World Electr. Veh. J., Volume 13, Issue 9 (September 2022) – 16 articles

In this paper, a battery pack and a supercapacitor bank hybrid energy storage system (HESS) with a new control configuration is proposed for electric vehicles (EVs). A bidirectional quasi-Z-source inverter (Bq-ZSI) and a bidirectional DC-DC converter are used in the powertrain of the EV. The scheme of the control for the proposed HESS Bq-ZSI using finite control set model predictive control (FCS-MPC) is first deduced to enhance the dynamic performance. With the idea of managing battery degradation mitigation, the fractional-order PI (FOPI) controller is then applied and associated with a filtering technique. The Opal-RT-based real-time simulation is next executed to verify the performance and effectiveness of the proposed HESS control strategy. As a result, the proposed HESS Bq-ZSI with this control scheme provides a quick response to the mechanical load and stable DC link voltage under the studied driving cycle. Moreover, the comparative results also show that the proposed HESS Bq-ZSI equipped with the new control configuration enables the reduction of the root-mean-square value, the mean value, and the standard deviation by 57%, 59%, and 27%, respectively, of the battery current compared to the battery-based inverter. Thus, the proposed HESS Bq-ZSI using these types of controllers can help to improve the EV system performance. Full article

Requests for emission reduction in the freight transport sector will be more intense in the coming years. One possible strategy to reduce emissions from freight transport is through utilising zero emission vehicles, which requires substantial investments both by transporters and by authorities. This paper examines how green public procurement (GPP) can be used to push the market in an environmentally sustainable direction. For this purpose, interviews with both public authorities and freight service providers are conducted. The results show that GPP is considered a useful tool for public authorities to both boost the uptake of zero emission vehicles and to share the investment costs with freight service providers. However, our study shows that there are differences between small and large municipalities. Moreover, to succeed with GPP, public authorities must prioritise such tasks in their daily routines through political decisions and strategies. Additionally, barriers related to financial possibilities are crucial to handle, as public support schemes are important to reduce costs for all involved stakeholders. Altogether, our paper shows that with the right tools and willingness among both public and private stakeholders, GPP can contribute to the use of more environmentally friendly solutions in the freight transport sector. Full article

With the development of the global economy, the automobile industry is also developing constantly. In recent years, due to the shortage of environmental energy and other problems, seeking clean energy as the power source of vehicles to replace traditional fossil energy could be one of the measures to reduce environmental pollution. Among them, fuel cell hybrid electric vehicles (FCHEVs) have been widely studied by researchers for their advantages of high energy efficiency, environmental protection, and long driving range. This paper first introduces the topology of common FCHEVs and then classifies and introduces the latest energy management strategies (EMSs) for FCHEVs. Finally, the future trends of EMSs for FCHEVs are discussed. This paper can be useful in helping researchers better understand the recent research progress of EMSs for FCHEVs. Full article

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver’s driving habits in terms of ensuring following safety. Full article

The resistance of the internal short-circuit (ISC) has a potential evolution trend accompanied by an increasing safety risk. Thus, an accurate online resistance estimation for the ISC is crucial for evaluating its safety risk and taking staged handling measures. Since the ISC battery mainly presents abnormal stage of charge (SOC) depletion behaviors, the SOC estimation processes based on state observers and battery models will act an important basis of the ISC resistance estimation problem. However, as it will be exhibited in this paper, when directly using the measured voltage of the ISC battery as the output variable of the state observer, the battery model error will limit the SOC estimation accuracy and further lead to very inaccurate or even divergent ISC resistance estimation results for large-format batteries, which present quite slight SOC depletion behaviors at the ISC state. To this end, this paper proposes a novel SOC and ISC resistance co-estimation method which combines a reconstruction method of the model-predicted voltage of the ISC battery. Experimental validations are carried out with a 37 Ah battery, results show that the proposed method which uses the reconstructed model-predicted voltage (RMPV) as the output variable of the state observer only present maximum estimation errors of 39.96 Ω and 2.00 Ω for the ISC resistances of 100 Ω and 10 Ω, respectively. Full article

In the context of smart cities, direct current overhead contact lines, usually adopted to power urban transportation systems such as trolleybuses, tramways, metros, and railways, can serve as a backbone to connect different modern emerging technologies. Among these, in-motion charging (IMC) trolleybuses with on-board batteries are expected to be very impactful on the DC network’s power flow and may require specific voltage and current control. These factors motivate the development of a simulation tool able to emulate these devices’ absorption and their effect on the supply infrastructure. The main innovative value of the work is to improve a simulation model of a trolleybus grid through a data-driven approach by using measurements of voltage and current output from a traction substation. The measurements are essential for understanding the behavior of vehicle weight variation throughout the day. Thanks to this information, a characterization of the current draw by conventional trolleybuses and IMC trolleybuses is then provided for each trolleybus route in a specific power section of the Bologna trolleybus system. By integrating the variation in vehicle weight within the model, a simulation of a possible daily operation of a trolleybus feeding section has been performed, obtaining a 7% error between the daily energy calculated from the simulation and that obtained through measurements. This analysis demonstrates the feasibility of the adopted simulation tool, which can also be used to evaluate additional hypothetical trolleybus operation scenarios. One of these possible scenarios considers IMC vehicles, and it is also evaluated in this paper. Full article

