This paper uses the coevolutionary particle swarm optimization (CPSO) method to identify battery parameters. A parameter identification window (PIW), which has the features of a fixed data length and real-time response, is used to store a piece of data that indicates the battery operation at the current moment. CPSO uses the data in the PIW to dynamically identify the battery parameters. Each equivalent circuit model (ECM) parameter uses a separate parameter particle swarm (PPS) to optimize their values. In every algorithm cycle, each particle in every PPS only evolves one step. The currently evolved PPS uses the current optimal values of the other PPS in CPSO to evaluate all of the particles and to find the best particle. Every PPS is scheduled by the CPSO, dynamically evolves one by one, and converges in real time to its optimal value, which is an ECM parameter. Real battery data are used to test the algorithm. The experimental results indicate that the fluctuation patterns of the open circuit voltage (OCV) are accurately identified. For the different algorithm parameters, the identification results for the OCV have good consistency, and the deviations between the identification results are less than 5 mV most of the time.

State of charge (SOC) estimation is of great significance for the safe operation of lithium-ion battery (LIB) packs. Improving the accuracy of SOC estimation results and reducing the algorithm complexity are important for the state estimation. In this paper, a zeroaxial straight line, whose slope changes along with SOC, is used to map the predictive SOC to the predictive open circuit voltage (OCV), and thus only one parameter is used to linearize the SOC-OCV curve near the present working point. An equivalent circuit model is used to simulate the dynamic behavior of a LIB, updating the linearization parameter in the measurement equation according to the present value of the state variables, and then a standard Kalman filter is used to estimate the SOC based on the local linearization. This estimation method makes the output equation of the nonlinear battery model contain only one parameter related to its dynamic variables. This is beneficial to simplify the algorithm structure and to reduce the computation cost. The linearization method do not essentially lose the main information of the dynamic model, and its effectiveness is verified experimentally. Fully and a partially charged battery experiments indicate that the estimation error of SOC is better than 0.5%.

In a variety of motor models, the effects of iron-loss (ILS) on motor control accuracy and efficiency are generally ignored. This makes it difficult for the motor control system to obtain accurate control parameters (especially on high speed and low load conditions), and limits the improvement of motor control accuracy. This paper aims to clarify the influence of different ILS modeling and observation methods on motor control performance. Three equivalent models of motors with iron losses are compared. These models are: A parallel model, a series model and the simplified traditional model. Three tests are conducted to obtain the effect of ILS perturbation on ILS estimation results, and then to derive the sensitivity of the motor state and torque to the perturbation. These test conditions include: Ideal no-load, heavy-load, locked-rotor, and ILS perturbations during speed regulation. Simulation results show that the impedance and excitation characteristics of the series model and the parallel model are similar, and the traditional model has the best speed regulation smoothness. The ILS estimation errors of the series model is nearly constant and easy to compensate. For accurate ILS observation results, the series model can achieve better control accuracy.

The RC network in the equivalent circuit model (ECM) can accurately describe the transient of lithium batteries. However, it has many combinations of optimal parameter values, which makes it difficult for the identification results of each parameter to converge to a clear quantitative value and to independently reflect engineering value. This article proposes a method of quantifying the impedance values of multiple RC networks in ECM into a single value impedance, which is suitable for offline rapid identification of battery parameters with strict measurement time limitations. It uses a window-based algorithm to simultaneously observe the parameters and states of the 2RC-ECM model over a continuous period of time. In the subsequent result quantification stage, it extracts the equivalent impedance of the entire transient network based on the excitation current and voltage observations of the 2RC network. In this way, it bypasses the separate quantification of the four circuit parameters of the 2RC network, thereby achieving stable identification of open circuit voltage, ohmic resistance, and polarization resistance simultaneously. Comparative tests using multiple methods show that it can accurately distinguish between ohmic resistance and polarization resistance, and the parameter identification results are stable, reliable, and have good adaptability to working conditions.