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The improved hydraulic energy storage system (IHESS) is a novel compact hydraulic ESS with only 10% of oil and 64.78% of installation space of the regular ones. However, its novel circulating structure and lightweight material result in poor heat dissipation. The thermodynamic and heat transfer model of IHESS with an oil-circulating layout is proposed. Based on the mining trucks’ dynamic model, thermal characteristics of IHESSs with different parameters under the actual and simplified working conditions are studied and the factors causing overheating are analyzed. Finally, a feasible thermal design is put forward, and its efficiency is analyzed. The simulation shows that more accumulators and higher recovery power lead to higher system temperature and vice versa. Under the standard simplified working condition at 40°C ambient temperature, the highest oil temperature reached is 93.13°C. About 90% of the generated heat is converted into the internal energy of nitrogen and oil. On this basis, by adopting an energy-saving passive cooling system with a cooling power of 6.68 kW, the highest temperature of the oil drops to 52.79°C and 28% of the generated heat is released through the cooling system.

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The alternating direction method of multipliers (ADMM) is a widely used model predictive control (MPC) acceleration method. It adopts the time‐splitting technique, splitting the original problem into independent subproblems. Relaxed ADMM (R‐ADMM) is a generalization of ADMM that often achieves faster convergence. However, its parameters must be chosen by an expert user. Besides, the existing convergence proof of R‐ADMM adopts a first‐order Taylor approximation, which makes the range of relaxation factors conservative. We tackle these weaknesses by giving rigorous evidence and finding the optimal relaxation factor. Firstly, we deduce the convergence of the R‐ADMM algorithm, yielding an accurate range of relaxation factors. Then, we analyze the relationship between convergence rate and relaxation factor and conclude that the optimal relaxation factor depends on a recurrence condition. Since splitting introduces the equality constraint, the decoupled states are getting close in the iteration, meeting the recursive requirements and helping find the optimal relaxation factor. Finally, various trajectory tracking tasks are conducted to verify the efficiency of the R‐ADMM algorithm. And the simulation results show that the R‐ADMM algorithm reduces the number of iterations by 63.7% compared with the ADMM algorithm in the double lane change task.

This paper presents a novel model-based method for estimating the attitude of underground articulated vehicles (UAV). We selected the Load–Haul–Dump (LHD) vehicle as our application object, as it is a typical UAV. First, we established the involved models of the LHD vehicle, including a kinematic model, the linear and angular constraints of a center articulation model, and a dynamic four degrees-of-freedom (DOF) yaw model. Second, we designed a Kalman filter (KF) to integrate the kinematic and constraint models with the data from an inertial measurement unit (IMU), overcoming gyroscope drift and disturbances in external acceleration. In addition, we designed another KF to estimate the yaw based on the dynamic yaw model. The accuracy of the estimations was further enhanced by data fusion. Then, the proposed method was validated by a simulation and a field test under different dynamic conditions. The errors in the estimation of roll, pitch, and yaw were 3.8%, 2.4%, and 4.2%, respectively, in the field test. The estimated longitudinal acceleration was used to obtain the velocity of the LHD vehicle; the error was found to be 1.2%. A comparison of these results to those of other methods showed that the proposed method has high precision. The proposed model-based method will greatly benefit the location, navigation, and control of UAVs without any artificial infrastructure in a global positioning system (GPS)-free environment.