The series hybrid electric tracked bulldozer 2 (HETB)'s fuel economy heavily depends on its energy 3 management strategy. This paper presents a model predictive 4 controller (MPC) to solve the energy management problem in 5 an HETB for the first time. A real typical working condition 6 of the HETB is utilized to develop the MPC. The results are 7 compared to two other strategies: a rule-based strategy and a 8 dynamic programming (DP) based one. The latter is a global 9 optimization approach used as a benchmark. The effect of the 10 MPC's parameters (e.g. length of prediction horizon) is also 11 studied. The comparison results demonstrate that the 12 proposed approach has approximately a 6% improvement in 13 fuel economy over the rule-based one, and it can achieve over 14 98% of the fuel optimality of DP in typical working 15 conditions. To show the advantage of the proposed MPC and 16 its robustness under large disturbances, 40% white noise has 17 been added to the typical working condition. Simulation 18 results show that an 8% improvement in fuel economy is 19 obtained by the proposed approach compared to the 20 rule-based one.
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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