State-of-health (SOH) estimation is crucial for ensuring efficient, reliable and safe operation of power battery in electric vehicle (EV) application. However, due to the complicated physicochemical reactions happened in battery cells, it is extremely difficult to accurately estimate SOH, especially in real-world EV application scenarios. Traditional SOH estimation methods, including both model-based and data-driven ones, are deterministic, which cannot capture the stochastic property of battery aging process aroused from the inherent inconsistency during battery production. In this paper, Bayesian network (BN), which is a probabilistic graphical modeling method for indeterministic process, is used to battery degradation modeling. Its structure is derived from existing knowledge about battery aging mechanism. Two-year operational data and capacity calibration results of 16 electric taxies are collected for model training and validation. Specifically, a systematic data filling procedure is proposed to predict the missing values of variables necessary for SOH estimation. Markov Chain Monte Carlo method is adopted to generate the samples from parameterized BN for SOH estimation. Results show that the estimation result is very close to the calibrated SOH with mean absolute error below 4%. The proposed method is promising to be applied online for SOH estimation in real-world EV application.INDEX TERMS Electric vehicle, battery aging, state-of-health estimation, real-world data.

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Purpose The purpose of this paper is to find optimal reef parameters to minimize the maximum instantaneous opening load for a reefed parachute with geometry and environmental parameters given in the model. Design/methodology/approach The dynamic model Drop Test Vehicle Simulation (DTVSim) is used to model the inflation and descent of the reefed parachute system. It is solved by the fourth-order Runge–Kutta method, and the opening load values are thereby obtained. A parallel genetic algorithm (GA) code is developed to optimize the reefed parachute. A penalty scheme is used to have the maximum dynamic pressure restricted within a certain range. Findings The simulation results from DTVSim fit well with experimental data from drop tests, showing that the simulator has high accuracy. The one-stage and two-stage reefed parachute systems are optimized by GA and their maximum opening loads are decreased by 43 and 25 per cent, respectively. With the optimal reef parameters, two of the peaks in the opening load curve are almost equal. The velocity, loiter time and flight path angle of the parachute system all change, but these changes have no negative effect on the parachute’s operational performance. Originality/value An optimization method for reefed parachute design is proposed for the first time. This methodology can be used in the preliminary design phase for a reefed parachute system and significantly improve design efficiency.

A secondary seeding precision double-seed peanut hole seeding seed metering device was designed to improve the performance of the peanut planting equipment and provide a solution for problems on the high seed charge that can cause poor cavitation and uniformity easily. The main structure and operation parameters in terms of groove length, seed charge height, seed-bed belt speed, and rotation speed of the seed metering wheel were determined through theoretical analysis. Single-factor and orthogonal tests were carried out through the JPS-12 seed metering device test bench, and the peanut variety Jinonghua-3 was selected as the test object. The single hole double-seed rate, qualified rate, the variation coefficient of hole spacing, and hole rate were chosen for evaluating the working performance. The results of the single-factor test showed that the seed metering performance is mainly affected by the groove length, the speed of the seed-bed belt and the rotation speed of the seed metering wheel, and the influence of the cavitation rate is minimal. The optimal seeding height is determined to be 40 mm. The results of the orthogonal test showed that the groove length was 27.3 mm, the seed-bed belt speed was 1.51 km/h, and the rotation speed of the seed metering wheel was 14.11 r/min. What's more, a regression model based on the orthogonal test results was established, the qualified rate of the number of holes obtained after optimizing the model was 98.84%, the variation coefficient of hole spacing was 9.74%, and the hole rate was 1.40%. Notably, the working performances of the device can meet the requirement of precision seeding.

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