Besides the integration of renewable energies, electric vehicles pose an additional challenge to modern power grids. However, electric vehicles can also be a flexibility source and contribute to the power system stability. Today, the power system still heavily relies on conventional technologies to stay stable. In order to operate a future power system based on renewable energies only, we need to understand the flexibility potential of assets such as electric vehicles and become able to use their flexibility. In this paper, we analyzed how vast amounts of coordinated charging processes can be used to provide frequency containment reserve power, one of the most important ancillary services for system stability. Therefore, we used an extensive simulation model of a virtual power plant of millions of electric vehicles. The model considers not only technical components but also the stochastic behavior of electric vehicle drivers based on real data. Our results show that, in 2030, electric vehicles have the potential to serve the whole frequency containment reserve power market in Germany. We differentiate between using unidirectional and bidirectional chargers. Bidirectional chargers have a larger potential but also result in unwanted battery degradation. Unidirectional chargers are more constrained in terms of flexibility, but do not lead to additional battery degradation. We conclude that using a mix of both can combine the advantages of both worlds. Thereby, average private cars can provide the service without any notable additional battery degradation and achieve yearly earnings between EUR 200 and EUR 500, depending on the volatile market prices. Commercial vehicles have an even higher potential, as the results increase with vehicle utilization and consumption. Full article

With an influx of public and private sector investment in the electric vehicle (EV) domain, public agencies and stakeholders need objective, equitable and systematic processes for identifying candidate sites for siting charging stations. This paper reports on a case study examining the Indiana Interstate network using connected vehicle data (CV). The Indiana Interstate network analyzed by this study is composed of 1247 centerline miles along nine routes. Each month, approximately 13 billion CV records representing more than 44 million unique trips are generated along all roads in Indiana. For this study 3.02 billion records comprising 4.78 million trips on and around Indiana Interstates and Exits were analyzed for usage patterns. The CV data was predominantly from internal combustion engine vehicle (ICEV) passenger cars, but provides insight into exit utilization and dwell times at 544 exits on 9 interstate roadways to evaluate how their current usage would align with building out Indiana’s Alternative Fuel Corridors. A pareto sorted graphic for the top 50 busiest exits in the state shows that all but two are not well served by fast charging infrastructure. The paper suggests this pareto sorted list as a good starting point for further analysis and identified 15 exits on Indiana interstates, if chosen for deploying charging infrastructure, would ensure full compliance. The results provide a systemwide look at present dwell patterns among ICEVs and help identify locations of interest that would most benefit from addition of charging infrastructure as the current fleet of ICEVs gradually transitions to EVs. Full article

The electrification of the transport sector is of crucial importance for a successful transition to a fossil-free society. However, the electricity grid constitutes a bottleneck. This article provides a case study based on a real-world parking garage with a smart grid infrastructure, called Dansmästaren. The analysis shows how renewable energy sources, energy storage technologies, and smart charging of electric vehicles can smooth out the load curve of the parking garage and relieve the electric grid during peak hours. Dansmästaren is located in Uppsala, Sweden, and equipped with 60 charging points for electric vehicles, a PV system, and a battery storage system. The study utilizes an energy flow model to show the potential of a realistically dimensioned smart energy system, that can benefit the parking facility in itself and the local distribution grid in a city, Uppsala, with grid capacity challenges. The results suggest that the parking garage demand on the local grid can be significantly lowered by smarter control of its relatively small battery energy storage. Moreover, further smart control strategies can decrease demand up to 60% during high load hours while still guaranteeing fully charged vehicles at departure in near future scenarios. The study also shows that peak shaving strategies can lower the maximum peaks by up to 79%. A better understanding of the potential of public infrastructures for electric vehicle charging helps to increase knowledge on how they can contribute to more sustainable cities and a fossil-free society. Full article

In special locations (scenes) such as campuses and closed parks, small unmanned vehicles have gained more attention and application. Autonomous navigation is one of the key technologies of low-speed small unmanned vehicles. It has become a research hotspot, but there are still many problems, such as perception sensitivity, navigation, and positioning accuracy, motion planning accuracy, and tracking control accuracy. In order to sort out the research status of the key technologies of autonomous navigation for small unmanned vehicles more clearly, this paper firstly reviews the key technologies of autonomous navigation and presents an analysis and summary. Finally, future research trends of small unmanned vehicles with low speed are given. Full article

Electric vehicles, particularly those in mass transit systems, make use of accurate power estimations for different routes to calculate powertrain and battery requirements and plan the location and times of charging stations. Hence, chassis dynamometers are a common tool for vehicle designers as they allow for the emulation of vehicle performance and energy consumption by simulating realistic road conditions. In this paper, a method is presented where inertia events and negative slopes can be represented in the dynamometer through a single motor; allowing researchers to perform fast and cheap tests, while also considering the effect of these variables. A dynamic simulation is used to distribute the energy used in three ways: first, accelerating the vehicle by overcoming the forces opposing motion; second, emulating the kinetic energy delivered by the vehicle mass when decelerating; and third, emulating the energy delivered to the vehicle by negative slopes. Tests were carried out on a dynamometer validating the method through an example route, estimating energy consumption and regeneration; this method reduces the error in energy consumption by inertial effects and negative slopes, otherwise not considered in one motor dynamometers, showing a 9.11% difference between total test energy and real bus energy for this route. Full article

