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 underlying voltage/current tracking control is a key issue for a hybrid energy storage system (HESS) in electric vehicles. This article presents an innovative passivity-based L2-gain adaptive robust control (L2-ARC) method for a fully active battery/super-capacitor HESS. First, by exploiting and analyzing the internal structural properties, the port-controlled Hamiltonian model with dissipation for HESS is derived and then, the interconnection and damping assignment-passive based controller (IDA-PBC) is designed to realize the underlying control, where the rule-based energy management strategy is adopted to generate the current references. To overcome the adverse influence of external disturbances and parameter perturbations under complex driving conditions, by combining the L2 gain disturbance attenuation technique with the IDA-PBC method, the L2-ARC method is developed to guarantee fast response, high performance, and robust stability. Moreover, an adaptive mechanism is adopted to estimate the electrical parameters. The performance of L2-ARC is thoroughly investigated and compared through comprehensive case studies with traditional PID, IDA-PBC, and sliding mode controllers. Finally, a control prototype is implemented to validate L2-ARC.

Continuous and stable acquisition of accurate three-phase currents information is critical to vector control. The immeasurable areas (IAs) is the most intractable problem in the traditional single current sensor phase current reconstruction (SCS-PCR) technology. In addition, the change of the sampling position owing to the influence of the IAs makes the current ripple tend to be serious, which further increases the current distortion and decreases the current reconstruction accuracy. This article is the first to investigate the influence of the IAs and the current ripple of SCS-PCR. A midpoint value (MPV) random space vector pulsewidth modulation (RSVPWM) reconstruction strategy is proposed, which is based on the Thevenin equivalent circuit of the sampling circuit of SCS-PCR. RSVPWM constructs a current sampling window that satisfies the minimum sampling time to complete two sequential samplings, while the MPV strategy obtains the mean value of two sampling values in the in-phase dynamic current to reduce current ripple. The proposed MPV RSVPWM reconstruction strategy does not require the insertion of redundant voltage vectors and major computation and is not limited to a particular PWM technology. The experimental results verify the accuracy and practicability of the proposed method.

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.