The performance of battery management systems relies on the core temperature estimation, which is one of the major technical bottlenecks for electric vehicles. Aiming to tackle this problem, a lumped-parameter two-state thermal model for lithium-ion batteries is established in this paper. Then, this thermal model is coupled with a temperature-dependent second-order RC equivalent circuit model to form an electro-thermal model for lithium-ion batteries. Using the proposed electro-thermal model, an adaptive estimation algorithm based on joint Kalman filtering is proposed for battery core temperature estimation, considering the heat transfer condition variations between the battery surface and ambient media. The verification results show that the proposed algorithm enhances the temperature estimation accuracy, compared to the results obtained directly from the electro-thermal model. Besides, the verification results demonstrate the high adaptability of the proposed algorithm, that is, it is robust to variations in ambient temperature, as well as variations in thermal resistance between the battery surface and ambient media. Considering the influences of temperature and SOC on the thermal generation rate of the battery, an electro-thermal model for lithium-ion batteries is established. This model is composed of a lumped-parameter two-state thermal model and a temperature-dependent second-order RC equivalent circuit model. An adaptive core temperature estimation algorithm based on joint Kalman filtering is proposed, considering the heat transfer condition variations between the battery surface and the ambient media.
Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness.
The existing energy management strategies for four-wheel-drive electric vehicles only take into account the vehicle energy consumption under static adhesion constraints. However, the front and rear axle loads transfer under dynamic conditions lead to the variations of vehicle adhesion characteristics, which results in the changes of vehicle energy consumptions. In this paper, a multi-objective optimal torque distribution strategy is proposed, taking into account the front and rear axle load transfer and the variations of adhesion characteristics. The advantages of the proposed strategy are verified through simulation studies in terms of vehicle energy consumption and wheel slip ratio, in comparison with the average torque distribution strategy and the optimal torque distribution strategy based on Sequential Quadratic Programming Algorithm. The simulation results show that the economy performance of the proposed strategy is superior to those of the competing methods. Furthermore, the proposed strategy provides good power performance and eliminates excessive wheel slip, which in turn ensures vehicle longitudinal stability and avoids energy loss resulting from frequent ASR interventions. INDEX TERMS Axle load transfer, optimal efficiency, slip ratio, all-wheel-drive torque distribution, vehicle longitudinal stability.
Dyskinesia of the upper limbs caused by stroke, sports injury, or traffic accidents limits the ability to perform the activities of daily living. Besides the necessary medical treatment, correct and scientific rehabilitation training for the injured joint is an important auxiliary means during the treatment of the effected upper limb. Conventional upper-limb rehabilitation robots have some disadvantages, such as a complex structure, poor compliance, high cost, and poor portability. In this study, a novel soft wearable upper limb rehabilitation robot (SWULRR) with reinforced soft pneumatic actuators (RSPAs) that can withstand high pressure and featuring excellent loading characteristics was developed. Driven by RSPAs, this portable SWULRR can perform rehabilitation training of the wrist and elbow joints. In this study, the kinematics of an SWULRR were analyzed, and the force and motion characteristics of RSPA were studied experimentally. The results provide a reference for the development and application of wearable upper limb rehabilitation robots. An experimental study on the rotation angle of the wrist and the pressure of the RSPA was conducted to test the effect of the rehabilitation training and verify the rationality of the theoretical model. The process of wrist rehabilitation training was tested and evaluated, indicating that SWULRR with RSPAs will enhance the flexibility, comfort, and safety of rehabilitation training. This work is expected to promote the development of wearable upper-limb rehabilitation robots based on modular reinforced soft pneumatic actuators.
Considering that use of measured current as input of a battery model may cause distortion of the model due to low accuracy of the on-board current sensor and that power can be used to indicate energy transmission in an electric vehicle model, the power input internal resistance model is widely used in simulation of whole electric vehicles. However, since no consideration is given to battery polarization and electro-thermal coupling characteristics, the foregoing model cannot be used to describe the internal temperature change of batteries under working conditions. Three contributions are made in the present study: (1) ternary lithium-ion batteries were taken as the research objects and a second-order RC equivalent circuit model with power as the input was established in the present study; (2) A dynamic heat generation rate model suitable for RC equivalent circuits was built based on coupled electrical and thermal characteristics of lithium-ion batteries; (3) An electric model and a two-state equivalent thermal network model were further built and combined by using the heat generation rate model to form a power input electro-thermal model. Parameters of the model so formed were identified offline, and the battery model was verified with respect to accuracy under seven working conditions. The results show that the maximum root mean square error in voltage estimation, current estimation, and surface temperature estimation is 19.38 mV, 9.51 mA, and 0.19 °C respectively, which indicates that the power input electro-thermal model can describe the electrical and thermal dynamic behavior of batteries more accurately and comprehensively than the traditional power input internal resistance model.
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