Bin Ma

Hybrid energy storage system (HESS) is the preferred energy source for hybrid electric vehicles (EVs). Extending system lifespan and improving energy management efficiency are critical factors in enhancing the availability and sustainability of EVs. This study develops a predictive deep reinforcement learning energy management strategy using vehicle historical data and considering the battery life effect during the power optimization process. First, the Autoregressive Integrated Moving Average (ARIMA) model processes the vehicle’s historical data to predict short-term future speed and road gradient changes. Second, a battery life-aware predictive deep Q-Network (LAP-DQN) energy management strategy (EMS) is introduced, and the battery aging effect is incorporated during training to achieve a synergistic optimization of energy consumption and battery lifespan. Finally, the effectiveness of the proposed method is validated via comparative simulations against CD-CS and PMP via three cycles. The results demonstrated that LAP-DQN significantly extended battery life by 8.76% while improving UC utilization ratio by 17.91% in overall performance. This study offers new insight into EMS for EVs and shows promising prospects for engineering sustainability applications and the circular economy.

Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs.

Short vehicle speed prediction is important in predictive energy management strategies, and the accuracy of the prediction is beneficial for energy-saving performance. However, the nonlinear feature of the speed series hinders the improvement of prediction accuracy. In this study, a novel hybrid model that combines an autoregressive integrated moving average (ARIMA) and a long short-term memory (LSTM) model is proposed to handle the nonlinear part efficiently. Generally, the ARIMA component filters out linear trends from the speed series data, and the parameters of the ARIMA are determined with the analysis. Then the LSTM handles the residual normalized nonlinear items, which is the residual of ARIMA. Finally, the two parts of the prediction results are superimposed to obtain the final speed prediction results. To assess the performance of the hybrid model (ARIMA-LSTM), two tested driving cycles and two typical driving scenarios are subjected to rigorous analysis. The results demonstrate that the combined prediction model outperforms individual methods ARIMA and LSTM in dealing with complex, nonlinear variations, and exhibits significantly improved performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MAPE). The proposed hybrid model provides a further improvement for the accuracy prediction of vehicle traveling processes.

The auto-regressive integrated moving average (ARIMA) model has shown promise in predicting vehicle velocity and road gradient (V–G) for the purpose of constructing power demands in predictive energy management strategies (PEMS) for electric vehicles (EVs). It offers flexibility, accuracy, and computational efficiency. However, the performance of a conventional ARIMA model with fixed structure parameters can be disappointing when the data fluctuate. To overcome this limitation, a novel and flexible-structure-based ARIMA (FS–ARIMA) is proposed in this paper to improve online prediction performance. First, the sliding window method was developed to produce fitting data in real time based on real local historical data, reducing the online computation time. Secondly, the influence of the sliding window sample size, differencing order, and lag in the model on the prediction accuracy was investigated. Based on this, an FS–ARIMA was proposed to improve the prediction accuracy, where an augmented Dickey–Fuller (ADF) test was developed to select the differencing order in real time and the Bayesian information criterion (BIC) was applied to update the model and determine its lag under an optimal sample size. Lastly, to validate the proposed FS–ARIMA, simulations were conducted using two typical driving cycles collected via experiments, as well as the following three typical driving cycles: the New European Driving Cycle (NEDC), the Urban Dynamometer Driving Schedule (UDDS), and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results demonstrated that FS–ARIMA improved prediction accuracy by approximately 41.63% and 42.19% for the velocity and gradient, respectively. The proposed FS–ARIMA prediction model has potential applications in predictive energy management strategies for EVs.

Tire wear quantity and difference is one of the main points that affect the intension of tire road print feature and is under a tight link not only with tire road friction energy but also with vehicle dynamics. The proposed methodology aims to provide a mathematical method to analyze tire marks’ feature for prediction the vehicle operating status in pro- and post-crash phases. The kinetic sliding friction coefficient considering the road effect is simplified with the new real contact area model. After, a simplified 9 degrees of freedom vehicle dynamics model by coupling the tire dynamic model is set up, and the effectiveness of the model is evaluated with vehicle stability measurement equipment. Furthermore, a theoretical model of tire wear quantity and difference as a function of road properties and vehicle dynamics is set up combined with vehicle–road coupling algorithm, and important parameters (e.g. vehicle speed, steering angle, braking force, and road feature) are analyzed to assess the tire wear quantity and difference feature when the vehicle is in unsteady condition. Results show that vehicle speed, braking force, and road surface feature have a significant impact to tire wear quantity and difference, the right rear exhibits a fluctuation and the tire wear quantity and difference between rear axle and the right side is obvious, and lay a strong foundation for tire road print analysis.

A novel structure of the combined braking system based on the regenerative braking energy has been proposed to achieve simplified structure and energy-saving capability simultaneously, which includes the hydraulic regenerative braking system, electro-hydraulic braking system, and the power coordinate module. Theoretical contributions and managerial implications of the developed system are discussed. The corresponding mathematic models are developed, a fuzzy control method which can fulfill the power coordinate between the high-pressure and low-pressure accumulators is proposed, and the correctness of the model is verified with the utilization of the test bench. The dynamic characteristics and efficiency are further investigated in various parameters based on MATLAB/Simulink, such as the vehicle initial braking speed, the upper pressure, the liquid capacity, and the state-of-charge (SOC) of the regenerative braking accumulator. The results of the simulations which provide strong evidence for the liquid capacity and initial SOC of the regenerative braking accumulator are the key factors that affect the energy recovery efficiency, and the initial braking speed is the key point that affects the total energy. The results can provide analytical references to practical applications.

Summary: Parametric formulation is a systematic method that ties the constraint functions to the resulting joint profiles for bipedal robots. Acceptable joint profiles for continuous and steady walking must satisfy the additional condition of being repeatable. This extra condition imposes restrictions on the selection of constraint functions, gait parameters and the initial posture of each step, and can make the synthesis of joint profiles extremely challenging. This paper presents a correlation between the gait parameters used in a class of specific constraint functions and the initial posture that will create repeatable gait. Furthermore, a general necessary and sufficient condition for repeatable gait is proposed. This condition provides a guideline for selecting constraint functions and their associated gait parameters in the context of producing repeatable gait. This research can not only provide valuable insights into bipedal walking, but also serve as a stepping stone for employing parametric formulation with various constraint functions.