A strategy for grid power peak shaving and valley filling using vehicle-to-grid systems (V2G) is proposed. The architecture of the V2G systems and the logical relationship between their sub-systems ...are described. An objective function of V2G peak-shaving control is proposed and the main constraints are formulated. The influences of the number of connected EVs and the average value of the target curve are analyzed. The rms and the standard deviation of the difference between the target and planned curves are proposed as indices for measuring the degree of matching between the two curves. The simulation results demonstrate that peaking shaving using V2G can be effective and controllable, and the proposed control algorithm is feasible.
Precisely battery state of health estimation and remaining useful lifetime prediction are crucial factors in ensuring the reliability and safety for system operation. This paper thus focuses on the ...short-term battery state of health estimation and long-term battery remaining useful lifetime prediction. A novel hybrid method by fusion of partial incremental capacity and Gaussian process regression is proposed and dual Gaussian process regression models are employed to forecast battery health conditions. First, the initial incremental capacity curves are filtered by using the advanced signal process technology. Second, the important health feature variables are extracted from partial incremental capacity curves using correlation analysis method. Third, the Gaussian process regression is applied to model the short-term battery SOH estimation using the feature variables. Forth, an autoregressive long-term battery remaining useful lifetime model is established using the results of battery SOH values and previous output. The predictive capability and effectiveness of two models are demonstrated by four battery datasets under different cycling test conditions. Otherwise, the robustness of the two models is verified using four datasets with different health levels. The experimental results show that the proposed method can provide accurate battery state of health estimation and remaining useful lifetime.
•Dual GPR-based models are proposed to establish battery degradation models.•The health indexes are extracted from partial IC curves as model input features.•Correlational coefficient analysis method is applied to extract feature variables.•An autoregressive RUL model is developed using the capacity vs. cycle number.•Four batteries with different initial health levels are used to verify robustness.
Supercapacitors (SCs) have high power density and exceptional durability. Progress has been made in their materials and chemistries, while extensive research has been carried out to address ...challenges of SC management. The potential engineering applications of SCs are being continually explored. This paper presents a review of SC modeling, state estimation, and industrial applications reported in the literature, with the overarching goal to summarize recent research progress and stimulate innovative thoughts for SC control/management. For SC modeling, the state-of-the-art models for electrical, self-discharge, and thermal behaviors are systematically reviewed, where electrochemical, equivalent circuit, intelligent, and fractional-order models for electrical behavior simulation are highlighted. For SC state estimation, methods for State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are covered, together with an underlying analysis of aging mechanism and its influencing factors. Finally, a wide range of potential SC applications is summarized. Particularly, co-working with high energy-density devices constitutes hybrid energy storage for renewable energy systems and electric vehicles (EVs), sufficiently reaping synergistic benefits of multiple energy-storage units.
Tremendous efforts are being made to develop electrode materials, electrolytes, and separators for energy storage devices to meet the needs of emerging technologies such as electric vehicles, ...decarbonized electricity, and electrochemical energy storage. However, the sustainability concerns of lithium-ion batteries (LIBs) and next-generation rechargeable batteries have received little attention. Recycling plays an important role in the overall sustainability of future batteries and is affected by battery attributes including environmental hazards and the value of their constituent resources. Therefore, recycling should be considered when developing battery systems. Herein, we provide a systematic overview of rechargeable battery sustainability. With a particular focus on electric vehicles, we analyze the market competitiveness of batteries in terms of economy, environment, and policy. Considering the large volumes of batteries soon to be retired, we comprehensively evaluate battery utilization and recycling from the perspectives of economic feasibility, environmental impact, technology, and safety. Battery sustainability is discussed with respect to life-cycle assessment and analyzed from the perspectives of strategic resources and economic demand. Finally, we propose a 4H strategy for battery recycling with the aims of high efficiency, high economic return, high environmental benefit, and high safety. New challenges and future prospects for battery sustainability are also highlighted.
This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural ...networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively.
