This article proposes a multienergy trading market model based on price matching, aiming to foster multienergy collaboration and enhance energy utilization through individual participation. With the ongoing advancements in energy distribution and marketization, the energy Internet necessitates improved applicability and efficiency for personalized energy responses. To address these requirements, a multienergy trading market model is proposed, which enables the avoidance of user information disclosure and guarantees user trading autonomy. In addition, a joint trading mechanism is designed that accounts for multiple time scales and energy types, consequently reducing trading failures caused by overlooking energy transmission processes. By performing the proposed trading mechanism, the market operator can match various energy types using conversion devices, thereby augmenting matching efficiency. An income mechanism is also established to deter the operator from purposefully evading potential trading opportunities for personal gain. To address the proposed model, an improved hierarchical reinforcement learning algorithm is employed, which effectively overcomes challenges associated with large state action spaces and sparse rewards. Numerical examples are provided to confirm the efficacy of the proposed approach.

State of charge (SOC) and state of power (SOP) are two critical indices for lithium-ion batteries. Due to the complex operating environment in real applications, battery temperature can change significantly. This will lead to varied battery internal characteristics and pose a challenge for battery modelling and state estimation. This paper proposes an adaptive approach for battery online SOC and SOP co-estimation considering temperatures. First, a fractional-order multi-model system (FO-MMS) is constructed by integrating three sub-models at $-$ 5 Celsius, 20 Celsius, and 45 Celsius. To accommodate the battery current-voltage behaviors at different loads, SOCs, and temperatures, the contribution coefficient of each sub-model is adapted online through a temperature-embedded regularized moving horizon estimation algorithm. Second, a fractional-order multi-model proportional-integral observer (FO-MM-PIO) is designed for SOC estimation which achieves rapid convergence and suppresses external disturbances using the H-infinity criteria. Moreover, the nonlinear charge transfer dynamics of a battery under intense loads is simulated through the Butler-Volmer equation. An iterative approaching algorithm is then derived to estimate battery SOP in high fidelity. The experimental validations demonstrate that the proposed co-estimation method achieves the mean absolute error of less than 1.3% in SOC estimation and 2 W in SOP estimation, even at a sub-zero temperature (i.e., $-$ 5 Celsius).

An improved model-free predictive current control scheme based on double voltage vector for two-level voltage source inverters (2L-VSIs) is proposed in this article. First, two basic voltage vectors (VVs) are chosen in each control period, whose durations are calculated based on the cost function. Then, a novel current gradient updating method is developed by using the applied two VVs to calculate the current gradients of the remaining six VVs. As a result, the stagnation effect can be eliminated and current ripples and total harmonic distortion can be reduced effectively. In addition, due to the nature of vector diagram symmetry, the processing of current gradient updating is simplified and the calculation speed is increased. The effectiveness of the proposed scheme is verified by simulation and experimental results from the 2L-VSI system.

Accurate state of charge (SOC) estimation provides an essential basis for the functionalities of battery management systems in electric vehicles (EVs). However, conventional equivalent circuit models suffer significant model accuracy deterioration under extreme SOCs, nonroom temperatures, and heavy loads. In this work, we implant the Butler–Volmer (BV) equation and the fractional-order model representation into a model-based physics-informed neural network (M-PINN) to simulate current-dependent battery charge transfer dynamics under various operating conditions. This M-PINN replaces the original neuron structure with a set of submodels and allows the BV coefficient to be randomly selected in a roughly estimated range for each submodel. By applying the Lyapunov analysis, a self-adaptive neural network-based fractional-order observer is proposed to guarantee the uniform ultimate boundedness stability of both system states and M-PINN weights, thereby achieving accurate online SOC estimation without necessitating substantial data and efforts for offline neural network training. The experimental validations are implemented under three EV driving profiles with different average currents at −5, 5, 20, and 35 Celsius. The validation results demonstrate that the proposed method achieves the mean absolute errors of less than 0.9% in all the validation scenarios.

A brushless dc (BLDC) motor with an outer rotor has great potential for emerging electric vehicle (EV) applications as an in-wheel hub motor. Interturn short circuit faults (ISCFs) are common electrical faults in these applications, and their fault diagnosis is of critical importance. This article proposes a signal analysis-based method to detect and quantitatively analyze the ISCF in a BLDC motor under a six-step commutation control strategy. The zero-sequence voltage component (ZSVC) and three-phase currents are simultaneously determined while the fundamental frequency amplitude of the ZSVC is developed as a fault indicator for diagnostic purposes. Thereafter, the faulty phase is identified, and the fault severity is estimated by jointly analyzing the ZSVC and faulted phase current. The proposed technique entails creating an analytical model, a simulation model, and experimental tests. Experimental results demonstrate that the proposed technique has excellent accuracy, quick response, and real-time fault diagnosis for BLDC motors. This technique will accelerate the use of BLDC-based hub motors in EVs and the widespread application of EVs to reduce the carbon emissions.

A novel nine-level inverter topology for low voltage applications is proposed in this paper. Each phase of the inverter is composed of a T type three level cell and an active neutral point clamp three level cell. The operation principles and current conduction paths are analyzed in detail and phase-disposition sinusoidal pulse width modulation is used to balance voltages of flying capacitors and neutral point. The mathematical relationship between the capacitance and the desired ripple in the inverter output is established to select the intermediate capacitance value. Compared with other nine-level inverters suitable for low voltage applications, the proposed inverter has advantages in a number of gate drivers, a number of dc sources and efficiency. Simulation and experimental results under different settings of modulation index, power factor, output frequency and load are presented to verify the effectiveness and performances of the proposed inverter.