A Reliable Approach of Differentiating Discrete Sampled-Data for Battery Diagnosis
Total Page：16
File Type：pdf, Size：1020Kb
eTransportation 3 (2020) 100051 Contents lists available at ScienceDirect eTransportation journal homepage: www.journals.elsevier.com/etransportation A reliable approach of differentiating discrete sampled-data for battery diagnosis * ** Xuning Feng a, b, Yu Merla c, Caihao Weng d, Minggao Ouyang b, , Xiangming He a, , Bor Yann Liaw e, Shriram Santhanagopalan f, Xuemin Li f, Ping Liu g, Languang Lu b, Xuebing Han b, Dongsheng Ren b, Yu Wang b, Ruihe Li b, Changyong Jin b, h, *** Peng Huang b, h, Mengchao Yi b, i, Li Wang a, Yan Zhao c, Yatish Patel c, Gregory Offer c, a Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China b State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China c Department of Mechanical Engineering, Imperial College London, Exhibition Road, South Kensington Campus, London, SW7 2AZ, UK d Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, 48109, USA e Idaho National Laboratory, P.O. Box 1625, Idaho Falls, ID, 83415, USA f Transportation and Hydrogen System Center, National Renewable Energy Laboratories, Golden, CO, 80401, USA g Department of NanoEngineering, University of California, San Diego, CA, 92093-0448, USA h College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China i College of Engineering, China Agricultural University, Beijing, 100083, China article info abstract Article history: Over the past decade, major progress in diagnosis of battery degradation has had a substantial effect on Received 14 January 2020 the development of electric vehicles. However, despite recent advances, most studies suffer from fatal Received in revised form flaws in how the data are processed caused by discrete sampling levels and associated noise, requiring 13 February 2020 smoothing algorithms that are not reliable or reproducible. We report the realization of an accurate and Accepted 16 February 2020 reproducible approach, as “Level Evaluation ANalysis” or LEAN method, to diagnose the battery degra- Available online 22 February 2020 dation based on counting the number of points at each sampling level, of which the accuracy and reproducibility is proven by mathematical arguments. Its reliability is verified to be consistent with Keywords: fi Battery aging previously published data from four laboratories around the world. The simple code, exact tting, Lithium-ion batteries consistent outcome, computational availability and reliability make the LEAN method promising for Energy storage vehicular application in both the big data analysis on the cloud and the online battery monitoring, Differential analysis supporting the intelligent management of power sources for autonomous vehicles. Incremental capacity analysis © 2020 Published by Elsevier B.V. Differential thermal voltammetry 1. Introduction electrochemical power sources [9e11]. Yet, degradation is highly unpredictable creating huge uncertainties over lifetime and total The world is becoming increasingly reliant on batteries for cost of ownership, setting a critical barrier for their widespread multiple facets, such as portable electronic devices [1,2], electric economic adoption. Researchers have developed a wide range of vehicles [3e5], and stationary electrical storage [6]. Long cycle life diagnostic techniques that can be used to understand degradation is one of the most significant features for the batteries in applica- [12,13]. These techniques range from destructive ex situ experi- tion [7,8]. The diagnosis of battery degradation is of great signifi- ments to in situ experiments. A number of the most promising in cance in the intelligent energy management of all kinds of situ techniques, particularly those with the potential to be used during application, can be classified as ‘differential techniques’, which compare the rate of change in current to the rate of change in voltage, or vice versa, such as the incremental capacity analysis * Corresponding author. (ICA) or the differential voltage analysis (DVA), or the rate of change ** Corresponding author. in temperature to the rate of change in voltage, such as the differ- *** Corresponding author. E-mail addresses: [email protected] (M. Ouyang), [email protected] ential thermal voltammetry (DTV). (X. He), [email protected] (G. Offer). In 1967, Balewski and Brenet [14] published the first paper using https://doi.org/10.1016/j.etran.2020.100051 2590-1168/© 2020 Published by Elsevier B.V. 2 X. Feng et al. / eTransportation 3 (2020) 100051 ICA to characterize the electrochemical reactivity of manganese signals with sampling levels and significant noise introduces mul- dioxide. Dahn et al. [15] and Tillement and Quarton [16] endorsed tiple problems [28], as shown in Fig. 1, requiring data to be the use of ICA by applying it to the characterization of the elec- manipulated in various ways before it can be used. There is trochemical insertion of cell materials. Since 2005, Dubarry et al. currently no standardized approach to manipulate data, and [17,18] have pioneered the application of ICA for full-cell degrada- therefore, it is often not possible to compare data. The challenge of tion analysis. The ICA technique is similar to cyclic voltammetry calculating the differentiation between discrete sampling signals (CV) [19] in characterizing battery behaviour [20,21] but is typically that contain levels due to digital sampling with specific resolution faster and can also be applied to characterize the performance of and noise has thwarted scientists for years. An intuitive solution is large-format batteries [22]. Using ICA to characterize battery per- to transfer signals with discrete levels and random noise into formance has received increasing attention, and the number of smoothed and differentiable functions, so-called “curve fitting”,as research papers has continuously increased over the past five years. the fitted curves have continuous functions that are more easily DVA is complementary to ICA because their results are reciprocals differentiable. [19]. Bloom et al. [23] first used DVA to characterize capacity In 1964, Savitzky and Golay [29] proposed an approach to degradation, followed by Honkura et al. [24] and others [25,26]. In a smooth and differentiate sampled data using piecewise polynomial significant recent development, Wu et al. [27] proposed a new curve fitting with optimality obtained by least squares fitting. The approach termed differential thermal voltammetry (DTV) to track method has been widely accepted as the famous SG method and battery degradation in a similar fashion to ICA but using temper- has been cited more than 15,000 times. Craven and Wahba [30] ature as the alternative signal to current, which makes it possible to improved upon the SG method, which can give large fluctuations in analyse cells in parallel and does not require constant temperature the results of high-order polynomial coefficients, by regulating the conditions. derivatives using generalized cross-validation techniques. Samad However, there is a challenge that prevents ICA, DVA and DTV et al. [31] successfully applied the SG method to calculate the ICA from becoming standardized tools for degradation analysis, either curves for battery degradation analysis. In recent years, techniques in the lab or during application. Taking derivatives of discrete for optimal curve fitting have been greatly improved upon through Fig. 1. Overview of the method and motivation. a (Left) The voltage curve under 1/3C rate charging for a 60Ah commercial lithium-ion cell with LiFePO4/graphite electrodes. The magnified figure shows the actual raw data, including sampling levels and noise. a (Right) The ideal IC curve that contains three characteristic peaks, which reflect the phase changes in the graphite anode. a (Middle-Top) The traditional method that uses smoothing techniques first and then calculates the differentiation of the smoothed function. a (Middle- Bottom) The proposed method, termed Level Evaluation Analysis (LEAN), which directly counts the numbers at each voltage level to acquire the IC curve. b The problem of the traditional method. First, multiple optimal results of curve fitting are possible during smoothing. Second, over-fitting/under-fitting misrepresents the shape and location of the IC peaks. c Comparison of the computational complexity G(n). G(n) is usually Q(n2) for the curve-fitting method, whereas G(n) ¼ Q(n) for the proposed LEAN method. X. Feng et al. / eTransportation 3 (2020) 100051 3 the use of artificial intelligence [32]. Weng et al. [33] and Wang (both charging and discharging) from 1/3Ce2C, and also at et al. [34] used support vector machines, which is a basic technique different aging status. in pattern recognition for kernel-based curve fitting, to smooth the sampling data and calculate the ICA curves by linearized support 3. Methods vector regression (SVR). Weng et al. [35] further extended the usage of SVR from processing the IC curves for cells to that for a battery 3.1. The sampling data of battery tests pack. However, smoothing approaches, including both SG and SVR, The sampling data of battery tests usually displays the stair-like share inherent deficiencies: ① The adjustable parameters in the feature as shown in Fig. 2(a), when we magnify the voltage curves smoothing algorithms influence the result of curve fitting, espe- to see the details. The stair-like feature is naturally formed when cially the peak position and height of the IC curve,