With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

An active disturbance rejection control (ADRC) of induction motor based on adaptive particle swarm optimization (APSO) algorithm is proposed in this paper, in order to realize the precise decoupling of induction motor and the disturbance compensation. The novel control method employs APSO as the automatic tune mechanism for ADRC controller. According to the feedback information of induction motor, an optimal solution can be achieved via the optimization mechanism and self-learning ability of APSO, so the reliance of ADRC controller on parameters can be reduced. In order to make the optimal solution more reasonable and reliable, the aggregation degree and evolution speed are introduced into the APSO to dynamically modify the inertia weight based on the practical optimization process. Simulation and experimental results indicate that the robustness of the proposed optimal design method for ADRC is better than the conventional ADRC when the disturbances occur, and the method is feasible and effective.

High frequency (HF) voltage injection method can accurately estimate the rotor position of interior permanent magnet synchronous motor at low speed. However, the fixed injection frequency for traditional HF voltage injection method will produce a loud audible noise, which limits the actual application of HF voltage injection method. In order to reduce audible noise, a novel HF square-wave voltage injection method with the random switching frequency is proposed in this paper. The frequency of the injected HF voltage varies with the random switching frequency, so it can effectively spread power spectrum caused by HF current and PWM, which will effectively reduce the audible noise caused by the HF current and PWM. Additionally, the power spectral density of the HF current based on the proposed method is analyzed to verify the effectiveness in reducing audible noise. Then, a corresponding demodulation method for induced HF current is presented. Finally, the experimental results show that the proposed method has continuous spectra and audible noises can be suppressed significantly.

A novel speed estimation method of induction motors based on active disturbance rejection observer (ADRO) is proposed in this paper, in order to realize the sensor-less control of induction motors. Such an approach permits to cope with the unmodelling dynamics as well as the uncertainties in the model parameters and exogenous disturbance, so that ADRO is robust to the external disturbances and model errors. Meanwhile, the system uncertainties and disturbance can be estimated instantaneously, so that ADRO observer realizes the disturbance estimation accurately without the knowledge of the exact system parameters. Therefore, the disturbance including rotor speed information is estimated in real-time by ADRO, and the rotor speed is estimated from the observed disturbance variation. The experimental results indicate that the novel speed estimation is feasible and effective.

Aiming at the problem of large harmonic distortion of grid-connected current in high-power grid-connected power generation system under weak sunlight, multiple hybrid power parallel inverters (MHPPIs) composed of some identical high-power inverters and one low-power inverter in parallel are proposed. According to the irradiance change of sunlight, the low-power inverter operates alone or cooperates with the switching process between the high-power inverters to ensure the low grid-connected current harmonics. Where, any inverters operate in four common working processes, such as soft start-up, steady-state operation, abrupt change of current, and soft stop. Due to the short running time of the dynamic process, most of the existing researches focus on the circulating current suppression under the steady-state condition. However, in engineering practice, the dynamic circulating current in the switching process between inverters is more likely to cause the over-current protection and other safe and stable operation problems of parallel inverters. Therefore, a dynamic circulating current suppression method with dynamic impedance matching for parallel inverters is proposed in this article. The working process of the MHPPIs is equivalent to that of two inverters. The dynamic working process is analyzed in detail, and the dynamic impedance mismatch is the main cause of the dynamic circulating current, so the dynamic virtual inductance matching is introduced to suppress it. Meanwhile, an online inductance identification method based on the model reference adaptive system is presented to compensate for the influence of inaccurate inductance parameter on the circulating current suppression and current control. An experimental prototype of equivalent MHPPIs is built and experimental results prove the validity of the proposed control method.

A fractional order equivalent circuit model with variable order can better reflect the internal reaction mechanism of a lithium‐ion battery, however, it is not easy to simultaneously identify the parameters and order of the fractional order model online. In this paper, based on the electrochemical impedance spectroscopy of the lithium‐ion battery, the FOM and its discretization are analysed in detail, and a fractional order repeated prediction recursive least square (FORPRLS) method is proposed, the approximate error of fractional derivative is corrected by the repeated identification process with changing the order, so that the parameters and order of the fractional order model can be identified online and accurately at the same time. For the second‐order RC equivalent circuit model and fractional order model, different online identification algorithms including the forgetting factor recursive least square and FORPRLS are carried out under the dynamic stress test experiment, and the predicted terminal voltages are compared with the actual measured terminal voltages to judge the identification accuracy of these algorithms. The experimental results verify that the FORPRLS algorithm is superior to the FFRLS algorithm, and the identified order can better match the reaction process of the lithium‐ion battery.