• Energy Research

Abstract An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are fed to train a deep neural network to estimate the internal concentrations and potentials in the electrodes and the electrolyte at different spatial positions. The results show that the proposed approach is capable of bridging spatial, temporal and chemical complexity and achieves a maximum error of 2.93% for all the estimated states under new ambient temperatures, indicating high reliability and generalization ability with solid robustness to input noises and outperforming the one-dimensional network under both normal and noisy conditions.

In this paper, an optimal control strategy for a four-wheel-independently-actuated electric vehicle (FWIA EV) is proposed to improve vehicle dynamics stability and handling performance. The proposed scheme has a hierarchical structure composed of an upper and a lower controller. The desired longitudinal and lateral forces and yaw moment are determined based on the sliding-mode control (SMC) scheme in the upper controller, which takes the longitudinal and lateral velocity and the yaw rate as control variables. In the lower controller, an optimization algorithm is adopted to allocate the driving/braking torques to each in-wheel motor. A cost function with adjustable weight coefficients is specially designed by taking the motor power capability and the tire workload into consideration. The simulation and hardware-in-loop experimental results show that the proposed control strategy exhibits superior performance in comparison to commonly-used rule-based control strategies, and has the capability of online implementation.

Abstract Ultracapacitors (UCs) have been widely recognized as an enabling energy storage technology in various industrial applications. They hold several advantages including high power density and exceptionally long lifespan over the well-adopted battery technology. Accurate modeling and State-of-Charge (SOC) estimation of UCs are essential for reliability, resilience, and safety in UC-powered system operations. In this paper, a novel fractional-order model composed of a series resistor, a constant-phase-element (CPE), and a Walburg-like element, is proposed to emulate the UC dynamics. The Grunald-Letnikov derivative (GLD) is then employed to discretize the continuous-time fractional-order model. The model parameters are optimally extracted using genetic algorithm (GA), based on the time-domain data acquired through the Federal Urban Driving Schedule (FUDS) test. By means of this fractional-order model, a fractional Kalman filter is synthesized to recursively estimate the UC SOC. Validation results prove that the proposed fractional-order modeling and state estimation scheme is accurate and outperforms current practice based on integer-order techniques.

Abstract Electrochemical energy storage systems play an important role in diverse applications, such as electrified transportation and integration of renewable energy with the electrical grid. To facilitate model-based management for extracting full system potentials, proper mathematical models are imperative. Due to extra degrees of freedom brought by differentiation derivatives, fractional-order models may be able to better describe the dynamic behaviors of electrochemical systems. This paper provides a critical overview of fractional-order techniques for managing lithium-ion batteries, lead-acid batteries, and supercapacitors. Starting with the basic concepts and technical tools from fractional-order calculus, the modeling principles for these energy systems are presented by identifying disperse dynamic processes and using electrochemical impedance spectroscopy. Available battery/supercapacitor models are comprehensively reviewed, and the advantages of fractional types are discussed. Two case studies demonstrate the accuracy and computational efficiency of fractional-order models. These models offer 15–30% higher accuracy than their integer-order analogues, but have reasonable complexity. Consequently, fractional-order models can be good candidates for the development of advanced battery/supercapacitor management systems. Finally, the main technical challenges facing electrochemical energy storage system modeling, state estimation, and control in the fractional-order domain, as well as future research directions, are highlighted.

Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This article presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. The open-circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis’ operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least-squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate the multiparameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.

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.

Abstract Energy storage system plays an important role in modern power systems for mitigating the variation and intermittency of renewable energy sources. The Lithium-ion battery is currently the most widely used solution for energy storage system. However, its high cost is considered as one of the major barriers hampering the integration of renewable energy and the adoption of electric vehicles. Reusing electric vehicle batteries seems a promising solution to the aforementioned problem. Based on a dynamic degradation model of Lithium-ion batteries, this paper first compares the profits that second-life and fresh batteries can bring to the wind farm. Model predictive control is adopted to solve an hourly optimal wind scheduling problem and maximize the profit of wind farm owner. The optimal size of battery is determined and then the comparison of second-life and fresh batteries is conducted taking the battery degradation, the profit of the wind farm owner, and various remaining capacities of battery into account. Two case studies in USA and Denmark are conducted and the analysis shows that given the current prices of wind energy and Lithium-ion batteries, reusing batteries is not worthwhile for the studied wind farms, but it may outperform fresh batteries in the future if the wind energy price decreases much faster than the battery price.