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.

In this paper, an online energy management strategy (EMS) for hybrid electric tracked vehicle (HETV) is developed based on deep deterministic policy gradient (DDPG) with time-varying weighting factor to further improve economic performance of HETV and reduce computational burden. The DDPG is applied to model the EMS problem for the target HETV. Especially, a time-varying weighting factor is introduced here to update old network parameters with experience learned from most recent cycle segment. Afterwards, simulation is conducted to verify the effectiveness and adaptability of the proposed method. Results show that DDPG-based EMS with online updating mechanism can achieve nearly 90% fuel economy performance as that of dynamic programming while computational time is greatly reduced. Finally, hardware-in-loop experiment is carried out to evaluate the real-world performance of the proposed method.

It is a common traffic phenomenon that side vehicles change lanes without any forewarning, which is easy to induce vehicle side collision and rear-end collision. In order to improve the active safety of cruise control in such a random traffic environment, this article proposes a connected cruise control (CCC) framework considering merging behavior probability of side vehicles. The control strategy of the framework first uses vehicle-to-vehicle communication and radar recognition algorithm to detect the surrounding vehicle targets. Then, the merging potential of the side vehicle is determined through the combination of fuzzy support vector machine and sliding window detection. Afterward, a cruise distance strategy considering the cutting-in probability of merging behavior is proposed and applied to the CCC system to realize the early braking of the main vehicle. Finally, the longitudinal control of the host vehicle is realized by the acceleration control, considering both the time delay and feedback error. The effectiveness of the proposed systematic control framework is verified through not only simulation but also field test. Results show that our method can accurately detect the lane change intent of the side vehicle, thus realizing braking of the host vehicle in advance when the side vehicle changes lane.

Considering the slow response and unstable velocity of agricultural machinery caused by soil resistance, actuator delay, environmental change, velocity fluctuation, and other internal and external factors under real working conditions, a kind of agricultural machinery following a control system that considers variable load and control delay was proposed. Taking distance-keeping, velocity-following, and acceleration-following as parameters, the controller model was deduced, and the influence of different values of model parameters on the driving stability of agricultural machinery was analyzed in detail. In addition, this paper describes a kind of agricultural machinery following a strategy that can realize the graded adjustment of vehicle distance with the dynamic increase in vehicle weight. Then, the following strategy, under the influence of velocity and quality, was simulated and verified using MATLAB/Simulink (MATLAB2016a, mathworks: Natick, Massachusetts, USA). When the crop harvester was at 1.5 m/s and the amplitude of velocity fluctuation was 0.3 m and 1.3 m, respectively, the grain truck could adjust its velocity to keep up with the crop harvester to complete the operation task. Simulation verification was carried out for the proposed graded adjustment of vehicle distance of agricultural machinery following strategy. The unit mass of the crops was set at 360 kg, and the vehicle distance changed at 18s to adapt to the graded adjustment of the vehicle distance following strategy. Finally, a real-vehicle validation test was carried out, and the results show that the grain truck velocity can keep up with the change of crop harvester velocity on the basis of maintaining the desired vehicle distance, the grain truck velocity can keep up with the change of crop harvester velocity on the road condition, which verifies the effectiveness and feasibility of the proposed method.