A Novel Data-Driven Fast Capacity Estimation of Spent Electric Vehicle Lithium-ion Batteries

Abstract

Fast capacity estimation is a key enabling technique for second-life of lithium-ion batteries due to the hard work involved in determining the capacity of a large number of used electric vehicle (EV) batteries. This paper tries to make three contributions to the existing literature through a robust and advanced algorithm: (1) a three layer back propagation artificial neural network (BP ANN) model is developed to estimate the battery capacity. The model employs internal resistance expressing the battery's kinetics as the model input, which can realize fast capacity estimation; (2) an estimation error model is established to investigate the relationship between the robustness coefficient and regression coefficient. It is revealed that commonly used ANN capacity estimation algorithm is flawed in providing robustness of parameter measurement uncertainties; (3) the law of large numbers is used as the basis for a proposed robust estimation approach, which optimally balances the relationship between estimation accuracy and disturbance rejection. An optimal range of the threshold for robustness coefficient is also discussed and proposed. Experimental results demonstrate the efficacy and the robustness of the BP ANN model together with the OPEN ACCESSEnergies 2014, 7 8077 proposed identification approach, which can provide an important basis for large scale applications of second-life of batteries.

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

“…Hybrid models: Combining models Kalman filter [6,9] and data to achieve High cost for model development Particle filter (PF) [7,8] a better performance Data-driven approach Neural networks [11,12,18] Simple Large amount of data are required Gaussian process regression (GPR) [13] Relevance vector machine (RVM) [14] Practical Only for short-term prediction SVR and PF [15] High accuracy Hybrid approach GPR and PF [16] Combines advantages Complex RVM and PF [17] of different approaches…”

“…These model-based approaches can achieve high estimation accuracy, but they also require heavy work in the model development. For the data-driven approach [11][12][13][14], it is not necessary to understand the degradation principle of the battery; it only uses degradation data to build the degradation model. For example, Rezvani et al [12] used an adaptive neural network (ANN) and linear prediction error method for the SOH quantification of a lithium-ion battery.…”

Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine

“…Therefore, high temperature can cause degradation, despite its ability to temporarily increase the battery performance. Li et al [27] demonstrated that the internal temperature of a battery Energies 2015, 8,[12439][12440][12441][12442][12443][12444][12445][12446][12447][12448][12449][12450][12451][12452][12453][12454][12455][12456][12457] not only functions as a safety precaution, but also provides external characteristic information, which can be utilized to assess the decrease in battery capacity. Figure 3 depicts the changes in the charge and discharge temperature of battery 7 in 168 cycles.…”

“…In MKRVM, each kernel function is a linear combination of different basic kernels. A typical multi-kernel function is a combination of a radial basic function (RBF) kernel and a polynomial kernel Energies 2015, 8,[12439][12440][12441][12442][12443][12444][12445][12446][12447][12448][12449][12450][12451][12452][12453][12454][12455][12456][12457] because the former is local and the latter is global. Given a set of N observations x i (i = 1, 2, .…”

“…To model the complex and nonlinear dependency between multiple features and battery capacity, scholars have applied various intelligent data-driven methods for online capacity estimation, such as neural network (NN) [6][7][8], support vector machine (SVM) [9][10][11][12], and relevance vector machine (RVM) [4,13]. The NN approach can be used to establish a network to characterize the relationship among various inputs (i.e., current, voltage, and temperature) and outputs (i.e., capacity).…”