Lithium-ion batteries (LIBs) are being intensively studied and universally used as power sources for electric vehicle applications. Despite the staggering growth in sales of LIBs worldwide, thermal ...safety issues still turn out to be the most intolerable pain point, and remain the focus of research for technological improvements. This paper presents a comprehensive overview on thermal safety issues of LIBs, in terms of thermal behavior and thermal runaway modeling and tests for battery cells, and safety management strategies for battery packs. Considering heat generation mechanism and thermal characteristics of LIBs, heat generation, dissipation and accumulation inside a cell are elaborated. The triggering factors leading to thermal runaway are also summarized. Finally, thermal runaway detection and prevention strategies for both cell- and pack-levels are introduced. Different engineering approaches from material refinement and additive adoption to thermal, electrical, and mechanical design are presented for thermal runaway prevention.
Advanced lithium-ion battery systems, in multi-cell configurations and larger-scale operations, are being currently developed for energy storage applications. Furthermore, the retired batteries are ...being increasingly second utilized in energy storage scenes. Thus, realistic and accurate battery state of health diagnosis and related aging mechanisms identification is desired to improve the battery management and control, and eventually guarantee the reliability and safety of the battery system. A half-cell model based battery state of health diagnostic method is proposed to investigate the aging mechanisms and possible attribute to the capacity fade in a quantitative manner. Using particle swarm optimization algorithm, the half-cell model is parameterized to quantify the battery degradation mechanisms derived from the parameter variations, which describe the electrode behavior with proper matching ratio and their evolutions at different battery aging levels. The reliability and robustness of the approach has been verified and evaluated by the database of the cells experienced different aging paths. Our approach is a data-model fusion method to offer the benefits of wide applicability to various cell chemistries and operating modes.
Battery State-of-Health (SOH) estimation is of utmost importance for the performance and cost-effectiveness of electric vehicles. Incremental capacity analysis (ICA) has been ubiquitously used for ...battery SOH estimation. However, challenges remain with regard to the characteristic parameter selection, estimation viability and feasibility for practical implementation. In this paper, a novel ICA-based method for battery SOH estimation is proposed, with the goals to identify the most effective characteristic parameters of IC curves, optimize the SOH model parameters for better prediction accuracy and enhance its applicability in realistic battery management systems. To this end, the IC curve is first derived and filtered using the wavelet filtering, with the peak value and position extracted as health factors (HFs). Then, the correlations between SOH and HFs are explored through the grey correlation analysis. The SOH model is further established based on the Gaussian process regression (GPR), in which the optimal hyper parameters are calculated through the conjugate gradient method and the multi-island genetic algorithm (MIGA). The effects of different HFs and kernel functions are also analyzed. The effectiveness of the proposed MIGA-GPR SOH model is validated by experimentation.
An X-by-wire chassis can improve the kinematic characteristics of human-vehicle closed-loop system and thus active safety especially under emergency scenarios via enabling chassis coordinated ...control. This paper aims to provide a complete and systematic survey on chassis coordinated control methods for full X-by-wire vehicles, with the primary goal of summarizing recent reserch advancements and stimulating innovative thoughts. Driving condition identification including driver's operation intention, critical vehicle states and road adhesion condition and integrated control of X-by-wire chassis subsystems constitute the main framework of a chassis coordinated control scheme. Under steering and braking maneuvers, different driving condition identification methods are described in this paper. These are the trigger conditions and the basis for the implementation of chassis coordinated control. For the vehicles equipped with steering-by-wire, braking-by-wire and/or wire-controlled-suspension systems, state-of-the-art chassis coordinated control methods are reviewed including the coordination of any two or three chassis subsystems. Finally, the development trends are discussed.
The battery is a key component and the major fault source in electric vehicles (EVs). Ensuring power battery safety is of great significance to make the diagnosis more effective and predict the ...occurrence of faults, for the power battery is one of the core technologies of EVs. This paper proposes a voltage fault diagnosis detection mechanism using entropy theory which is demonstrated in an EV with a multiple-cell battery system during an actual operation situation. The preliminary analysis, after collecting and preprocessing the typical data periods from Operation Service and Management Center for Electric Vehicle (OSMC-EV) in Beijing, shows that overvoltage fault for Li-ion batteries cell can be observed from the voltage curves. To further locate abnormal cells and predict faults, an entropy weight method is established to calculate the objective weight, which reduces the subjectivity and improves the reliability. The result clearly identifies the abnormity of cell voltage. The proposed diagnostic model can be used for EV real-time diagnosis without laboratory testing methods. It is more effective than traditional methods based on contrastive analysis.
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