Abstract
Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.
Highlights
Ahmed RachidCurrently, the traditional automobile industry is one of the main contributors to the global greenhouse effect and oil crisis, and the development of new electric vehicles is evolving
The specific layout of this paper is as follows: Section 2 gives a detailed introduction to the fractional order lithium battery model; Section 3 describes the identification of the internal parameters of integer order battery and fractional order battery models, and compares their respective effects; Section 4 initially introduces and analyzes the UKF and H infinity (HIF) algorithms, proposes a fractional order modelbased unscented and H-infinity (FOUHIF) estimation algorithm; Section 5 describes the experiment carried out under complex working conditions and compares the performances of the FOUHIF algorithm, the fractional order unscented Kalman filter (FOUKF) algorithm, and the integer-order unscented Kalman filter (UKF) algorithm
This platform mainly consisted of the following components: (1) an electric vehicle battery test system (EVTS) produced by Arbin (USA) with a measurement accuracy of ±0.1%FSR, (2) a constant temperature and moisture testing machine produced by Giant Force Company for controlling the ambient temperature of the lithium battery, (3) an upper computer for collecting the lithium battery parameters, (4) a battery pack composed of three ternary lithium batteries
Summary
The traditional automobile industry is one of the main contributors to the global greenhouse effect and oil crisis, and the development of new electric vehicles is evolving. The third method is an estimation process based on the battery characterization parameters It is mainly used in laboratory environments to identify the relationship between the parameters (including the open-circuit voltage and remaining capacity) and the SOC of the lithium battery. This method is convenient and accurate, but not suitable for practical application. Compared with integer order models, fractional order equivalent circuit lithium battery models can more comprehensively depict the internal chemical reaction mechanism of the battery, resulting in improved SOC estimation accuracy [19,20]. The specific layout of this paper is as follows: Section 2 gives a detailed introduction to the fractional order lithium battery model; Section 3 describes the identification of the internal parameters of integer order battery and fractional order battery models, and compares their respective effects; Section 4 initially introduces and analyzes the UKF and H infinity (HIF) algorithms, proposes a fractional order modelbased unscented and H-infinity (FOUHIF) estimation algorithm; Section 5 describes the experiment carried out under complex working conditions and compares the performances of the FOUHIF algorithm, the fractional order unscented Kalman filter (FOUKF) algorithm, and the integer-order unscented Kalman filter (UKF) algorithm
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