No abstract

Summary Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium‐ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF‐AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞‐CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium‐ion battery SOC. FF‐AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD‐CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF‐AGLS and H∞‐SVD‐CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

A contra-rotating axial-flow fan is prone to stalling and motor overload at partial flow rates. Moreover, its efficiency decreases sharply at a high flow rate, which is accompanied by disparity between the shaft powers of the two impellers and the light loads of their motors. It is necessary to maintain a high fan operating efficiency and a reasonable match between the powers of the two impellers. In this study, model and simulation analyses of a mine contra-rotating axialflow fan were conducted. Different impeller rotational speeds were obtained by controlling the pole speed. The aerodynamic characteristics, namely, the total pressure rise, shaft power, and operating efficiency, were investigated with respect to the flow rate and rotational speed combination of the impellers. The pressure and velocity distributions were also determined and analyzed. The high-efficiency operating range and variation range of the total pressure rise of the fan with different speed combinations were obtained. The findings indicate that the variable speed operation of two impellers can expand the fans' stable operating range and that impeller speed combinations can be set based on the flow rate and the resistance of the pipe network. This enables the contra-rotating axial-flow fan to operate in the highefficiency operating range.

Abstract. The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, there will be a need to recharge the battery using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, fuzzy logic energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for several number of drive cycles. The proposed PHEV algorithm results are compared with the determinacy rule-based energy management strategy for HEV with similar battery capacity as PHEV.

Aiming at the refitted HAFEI hybrid electric vehicle (HEV), fuzzy logic energy management strategy is constructed based on genetic algorithm optimization. The difference value D between the total require torque Tr of path and the target required torque Te of engine, the intelligence quotient value with Tr is selected as the first input variable of fuzzy controller, the SOC of battery as the second input variable; torque control coefficient C is selected as output variable, meanwhile two input variable membership function is improved on genetic algorithm. To further evaluate the control strategy, dynamic programming control strategy is used as standard; the simulation experiments show that every kind of gas emission is obviously reduced by 12% to 47% in fuzzy control strategy B based on genetic algorithm optimization compared to strategy A based on determinacy rules.

In view of the problems of declined estimation and diagnostic accuracy, as well as diagnosis delay caused by the fixed model transformation probability of the interacting multiple model (IMM) fault diagnosis algorithm, an IMM algorithm based on low inertia noise reduction (LN-IMM) was presented in this paper. The proposed algorithm realized the multi-fault diagnosis of lithium-ion batteries in combination with strong tracking Kalman filter (STKF). In the non-model transformation stage, a transition probability correction function was constructed using the difference of the n-th order based on model probability to suppress the effect of noise on the estimation accuracy of the algorithm. In the model transformation stage, a model jump threshold was introduced in order to reduce inertia when the matched model was switched and realized quick model transformation. Accordingly, the final experiment proved that the LN-IMM-STKF algorithm efficiently completed the state estimation of lithium-ion batteries and improved the diagnostic accuracy of faults while reducing diagnosis delay and attaining accurate detection as well as rapid separation of battery fault information.INDEX TERMS Interactive multiple model, low inertia, noise reduction, strong tracking Kalman filter, lithium-ion battery.

Aiming at the refitted HAFEI hybrid electric vehicle (HEV)

Summary Starting characteristics and dynamic impedances are the key parameters for the research of high‐voltage induction motor. The measurement of starting characteristics and dynamic impedances for high‐voltage induction motors can present problems for both manufacturers and users, so a practical and feasible calculation method is necessary. In order to facilitate the engineering application, this paper presents a set of useful calculation method for motor transient torque, current, and impedances. In the calculation, the influence of dynamic characteristics and impedances on each other, which are often neglected in previous calculation, is considered. So the influence of impedances on the characteristic curves can be analyzed more accurately using this method. Based on the earlier analyses, this paper further studies the influence of different start‐up voltages on the stator leakage reactance. Finally, through the analysis and calculation results of many motors, it can be verified that only when the relation between starting characteristics and dynamic impedances is considered in calculation process will the results be better in line with the actual value. The algorithm is suitable for engineering use. Copyright © 2014 John Wiley & Sons, Ltd.