ALBERT

Description: There is an increasing interest in properly representing local driving patterns. The most frequent alternative to describe driving patterns is through a representative time series of speed, denominated driving cycle (DC). However, the DC duration is an important factor in achieving DC representativeness. Long DCs involve high testing costs, while short DCs tend to increase the uncertainty of the fuel consumption and tailpipe emissions results. There is not a defined methodology to establish the DC duration. This study aims to study the effect of different durations of the DCs on their representativeness. We used data of speed, time, fuel consumption, and emissions obtained by monitoring for two months the regular operation of a fleet of 15 buses running in two flat urban regions with different traffic conditions. Using the micro-trips method, we constructed DCs with a duration of 5, 10, 15, 20, 25, 30, 45, 60, and 120 min for each region. For each duration, we repeated the process 500 times in order to establish the trend and dispersion of the DC characteristic parameters. The results indicate that to obtain driving pattern representativeness, the DCs must last at least 25 min. This duration also guarantees the DC representativeness in terms of energy consumption and tailpipe emissions.

Description: Although the energy management strategies at present have achieved a good effect, they still have their limitations, so there is still room for further improvement to improve the fuel economy of hybrid electric vehicles (HEV). This paper proposes an adaptive equivalent consumption minimization strategy (ECMS) based on speed prediction, which can distribute power more reasonably and improve the power balance and fuel economy. The driving speed reflects the operation of the road and driver during the driving process. Under the motor assisted energy management control strategy, knowing all working condition information in advance can improve the battery power use planning to a certain extent and reduce the fuel consumption during the whole driving process by adjusting parameters. In this paper, a novel adaptive adjustment method for the equivalent factor (EF) of the ECMS based on future information is proposed. In this paper, a novel speed-prediction method combined with wavelet packet transformation (WPT) and a radial basis function neural network (RBF-NN) is proposed to realize accurate vehicle speed prediction. Then, the optimal equivalent factor under the state of charge (SOC) constraint is calculated by using the predicted speed. Simulation studies are conducted to verify the effectiveness of the proposed adjustment method for the EF compared to a commonly adjustment method from SOC balance and economic viewpoints.

Description: Load transfer is an important way of restoring a power supply after equipment failure or maintenance. However, current methods ignore cooperation between networks and users, and they also fail to take into account the vehicle-to-grid (V2G) potential of electric vehicles (EVs). In this paper, a load transfer scheme between transmission and distribution networks is proposed, considering the mobile energy storage capacities of electric vehicles. First, the mobility characteristic and the available discharge capacity of EVs are analyzed on the basis of the parking generation rate. Then, the breadth-first algorithm is used to search the load transfer paths within and between stations, and an edge matching method is proposed to realize the conversion between networks with different voltage levels. Lastly, the optimal combination weighting method was adopted to combine subjective and objective index weights and to evaluate power supply paths. The effectiveness of the proposed scheme is validated in a case composed of an IEEE 30-node network and an IEEE 57-node network, with four typical scenarios.

Description: To solve the problem of severe DC bus voltage fluctuations caused by frequent changes in the distributed electric propulsion aircraft load, and to further optimize the size and life of the hybrid energy storage system (HESS), this paper proposes a method based on three-step power distribution (TSPD). This strategy realizes the reasonable distribution of battery and supercapacitor power by using a low pass filter. Through the introduction of a supercapacitor state-of-charge (SOC) automatic recovery control and maximum power value dynamic limit strategy, the size of the HESS is optimized, and the service life of the energy storage device is extended. Finally, simulation and experiment platform are built to prove the effectiveness of the proposed strategy.

