Leg properties have been involved in the broad study of human walking from mechanical energy to motion prediction of robotics. However, the variable leg elasticities and their functions during gait have not been fully explored. This study presented that the fundamental leg properties during human walking comprise axial stiffness, rest leg length, tangential stiffness and force-free leg angles. We measured the axial force-leg length and tangential force-leg angle data in eight participants (mean ± s.d. age 24.6 ± 3.0 years, mass 68.2 ± 6.8 kg, height 177.5 ± 5.2 cm) at three self-selected walking speeds (slow: 1.25 ± 0.22, normal: 1.48 ± 0.28, fast: 1.75 ± 0.32 m/s) on two different contact conditions (fixed and moving). After obtaining these gait measurements, we extracted the linear and nonlinear leg elasticities during human walking by using a minimum root-mean-square fitting. We found that the axial stiffness of nonlinear elasticity (fixed condition: 7.1−8.0, moving condition: 21.3−22.6) is higher than that of the linear elasticity (fixed condition: 5.0−5.7, moving condition: 15.2−16.5). The tangential stiffness behaves different during four stance phases of gait, with the highest (linear: 2.52−3.72, nonlinear: 1.71−2.01, in moving condition) occurred at early stance and second highest at late stance, followed by two stiffnesses in mid-stance. For both linearity and nonlinearity, the axial stiffness and rest length are independent of walking speeds in both contact conditions, while the tangential stiffness and contact angles are independent of walking speeds only in moving condition. Regardless of walking speed, elasticity and contact condition, the force-free contact angle at mid-stance is maintained at average of 82.2°. This paper first demonstrates the mechanical walking leg property from both axial and tangential aspects. The findings provide insight into the fundamental properties including linearity and nonlinearity of human leg during locomotion for stability analysis and precise motion prediction of robotics and rehabilitation exoskeletons.

Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.

Traditional robotic foot and ankle have received considerable attention for adaptive locomotion on complex terrain and land buffering ability. This study aims to develop a robotic ankle based on series elastic actuator with a parallel spring (SEAPS) by mimicking the two degree-of-freedom human ankle and related muscles. A unique four-SEAPS-based spring mechanism enhances motion adaptation and landing buffer. A dynamic model based on the SEAPS drive is presented, whereas a kinematic solution is realized. The bionic ankle has a wide range of motions with 30° in both the sagittal and frontal planes, which cover most of the human ankle motions. Four experiments were conducted to thoroughly characterize the capabilities. First, the static stiffness and the 2-D/3-D trajectory tracking performance of the proposed ankle were tested. Second, the angle sensing capacity under inclined road surface and the land buffering performance from a specific height were evaluated. The results show that the maximum 3-D motion tracking error is smaller than 2.3%, and the minimum sensing error of inclined road is smaller than 0.5%. The proposed ankle can closely track the 3-D walking trajectory of natural ankle joint with relative root-mean-square error well below 1.0%. Compared with no springs, landing buffer of the ankle with SEAPS can yield remarkable reduction in both peak ground reaction force (52.2%) and joint torque (57.9%). These findings prove that the SEAPS-based bioinspired robotic ankle exhibits high land buffering performance in an unstructured environment, and also well reproduces the human-like movement in terms of complex 3-D ankle motions.

In the era of big data, using big data to realize the online estimation of battery SOH has become possible. Traditional solutions based on theoretical models cannot take into account driving behavior and complicated environmental factors. In this paper, an approximate SOH degradation model based on real operating data and environmental temperature data of electric vehicles (EVs) collected with a big data platform is proposed. Firstly, the health indicators are extracted from the historical operating data, and the equivalent capacity at 25 °C is obtained based on the capacity–temperature empirical formula and the capacity offset. Then, the attenuation rate during each charging and discharging process is calculated by combining the operating data and the environmental temperature. Finally, the long short-term memory (LSTM) neural network is used to learn the degradation trend of the battery and predict the future decline trend. The test results show that the proposed method has better performance.

Most electric vehicles adopt cooperative braking systems that can blend friction braking torque with regenerative braking torque to achieve higher energy efficiency while maintaining a certain braking performance and driving safety. This paper presented a new cooperative regenerative braking system that contained a fully-decoupled hydraulic braking mechanism based on a modified electric stability control system. The pressure control algorithm and brake force distribution strategy were also discussed. Dynamic models of a front wheel drive electric car equipped with this system and a simulation platform with a driver model and driving cycles were established. Tests to evaluate the braking performance and energy regeneration were simulated and analyzed on this platform and the simulation results showed the feasibility and effectiveness of this system.