Recursive least square (RLS) algorithms are considered as a kind of accurate parameter identification method for lithium-ion batteries. However, traditional RLS algorithms usually employ a fixed forgetting factor, which does not have adequate robustness when the algorithm has interfered. In order to solve this problem, a novel variable forgetting factor method is put forward in this paper. Comparing with traditional variable forgetting factor methods, it has higher stability and sensitivity by using some mathematic improvements. The improvements in the robustness of recursive least square with a variable forgetting factor (VFF-RLS) algorithm is verified in this paper. A Thevenin model which is frequently-used in battery management system is employed in the verification. A data loss battery working condition is designed to simulate the interference to the algorithm. A simulation platform is established in MATLAB/Simulink software, and the data used in the verification is obtained by battery experiments. The analysis indicated that the novel VFF-RLS algorithm has better robustness and convergence ability, and has an acceptable identification accuracy.

Although the demand of battery electric vehicle (BEV) growths fast as the requirement of reducing greenhouse gas emission and the usage of fossil fuels, the limited driving range and unfriendly retail price present barriers to BEV to provide comparable performance as a traditional vehicle. This paper proposes a dual-motor two-speed direct drive BEV powertrain to boost average motor operational efficiency in daily driving without increasing any complexity of manufacturing or control, ultimately, saving limited battery energy and manufacturing cost. The specifications of the proposed powertrain are first identified through mathematical and graphical calculations, which split traditional one propelling motor to two with separate permanent engaged gears to maximize the motor efficiency. Based on dynamic powertrain modeling in a Simulink/Simscape, economic shifting strategy, and dynamic torque transfer control are designed and tested. According to the simulation results, it is noticed that significant energy efficiency improvement can be achieved. Thanks to the optimized torque transfer control strategy, extremely low vehicle jerk are recorded during the shifting process. At last, conclusions can be made that the proposed dual-motor powertrain superior to the traditional single motor counterpart in terms of fuel economy, driving range, and cost.INDEX TERMS Dual motor, two speeds, electric vehicle, dynamic modeling, energy economy.

Rolling element bearings are widely employed in almost every rotating machine. The health status of bearings plays an important role in the reliability of rotating machines. This paper deals with the principle and application of an effective multi-sensor data fusion fault diagnosis approach for rolling element bearings. In particular, two single-axis accelerometers are employed to improve classification accuracy. By applying the improved detrended fluctuation analysis (IDFA), the corresponding fluctuations detrended by the local fit of vibration signals are evaluated. Then the polynomial fitting coefficients of the fluctuation function are selected as the fault features. A multi-sensor data fusion classification method based on linear discriminant analysis (LDA) is presented in the feature classification process. The faults that occurred in the inner race, cage, and outer race are considered in the paper. The experimental results show that the classification accuracy of the proposed diagnosis method can reach 100%.

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A novel direct torque control (DTC) method based on sliding-mode-control (SMC) strategy is proposed for permanent magnet synchronous motor (PMSM) which is used in electric vehicles (EVs). In order to improve the dynamic response time and enhance the robustness performance against the external loading disturbances and motor parameter's variation, a kind of SMC-based torque controller and speed controller are designed to regulate the torque angle increment and the speed respectively. The torque controller is designed based on a sliding mode controller with an asymmetric boundary layer to reduce the overshoot. Compared with other DTC methods based on space vector modulation (SVM) in the literature, the proposed DTC scheme adopts the asymmetric boundary layer SMC instead of the proportional-integral (PI) regulator. The simulation results have validated the effectiveness of the proposed SMC-based DTC method.

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A new hybrid electric tracked bulldozer composed of an engine generator, two driving motors, and an ultracapacitor is put forward, which can provide high efficiencies and less fuel consumption comparing with traditional ones. This paper first presents the terramechanics of this hybrid electric tracked bulldozer. The driving dynamics for this tracked bulldozer is then analyzed. After that, based on analyzing the working characteristics of the engine, generator, and driving motors, the power train system model and control strategy optimization is established by using MATLAB/Simulink and OPTIMUS software. Simulation is performed under a representative working condition, and the results demonstrate that fuel economy of the HETV can be significantly improved.

Based on the real-time model and platform, a dynamic hardware-in-the-loop (HIL) testing method for the distributed powertrain of electric vehicle (EV) is proposed. Compared with static point testing, the dynamic HIL test can provide a more realistic working environment for the EV's distributed electric powertrain (DEP) early development. The test data of electric motor's efficiency and maneuver performance under a dynamic work condition are more authentic and meaningful. Meanwhile, the driver-vehicle-road real-time (DVRRT) model is set up to emulate the actual condition. The speed-tracking control method with proportional-integral (PI) gains tuned by the neuron network algorithm is used to generate the distributed real-time loads. Maximum adhesion limitation is added once the slipping is detected in the real-time model. Simulation and experiment of the test bench are done. The generated distributed load is compared with both the theoretical one and the simulated one in the Carsim software platform. Two comparisons show the similar results. The load accuracy is high, but there is a short time delay. The mechanical work measured by the experiment test bench is highly consistent (97.5%) with the theoretical value. As a result, the proposed test bench and its control method can be used for DEP efficiency test. Index Terms-Distributed electric powertrain (DEP), drivervehicle-road real-time (DVRRT) model, dynamic loading, dynamometer, hardware-in-the-loop (HIL) test bench.