Description: In the operation process of a power battery pack, the inconsistency among lithium-ion cells may seriously restrict the pack’s capacity, power capability and lifetime, which may bring hidden danger to the use of electric vehicles. Equalization management systems (EMSs) are crucial to alleviate such inter-cell inconsistency, whose performance such as accuracy and stability, mainly depends on the setting of equalization control strategies. This paper proposes an equalization strategy aimed at throughput maximization of series battery in the whole life cycle based on Model Prediction Control (MPC). In this paper, a Mean-plus-difference model (M+D) model is selected as the series battery model and the parameters are identified by Recursive Least Squares (RLS). Based on the model predictive control theory, the control model of series battery pack is established and the objective function of maximizing the throughput in the whole life cycle is derived. At the end of the paper, the simulation results show that the proposed equalization strategy can achieve greater life cycle throughput compared with the traditional SOC equalization strategy, which verifies the guiding significance of the equilibrium strategy proposed in this paper.

Description: In recent years, the orderly charging of electric vehicles (EVs) in commercial parking has become a meaningful research topic due to the increasing number of EVs, especially for parking lots close to workplaces and serving fixed users. In this paper, a parking lot energy management system integrated with energy storage system (ESS) and photovoltaic (PV) system is established. The concept of energy price tag (EPT) is introduced to define the price of all energy storage devices, and the priority order between PV, ESS, EVs, and power grid is established. Taking the minimization of charging cost as the optimization objective, the charging plans of ESS, EVs, and buildings are optimized considering the constraints of EVs user demand and PV power. By comparing the simulation results of four cases, it is proven that this strategy can reduce the charging cost and improve the consumption rate of PV.

Description: Exploring the system-level interactions within the modern urban transportation system, factors such as human health, vehicle exhaust pollution, air quality, emerging personal transportation technologies, and local weather events, are increasingly expedient considering the growth of human population centers projected in the 21st century. Pollutants often accumulate to unhealthy concentrations during winter inversion events such as those that commonly occur in Utah’s Salt Lake valley and other mountainous regions. This work examines the degree to which replacing conventionally powered vehicles with electric vehicles (EV) could reduce the near-road accumulation of criteria pollutants under various degrees of inversion depth and wind speed. Vehicle emissions data are combined with inversion and wind factors to determine changes in the Air Quality Index, and a first-order estimate of the cost required to build an EV charging infrastructure to support a given EV adoption scenario is also derived. Results are presented in the form of multiple Pareto frontiers and a simplified cost–benefit formula that inform potential public and private EV charging infrastructure investments to drive the EV adoption that would result in optimal air quality improvements during average weather and winter inversion events.

Description: The ramp-up of battery electric vehicles (BEVs) could lead to severe grid issues but also enables flexibility. This paper provides a better understanding of the challenges and potentials of integrating BEVs in power grids. Three charging strategies were modelled on four use cases and six low-voltage grids for urban and rural areas. Especially in rural areas, where many cars charge at home overnight, the charging strategy significantly affects grid issues. Purely market-oriented strategies can lead to high load peaks and thus to transformer and line overloading, while even a relatively simple, balanced charging strategy can significantly reduce grid issues.

Description: In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. Particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform.

Description: Due to the open structure of wireless charging systems, foreign objects are inevitably involved. In the process of energy transfer, the intervention of metal foreign objects will cause the system to deviate from the normal operating point and even cause safety accidents. Therefore, metal foreign objects detection (MOD) technology is of considerable importance. In this paper, a metal foreign object detection system of a wireless power transfer (WPT) system based on the symmetrical detection coil was designed, and a MOD method based on frequency scanning is proposed. The coil size of the WPT system was designed based on SAE-J2954 standard. In addition, the suitable detection coil was installed at the transmitting coil. The system model was established in ANSYS simulation software, and 5 cm × 5 cm copper sheet and iron sheet were used as the metal to be detected. The result shows that the self-induction of the detection coil at the position of the foreign object can achieve a fluctuation of 52.46% and −34.72%, and the resonant frequency of the detection system is offset by 24.7% and −18.82%, respectively. Finally, the experimental platform of MOD system was built, and the effectiveness of the system was verified.