The experimental results indicated that the proposed method could achieve precise prediction results and had a wide range of practicability and universality. The developed technologies could be incorporated with the other control algorithms for application in BMS of EVs.

Abstract In this study a grey extended Kalman filter and a novel open-circuit voltage model for the estimation of the state of charge of lithium-ion batteries are presented. To eliminate the influence of truncation error, this study utilizes a grey prediction model to deal with the state prediction problem. In order to further improve the accuracy of state of charge estimation, a novel open-circuit voltage model based on cubic-Hermite interpolation is also proposed to update the state estimate. Moreover, the accuracy of the proposed open-circuit voltage model is verified in terms of the following two aspects: capacity estimation and state of charge estimation. The accuracy and convergence of the grey extended Kalman filter is analyzed for different types of dynamic loading conditions, including the Urban Dynamometer Driving Schedule and the New European Driving Cycle. The experimental results show that the proposed approach offers good accuracy for the estimation of the state of charge. The experimental results show good agreement with the estimation results, and the proposed method can effectively improve the accuracy of extended Kalman filter.

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.

Abstract Battery health monitoring and management is critically important for electric vehicle performance and economy. This paper presents a multiple health indicators-based and machine learning-enabled state-of-health estimator for prognostics and health management. The multiple online health indicators without the influence of different loading profiles are used as effective signatures of the health estimator for effective quantification of capacity degradation. An extreme learning machine is introduced to capture the underlying correlation between the extracted health indicators and capacity degradation to improve the speed and accuracy of machine learning for online estimation. The proposed estimator is also compared to the traditional BP neural network. The associated results indicate that the maximum estimation error of the proposed health management strategy is less than 2.5%, and it has better performance and faster speed than the BP neural network.