The underlying control in the hybrid energy source system (HESS) of an electric vehicle plays a pivotal role. Uncertainty is unavoidable in system modeling owing to variations in electrical parameters and unknown external disturbances, which inevitably deteriorate the control performance of the HESS. In this study, an innovative adaptive dynamic surface control with disturbance observers (ADSCDOB) is adopted as a control scheme for underlying tracking in the HESS to counteract the adverse effects and improve control accuracy. First, disturbance observers are designed by employing a nonlinear disturbance observer and an extreme learning machine approximator to estimate the mismatched and matched uncertainties. Subsequently, the proposed ADSCDOB scheme integrates the adaptive dynamic surface technique and second-order differentiators to achieve robust control, in which voltage/current references are obtained through the rule-based energy management strategy. The established ADSCDOB obviates the “differential explosion” problem and ensures that the closed-loop system is semi-globally and uniformly bounded. Comprehensive simulations and prototype experiments prove the effectiveness of the ADSCDOB, confirming its satisfactory performance in terms of a fast response, reduced error, and robust stability under hybrid driving conditions.

The rapid development of intelligent connected technologies and cellular vehicle-to-everything communication (C-V2X) provide new opportunities to solve the connected automated vehicle (CAV) traffic problem for eco-driving at continuous signalized intersections. With C-V2X, a hierarchical velocity optimization design based on hybrid model predictive control technique (HVO-HMPC) is presented to reduce the fuel consumption and pollution emission. First, a distance–domain velocity optimization problem, with distance as the independent variable, was constructed. Second, a hybrid MPC scheme was developed by combining the multiple shooting method and MPC technique to calculate the optimal velocity profile of a high-level controller, which acts as the reference velocity in a low-level controller. Then, a car-following model was built, the low-level controller tracked the reference velocity with the predictive control as the backbone, and the optimal velocity was calculated while ensuring that the safety velocity constraint is satisfied. Next, the proposed HVO-HMPC was tested in Prescan, and the effect comparisons with different control methods in terms of fuel consumption, pollution emission, braking time, and number of braking applications were studied under different driving scenarios. Results show that once the maximal speed is limited to 40 km/h under short-period signals and 20 km/h under long-period signals, the HVO-HMPC effectively reduces fuel consumption by 27.21%, 25.89%, and pollution emissions by 25.3%, 25.97%, respectively, while achieving best performance. Finally, an experimental prototype is built to confirm the validity of the HVO-HMPC.

The underlying circuit control is a key problem of the hybrid energy-storage system (HESS) in electric vehicles (EV). In this paper, a composite non-linear control strategy (CNC) is proposed for the accurate tracking current/voltage of the fully-active HESS by combining the exact feedback linearization method and the sliding mode variable structure control technology. Firstly, by analyzing the circuit characteristics of HESS, the affine non-linear model of fully-active HESS is derived. Then, a rule-based energy management strategy (EMS) is designed to generate the reference current value. Finally, the HESS is linearized by the exact feedback linearization method, and the proposed CNC strategy is developed combined with sliding mode variable structure control technology to ensure fast response, high performance, and robustness. At the same time, the stability proof based on the Lyapunov method is given. Moreover, the performance of the CNC strategy is thoroughly investigated and compared with simulation studies with the traditional PI control and a modified sliding mode control, and its effectiveness under different driving conditions is fully verified.

The tremendous revolutionary progress of cellular vehicle-to-everything (C-V2X) and vehicular edge computing (VEC) technologies provide new opportunities to overcome the autonomous transportation issue of the mining electric locomotives (MELs), in which the accurate and fast detection of obstacles is crucial for the safe operation. With the VEC and C-V2X, we proposed a new high-precision obstacle detection strategy for MELs (MEL-YOLO). Firstly, we investigated the convolutional attention mechanism integrated into the path aggregation network of the Neck layer to strengthen the feature extraction capabilities. Secondly, we added a small-object oriented prediction layer in the Head to form the multi-scale feature prediction. Thirdly, we introduced a more efficient loss function to alleviate the gradient explosion problem in the feature transfer. Finally, we utilized the K-means $++$ optimization to derive the anchor boxes matchable with the dataset, which was collected and created by featuring different scenes to train validate the model. The MEL-YOLO was compressed by BN layer pruning and implemented on the edge device in a 6G/B5G based-V2X environment. Experimental results verify that the MEL-YOLO can effectively detect obstacles and significantly improve detection accuracy for small obstacles, computationally increasing mAP by 3.3% to original model, while maintaining detection speed and model size nearly unchanged.

Energy management strategy (EMS) is a key issue for hybrid energy storage system (HESS) in electric vehicles. By innovatively introducing the current speed information, the vehicle speed optimized fuzzy energy management strategy (VSO-FEMS) for HESS is proposed in this paper. Firstly, the pruned fuzzy rules are formulated by the SOC change of battery and super-capacitor to preallocate the required power of vehicle. Then, the real-time vehicle speed is used to optimize the pre-allocated results based on the principle of vehicle dynamics, so as to realize the optimal allocation of required power. To validate the proposed VSO-FEMS strategy for HESS, simulations were done and compared with other EMSs under the typical urban cycle in China (CYC-CHINA). Results show that the final SOC of battery and super-capacitor are optimized in varying degrees, and the total energy consumption under the VSO-FEMS strategy is 2.43% less than rule-based strategy and 1.28% less than fuzzy control strategy, which verifies the effectiveness of the VSO-FEMS strategy.

This paper combines two types of energy storage components, the battery and supercapacitor (SC), to form a fully active hybrid energy storage system (HESS) as a power source for electric vehicles (EVs). At the same time, a hierarchical coordinated energy management strategy based on model predictive control (HCEMS-MPC) is presented. Firstly, the mathematical model of the fully active HESS is obtained based on Kirchhoff’s law and state-space modeling technology. Secondly, considering the state of charge (SOC) of the battery, a fuzzy-control-based upper-level energy management strategy (EMS) is proposed to optimize power allocation and to generate a reference current for a lower-level current controller. Then, a lower-level current predictive controller is designed to achieve accurate current tracking. Finally, a lower-level voltage sliding mode controller is designed to stabilize the bus voltage. Compared with previous works, the HCEMS-MPC strategy only needs to adjust the weight matrix and the reaching term to avoid the problem of excessive controller parameters. The simulation results, under different driving conditions, show that the HCEMS-MPC strategy has a better performance with respect to its fast response, error reduction, and robust stability. In addition, the SOC of the battery decreases more slowly, and the final SOC value significantly increases, thereby extending the single-discharge cycle time of the battery and improving the service life of the battery.