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Jichang Peng

Bio: Jichang Peng is an academic researcher from Nanjing Institute of Technology. The author has contributed to research in topics: Battery (electricity) & Recursive least squares filter. The author has an hindex of 2, co-authored 3 publications receiving 38 citations.

Papers
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Journal ArticleDOI
TL;DR: An optimization process with nondominated sorting genetic algorithm II (NSGA-II) is proposed to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test and the degradation features in this article are the knee points at the transfer instants of the voltage.
Abstract: As a favorable energy storage component, lithium-ion (Li-ion) battery has been widely used in the battery energy storage systems (BESS) and electric vehicles (EV). Data driven methods estimate the battery state-of-health (SOH) with the features extracted from the measurement. However, excessive features may reduce the estimation accuracy and also increases the human labor in the lab. By proposing an optimization process with nondominated sorting genetic algorithm II (NSGA-II), this article is able to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test. NSGA-II optimizes the entire process of establishing a SOH estimator considering both the measurement cost of the feature and the estimation accuracy. A series of nondominated solutions are obtained by solving the multiobjective optimization problem, which also provides more flexibility to establish the SOH estimator at various conditions. The degradation features in this article are the knee points at the transfer instants of the voltage in the short-term current pulse test, which is fairly convenient and easy to be obtained in real applications. The proposed method is validated on the measurement from two LiFePO4/C batteries aged with the mission profile providing the primary frequency regulation (PFR) service to the grid.

85 citations

Journal ArticleDOI
TL;DR: An optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient Soh estimation framework.
Abstract: Current pulses are convenient to be actively implemented by a Battery Management System (BMS). However, the Short-Term Features (STF) from current pulses originate from various sensors with uneven qualities, which hinders one powerful and strong learner with STF for the battery SOH estimation. This paper thus proposes an optimized weak learner formulation procedure for Lithium-ion (Li-ion) battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto Front-based Selection Strategy (PFSS) is designed to select the representative solutions from the non-dominated solutions fed by a Knee point driven Evolutionary Algorithm (KnEA), which guarantees both the diversity and accuracy of the weak learners. Afterwards, the weak learners, whose coefficients are obtained by Self-adaptive Differential Evolution (SaDE), are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing Primary Frequency Regulation (PFR) service to the grid and one constant current profile.

38 citations

Journal ArticleDOI
TL;DR: Refined Instrumental Variable estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise.
Abstract: Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: The five most studied types of ML algorithms for battery SOH estimation are systematically reviewed and it can be concluded that amongst these methods, support vector machine and artificial neural network algorithms are still research hotspots.

112 citations

Journal ArticleDOI
01 May 2022-Energy
TL;DR: In this paper , an improved feedforward-long short-term memory (FF-LSTM) modeling method is proposed to realize an accurate whole-life-cycle state of charge (SOC) prediction by effectively considering the current, voltage, and temperature variations.

105 citations

Journal ArticleDOI
01 Oct 2021-Energy
TL;DR: This work combined the empirical mode decomposition (EMD) method and backpropagation long-short-term memory (B-LSTM) neural network (NN) to develop SOH estimation and RUL prediction models that have high robustness, good accuracy, and applicability.

80 citations

Journal ArticleDOI
TL;DR: In this paper , the evolutions and challenges of state-of-the-art battery technologies and battery management technologies for hybrid and pure EVs are reviewed, revealing the major features, pros and cons, new technological breakthroughs, future challenges, and opportunities for advancing electric mobility.

80 citations

Journal ArticleDOI
01 Sep 2020-Energy
TL;DR: A novel ensemble learning framework to estimate the battery SOH, which can boost the performance of the data driven SOH estimation through a well-designed integration of the weak learners is proposed.

62 citations