Amid growing concerns about climate change, electricity-powered transportation systems stand out as an opportunity to help in reducing fuel consumption. Electric vehicles (EVs) would connect to the grid using clean, renewable electricity; however, the interconnection between EVs and the grid brings about new challenges for traditional power systems. Plug-in hybrid EVs and plug-in EVs have started to become more prevalent in the system; therefore, their impacts and benefits are also of concern. Among these concerns is the detailed analysis of the impact that EVs may have on short-circuit levels in microgrid protection schemes. In this context, the main contribution of this paper is a detailed evaluation of the impact of EVs on the short-circuit levels and protection coordination schemes in microgrids. For this purpose, a methodology was proposed to measure the impact of EVs on the protection coordination schemes in microgrids using different evaluation indices. The proposed approach was validated on a benchmark IEC microgrid considering different operative scenarios that envisage several levels of EVs penetration. The results evidenced the applicability of the proposed approach and allows to conclude that the incorporation of EVs in microgids impacts the performance of the protection schemes, specifically with respect to short-circuit levels. Full article

Heavy-duty trucks account for 27% of the European greenhouse gas emissions in the transport sector. To decarbonize road freight transport, the European Union plans to build a fast charging network for trucks. This paper presents two scenarios, covering European highways with charging stations at regular intervals every 50 or 100 km along the most important highways. For each location, the required number of charging points at 15% battery electric trucking is calculated individually using queueing theory. A third scenario takes into account the infrastructure ramp-up in 2025 and assumes a share of 5% battery electric trucking in a network with a 100 km distance. We define a network of 660 (100 km distance) or 1468 stations (50 km distance). Depending on the scenario and the individual station, the projected number of charging points per station varies between 1 and 18 in 2030. The results give a first insight into what a fast charging infrastructure for trucks in Europe might look like. In particular, we show that large charging stations with more than ten charging points could be necessary in the next few years. This knowledge might help to design future charging infrastructure for electric road freight transport. Full article

Cutting greenhouse gas emissions to comply with the Paris Agreement is challenging for road freight. While heavy-duty battery-electric trucks (BET) promise tremendous and immediate reduction potential, literature increasingly confirms technical feasibility in general, and several manufacturers launched BET models. However, their real-world application is still being questioned by fleet owners due to the limited range or payload penalties. Thus, our case study aims to assess the technical feasibility of urban and regional delivery in Germany based on real-world and per-vehicle operational data that feed into an energy simulation with Monte-Carlo modeling. Our results demonstrate the importance of vehicle-specific examination for the right battery capacity that ideally matches the vehicle’s operating profile. We find that full electrification may be most accessible for 18-t and 26-t rigid solo trucks, soon followed by tractor-trailers, while truck-trailers turn out as most challenging. With up to 600 kWh battery capacity available in all truck classes, we find nearly 40% of all transport performance and 60% of all diesel trucks may be replaced with BET—while already 400 kWh is sufficient for half of all trucks. Additional measures such as intermediate charging and adjusted and more flexible truck-tour allocation may significantly accelerate electrification. Full article

In order to solve the difficulty for intelligent vehicles in detecting near-field targets, this paper proposes a near-field object detection method based on multi-sensor information fusion. Firstly, the F-CenterFusion method is proposed to fuse the information from LiDAR, millimeter wave (mmWave) radar, and camera to fully obtain target state information in the near-field area. Secondly, multi-attention modules are constructed in the image and point cloud feature extraction networks, respectively, to locate the targets’ class-dependent features and suppress the expression of useless information. Then, the dynamic connection mechanism is used to fuse image and point cloud information to enhance feature expression capabilities. The fusion results are input into the predictive inference head network to obtain target attributes, locations, and other data. This method is verified by the nuScenes dataset. Compared with the CenterFusion method using mmWave radar and camera fusion information, the NDS and mAP values of our method are improved by 5.1% and 10.9%, respectively, and the average accuracy score of multi-class detection is improved by 22.7%. The experimental results show that the proposed method can enable intelligent vehicles to realize near-field target detection with high accuracy and strong robustness. Full article

Battery state of charge (SOC), state of health (SOH), and state of power (SOP) are decisive factors that influence the energy-management system (EMS) performance of electric vehicles. However, the accurate estimation of SOC, SOH, and SOP remains a challenge due to the high nonlinearity of the battery dynamic characteristics and the strong coupling among the states. In this paper, different methods of single-state and two-state joint estimation are classified and discussed, including SOC/SOH and SOC/SOP joint estimation methods, and their advantages and limitations are analyzed. On this basis, key issues of joint multi-state estimation are discussed, and suggestions for future work are made. Full article