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.

To improve the performance of the energy storage system of electric vehicles, a complete ensemble empirical mode decomposition-fuzzy logic control energy management strategy is proposed to attenuate the aging of lithium-ion batteries caused by high-frequency power demand. Firstly, the electric vehicle power demand is decomposed into a finite number of intrinsic mode functions components, and each component is reconstructed into low-frequency or high-frequency components according to its permutation entropy. Then, the low-frequency and high-frequency components of electric vehicle power demand are allocated to lithium-ion batteries and ultracapacitors, respectively. Finally, fuzzy logic based closed loop controller is designed to maintain the state of charge of ultracapacitors at the desired level. Experiments under HWFET, UDDS, US06 and combined drive cycles are performed, and experimental results show that compared with single energy storage system and other state-of-the-art methods, the proposed strategy can effectively reduce the maximum discharge current of the lithium-ion batteries and maintenance the state of charge balance of the ultracapacitor.

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.

The battery energy stored quasi-Z source inverter (BES-qZSI) based photovoltaic (PV) power system combines the advantages of the qZSI and energy storage system. However, as the BES-qZSI is a fast-response power converter without any inertia, when applied as a grid-connected system, it leads to decreased power system inertia. The low inertia problem will degrade the system’s performance and affect the system’s stability. Moreover, The BES-qZSI PV power system usually employs a phase-locked loop and PR regulator to control the grid current to track the desired reference in phase with the grid voltage. These do not reflect the characteristics of frequency modulation and voltage regulation. To enhance the system’s frequency and voltage self-adjustment ability, a control strategy based on virtual synchronous generator (VSG) technique for the BES-qZSI PV power system is proposed in this paper. By introducing the concept of synchronous generator, the BES-qZSI PV power system has the characteristics of heavy moment of inertia, droop frequency-active, droop characteristic of Voltage-reactive power and so on. Then an active power control strategy integrated with reactive power compensation is proposed, to implement PV generation and reactive power compensation at the same time. The simulation and experimental results verify the proposed control strategy.