A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Hua, Yinqun; Cordoba-Arenas, Andrea; Warner, Nicholas A.; Rizzoni, Giorgio

doi:10.1016/j.jpowsour.2015.01.112

Cited by 154 publications

(61 citation statements)

References 40 publications

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“…The NPF method was firstly proposed by Crassidis et al, for spacecraft attitude estimation and obtained satisfactory results [28]. It is a nonlinear optimum state estimation method implemented on continuous-discrete time systems, which is particularly suitable for battery systems [29]. In the NPF method, the nonlinear dynamic system is treated as a preliminary model with a to-be-determined model error part, where the preliminary model describes the system dynamic and the model error mainly represents the system process noise.…”

Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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Estimation of state of charge (SOC) is of great importance for lithium-ion (Li-ion) batteries used in electric vehicles. This paper presents a state of charge estimation method using nonlinear predictive filter (NPF) and evaluates the proposed method on the lithium-ion batteries with different chemistries. Contrary to most conventional filters which usually assume a zero mean white Gaussian process noise, the advantage of NPF is that the process noise in NPF is treated as an unknown model error and determined as a part of the solution without any prior assumption, and it can take any statistical distribution form, which improves the estimation accuracy. In consideration of the model accuracy and computational complexity, a first-order equivalent circuit model is applied to characterize the battery behavior. The experimental test is conducted on the LiCoO2 and LiFePO4 battery cells to validate the proposed method. The results show that the NPF method is able to accurately estimate the battery SOC and has good robust performance to the different initial states for both cells. Furthermore, the comparison study between NPF and well-established extended Kalman filter for battery SOC estimation indicates that the proposed NPF method has better estimation accuracy and converges faster.

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Section: Introductionmentioning

confidence: 99%

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Hua

et al. 2015

Energies

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“…Similarly, Caiyun () presents an adaptive bilinear‐filtered least square algorithm along with Lyapunov function to guarantee the stability of the error convergence in an active noise control system. Hua, Cordoba‐Arenas, Warner, and Rizzoni () use a nonlinear predictive filter to estimate states of health and charge measurements of lithium‐ion battery packs in hybrid electric cars. The nonlinear‐predictive filter processes the noise, and the process noise is also estimated as part of the solution.…”

Section: Adaptive And/or Intelligent Controlmentioning

confidence: 99%

Recent advances in control algorithms for smart structures and machines

Soto

Adeli

2017

Expert Systems

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This paper presents a state‐of‐the‐art review of recent control algorithms used primarily for vibration control of smart structures and machines. Control algorithms can be divided into open loop, feedback, feedforward, and combined feedback and feedforward control. Solutions proposed can be subdivided into (a) classical control, (b) optimal control, (c) robust control, (d) intelligent control, and (e) self‐sustaining solutions and energy harvesting used in centralized and decentralized configurations. The most recent research on novel autonomous control systems intersects distributed control theory, agent‐based modeling, bioinspired computational learning, and optimization algorithms.

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“…Normally, the weakest cell limiting power delivery is the cell with the largest impedance. The SOC of each cell also influences the power capability; in any case, in a full-featured BMS, the problem caused by SOC imbalance can be alleviated by cell balancing [27] or cell SOC estimation [28]. Thus, in this paper, the influence of SOC imbalance to power capability is neglected, and the main focus of this study is the influence of cell impedance.…”

Section: Power Prediction Of Series-connected Battery Packsmentioning

confidence: 99%

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

et al. 2017

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Abstract:The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.

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A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control

Cited by 154 publications

References 40 publications

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Estimation of State of Charge for Two Types of Lithium-Ion Batteries by Nonlinear Predictive Filter for Electric Vehicles

Recent advances in control algorithms for smart structures and machines

Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

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