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Hub-driven technology with its many advantages has become a potential transformation technology in the automotive industry. In order to inhibit the negative effects of the in-wheel motor, a new electric wheel configuration was presented with two-stage suspensions, and optimization was conducted for the suspension parameters. With the specially introduced flexible transmission elements and motor suspension, the electric wheel is capable of achieving power transmission and vertical vibration isolation simultaneously. A quarter-vehicle model was established, and vehicle performance-related indices were defined to evaluate the applicability of the new electric wheel. An optimization problem was then constructed for suspension parameters, where constraint conditions were derived according to vehicle dynamics. The optimal solution was sought by a genetic algorithm, aiming at utilizing the full potential of the configuration. Simulations were carried out taking the optimal suspension parameters into account, and comparison was made with vehicles equipped with other electric wheel configurations in terms of these performance indices. The results show that the proposed electric wheel reduces motor vibration, and increases wheel dynamic load and vehicle body acceleration.

In this paper, a novel constant current control method, with improved dynamic performance over the conventional PI method, is proposed for the CLLC converter. We proposed a state trajectory model for the CLLC converter. Based on the proposed the state trajectory analysis, a novel dead-band based control method with improved dynamic performance is proposed. The effectiveness of the proposed method was verified by experimental results. The response time was hugely reduced (less than 50% of the PI method) and no overshoot was detected.

As the rapid development of renewable energy and electrification of transport, the stability and efficiency of power system will encounter inevitable challenges. A decentralized Vehicle-to-Grid (V2G) framework for fast charging infrastructures joining in the primacy frequency regulation is proposed in this paper to cope the fluctuation of renewable energies. Two interaction modes are transferred according to the state of charge (SOC) of PEVs (Plug-in electric vehicles) in order to meet the fast-charging demand of drivers primarily. Based on the engineering system of vehicle battery pack, we fabricate detailed interaction logics among fast charger, V2G controller, battery pack and battery management system (BMS). The effective V2G control strategies need to consider the power limitations and charging effects. As a consequence, an equivalent circuit battery model (ECM) with high accuracy for dynamic and fluctuation working conditions is employed to obtain power limitations of lithium-ion batteries (LIBs). Furthermore, fast-charging-oriented droop control strategies are designed to participate regulation of power grid with negligible impact on fast charging. An alternative current (AC) power system with high penetration of renewable resources is constructed to verify the effects. The results of case study reveal that the proposed method with bidirectional chargers can reduce the fluctuation from renewables, loads and PEVs by 21.22%, accompanied by PEVs’ SOC changes lower than 0.5%. Interestingly, a high renewable penetration system can eliminate frequency derivation by adding PEVs with proposed regulation.

Optimizing the size of photovoltaic (PV) and energy storage system (ESS) is a key issue to ensure reliable, resilient and economic operation of the microgrid (MG). However, multiplicity of electric vehicle (EV) load profiles and computational complexity of the optimizing models limit the application of existing methods upon restricted scenarios. In this paper, a novel framework combining physical-economical model (PEM) and data-driven model (DDM) is established. First, a data augmentation approach based on reversed Monte Carlo (RMC) is proposed to generate comprehensive synthetic EV load datasets for big data training. Subsequently, a PEM is given to determine the capacity configuration of PV and ESS under each load condition, with the objective to obtain the best financial profitability. The model takes into consideration battery degradation and optimal power dispatch. Finally, a DDM is trained with machine learning techniques to relate optimal sizes with input of EV profiles and MG features. Testing results illustrate that in the test sets, the neural network could predict the optimal configuration within 15% and 20% relative error, respectively, enormously narrowing design space of PEM, and thus produces over 88% runtime savings while maintaining high fidelity and optimality.

