Abstract
With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.
Highlights
Since the urgent demand for a decrease in global petroleum consumption and environmental depredation, the exploration of renewable energy has become a global consensus
It consists of several major components: (1) a lithium-ion ferrous phosphate battery is used as the experimental objects of which the nominal capacity is 20 Ah and nominal voltage is 24 V; (2) a power battery test system (Arbin EVTS) with control accuracy which is less than ±0.1% FSR is used for lithium-ion battery (LIB) charging/discharging under various working condition; (3) a programmable temperature chamber is applied to control ambient temperature; (4) a host computer with MITS Pro v7.0 and a computer workstation with MATLAB R2012a are used for data acquisition and data simulation in the experiments, respectively
CV procedure; after standing for two hours, the measured terminal voltage can be used as the open-circuit voltage (OCV) of 100% state of charge (SOC). e test steps are as follows: the LIB is discharged to 98% SOC with 10A (0.5 C) constant current; after standing for two hours, the measured terminal voltage can be used as the OCV of 98% SOC
Summary
Since the urgent demand for a decrease in global petroleum consumption and environmental depredation, the exploration of renewable energy has become a global consensus. E SOC of LIB is essentially dependent on various SOC estimation methods based on its relevant characteristics, such as charge-discharge current, temperature, and terminal voltage [4, 5]. The model-free-based method is developed as a black box, and the nonlinear relationship between parameters and state is established by learning the train data such as terminal voltage, charge-discharge current, temperature, and cycles. Several model-free-based methods including neural network (NN) [10, 11] and fuzzy logic [12, 13] are developed for SOC estimation with positive results These methods can reduce BMS’s dependency on the accuracy of measurement data, the BMS is subjected to its processing power, storage capacity, and operational cost. E simulation result validates that the proposed SOC estimator established by the combination of AEKF-PID and one-order evenin battery model could adapt itself to the nonlinear characteristics of the LIB system.
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