Summary Heating battery at low temperatures is fundamental to avoiding the range anxiety and the time-consuming charging associated with electric vehicles (EVs). One method for achieving fast and uniform battery heating is to polarize the cell under pulse currents. However, the on-board implementation of this method leads to an increase in the cost and size. Therefore, in this study, an adapted EV circuitry compatible with the existing one and an optimized operating condition are proposed to enable rapid battery heating. With this circuit, electricity transfer between the cells can be realized through a motor, leading to remarkably higher battery currents than those of the conventional circuit. The increase in the maximum heating currents (from 1.41C to 4C) resulted in a battery temperature rise of 8.6°C/min at low temperatures. This heating method exhibits low cost, high efficiency, and negligible effects on battery degradation, practical and promising on battery heating of EVs., Graphical abstract, Highlights • Rapid temperature rise, high efficiency, and low cost for battery heating are obtained • The batteries are divided into two modules and connected to the inverter individually • The electricity transferred between battery modules is motivated by a static motor • The pulse currents for battery heating are improved significantly at any frequency, Electrochemistry; Electrochemical Energy Storage; Energy Engineering; Energy Systems; Energy Materials

The anode potential inside the lithium-ion battery is crucial for battery internal state observation in electric vehicles since it indicates the states of lithium deposition at the anode surface. Conventional equivalent circuit models (ECMs) used in the battery management system (BMS) can only predict limited battery external characteristics. Electrochemical models which can simulate the battery internal behaviors are too complex for onboard applications. In this paper, a novel decomposed electrode model (DEM) is proposed for real-time anode potential observation in real BMSs. The cathode and anode parameters can be calibrated by inserting a reference electrode inside the battery. The decomposed electrode model shows high accuracy in predicting the cell terminal voltage and the anode potential under various current conditions. This model also has predominance considering both model simplicity and computational efficiency. An online anode potential observation algorithm is developed based on the DEM and the extended Kalman filter. A lithium plating-free fast charging algorithm is formulated further by integrating the closed-loop potential observer and the closed-loop current controller. The result indicates that the proposed decomposed electrode model is suitable for online applications in the BMS.

Low temperature charging is a major challenge for lithium-ion batteries, since it could lead to dramatic performance degradation and potential safety issues. A pre-heating process is usually applied to overcome above-mentioned challenges. Pulsed operation is adopted as one of established internal pre-heating methods with good temperature uniformity. Herein we employed and investigated bidirectional pulsed current through experimental methods to obtain the main data of the thermal action for comprehensively analyzing heat generation characteristics and thermoelectric coupling model based on second-order RC circuit to verify the basic principle. Battery durability research was then conducted via a continuous heating test method which enables rapid testing of capacity degradation. The results indicated that proposed pulsed heating could not significantly damage the life span from the perspective of long-term applications: the battery has only 1% capacity decay after 170 h continuous heating with a heating rate of 11 °C/min. Parameters, which are beneficial for heating rate, are further found to be detrimental for degradation and vice versa. Nevertheless, based on the outcome of this study, the pulse waveform with a shorter period and a higher amplitude are suggested to give an optimal combination of higher heating rate and lower degradation. Such a pulsed heating method has several potential application scenarios, ranging from electric vehicles and even stationary storage systems.

In this study, the authors propose an efficient third-order cyclic moment-based estimation of signal parameters via rotational invariance techniques algorithm to cope with the mixed far-field and near-field cyclostationary sources localisation problem. The key point is that the auto-pairing parameters of near-field bearing and range is realised by estimating the unique non-singular matrix between the signal subspace and the matrix of direction vectors. This algorithm firstly constructs two special third-order cyclic moment matrices, in which the rotational factor is the function of the direction-of-arrival (DOA) and range of mixed far-field and near-field sources. By implementing the singular value decomposition and the high-resolution multiple signal classification spectral search, the authors estimate the azimuth DOAs for all the incoming sources. Then, the authors perform the estimation of the unique non-singular matrix between the signal subspace and the matrix of direction vectors. Finally, the range of near-field sources is determined from the obtained rotational factor. The proposed algorithm is computationally more efficient than the previous works, and it avoids pairing parameters. Computer simulations are carried out to demonstrate the performance of the proposed algorithm.