Power-based electric vehicle energy consumption model: Model development and validation

This work investigated the effect of a Nu Vinci type Continuous Variable Transmission (CVT) based Energy Efficient Cruise Control (EECC) on an Electric Vehicle's (EV's) energy consumption. Unlike petrol and diesel vehicles, almost all EVs have a fixed gear ratio. Although it reduces the capital cost, it may not offer optimal energy efficiency and may increase the recharge costs. While accelerating and cruising, it may cause an EV to spend more energy than needed. While braking, it may cause underutilisation of the regenerative braking potential. In this work, an EECC was designed to operate the EV's power train close to its peak efficiency region by controlling the CVT ratio and Electric Machine (EM) torque. The EECC exploits data from the lead vehicle and employs a constrained linear Model Predictive Control (MPC) with a novel problem formulation that reduces the computational complexity. The proposed EECC was tested in a MATLAB simulation environment for different drive cycles. The results show that compared to a baseline EV with a fixed gear ratio and an Adaptive Cruise Control, the proposed system can reduce an EV's energy consumption in urban drive cycles by 16.6%.

Estimating the HVAC energy consumption of plug-in electric vehicles

Microscopic series plug-in hybrid electric vehicle energy consumption model: Model development and validation

Referring to GB/T 18386-2017, the impact of NEDC, WLTC and CLTC on the energy consumption of pure electric vehicle is simulated and analysed. Combined with the characteristic parameters of the driving cycles, from the perspective of energy flow loss of each system of the vehicle, the influences of driving cycles on the energy consumption of each part of the vehicle and the working efficiency of the power components are analyzed in detail, and the internal mechanism of the influence rule of driving cycles on energy consumption is revealed. Based on the CLTC, the effects of load mass, ambient temperature and air conditioning on the energy consumption of the electric vehicle are explored. The research results show that the electric vehicle has the longest driving range and the lowest energy consumption under CLTC; as the loading quality increases, the driving range decreases, and the energy consumption per 100 kilometers increases; test temperature and the air conditioner have a great influence on the driving range, especially in the low temperature environment.

Vehicles offer people many conveniences, but at the same time cause considerable problems, for example for the environment. Today, the electrification of the automobile is considered the best way to solve at least a large part of the environmental problems caused by motor vehicles. Apart from the fact that an electric powertrain produces no local emissions, it also differs from a conventional fossil-fuel powertrain in its propulsion dynamics. It is therefore also to be expected that electric vehicles will influence the flow of traffic due to their different dynamic properties. This thesis focuses on using microscopic traffic simulation to provide a first estimation of how the electrification of vehicles could change the traffic flow and to what extent different traffic situations would influence the fuel and energy consumption of vehicles. In most microscopic traffic flow models, drivers and vehicles are modeled together by a combined model in one simulation object. By adjusting some parameter values, different combinations of driver and vehicle can be roughly distinguished. The values are determined on the basis of observational data of the traffic flow. However, the currently still small share of electric vehicles in road traffic and the fact that they look little different from combustion engine driven and hybrid vehicles make it difficult to collect data from electric vehicles in traffic flow, unless the data is collected in fleet tests. In this thesis, drivers and vehicles were modeled separately. The focus of this thesis is on the assessment of the influence of vehicle propulsion on traffic flow. Therefore, the model developed in this thesis was based on the physical capabilities of both the fossil-fuel powered and the electric powered vehicles. The driver model, which was also required, was created on the basis of data of human drivers, which were determined in an accompanying investigation of a driving simulator. Driver and vehicle model were combined again in the microscopic traffic simulation to a car-following model. For observing the impact of electric vehicles on traffic flow and estimating energy consumption of vehicles, a traffic scenario of a part of the City of Duisburg was established. The effect of electric vehicles on traffic flow, as well as the effect of traffic density on fuel and energy consumption was studied based on this scenario. The simulation results indicate that electric vehicles could raise the average speed of traffic flow to a certain degree, when traffic load and driving styles of the drivers remain the same. The increase of the share of electric vehicles has a positive effect on traffic efficiency. In situations with higher traffic density, energy consumption of electric vehicles is far superior to that of vehicles with internal combustion engine powertrains.

Modelling of energy consumption in electric vehicles is important for operation planning and smart charging strategies. This study proposes an energy consumption modelling and estimation strategy for autonomous electrified shuttles used for public transportation in Linköping. Operational experiences have highlighted the significant influence of weather conditions on the vehicles’ range, and the main complicating factor here is that detailed HVAC system data is unavailable. Therefore, a model is developed for estimating the energy consumption of the shuttles based on a physical formulation of vehicle dynamics and thermodynamics principles under varying weather conditions. The estimation relies on a fusion of predicted meteorological data, such as ambient temperature, and historical shuttle records, including interior temperature, vehicle speed, and discretized State of Charge (SoC). The lack of detailed HVAC operation information, e.g., cooling followed consecutively by heating within the same energy prediction profile, makes energy consumption estimation a challenging task. Even with some dynamic data available specifically for the shuttle’s short waiting time, these challenges render the HVAC system unobservable for nearly identical operations. To overcome this issue, a statistical approach combined with parameter estimation is employed to determine vehicle dynamic parameters and compute the thermal energy consumption and parametrization of the HVAC system. The data-driven model shows a satisfactory fitting level of R 2 with 0.85.

In most of the purchasable electric vehicles a kind of drag torque emulation is implemented, i.e. the regenerative brake already works when the accelerator pedal is released and not only when the brake pedal is pressed. The effect of that drag torque on the energy consumption is analyzed for several cooperative braking systems with differently developed blending of the torque generated by the mechanical friction brakes and the torque generated by the electric drive train. By means of simulations, it is shown that the drag torque reduces energy consumption, mainly when using simple braking systems. With advanced systems, it can be counterproductive. The positive effect for an electric vehicle with a simple braking system is validated by means of measurements.

Eco-driving is a key strategy for reducing energy consumption and emissions in electric vehicles (EVs) and internal combustion engine (ICE) vehicles. However, research gaps remain regarding its effectiveness across different driving environments, vehicle types, transmission systems, and contexts. This research evaluates eco-driving efficiency in urban and interurban settings, comparing small (Caceres) and large (Madrid) cities and assessing EVs ICE with direct, manual, and automatic transmissions. The authors conducted a large-scale driving experiment in Spain, with over 500 test runs across different road types. Results in the large city show that eco-driving reduces energy consumption by 30.4% in EVs on urban roads, benefiting from regenerative braking, compared to 10.75% in manual ICE vehicles. Automatic ICE vehicles also performed well, with 29.55% savings in local streets. In interurban settings, manual ICE vehicles achieved the highest savings (20.31%), while EVs showed more minor improvements (11.79%) due to already optimized efficiency at steady speeds. The small city showed higher savings due to smoother traffic flow, while single-speed transmissions in EVs enhanced efficiency across conditions. These findings provide valuable insights for optimizing eco-driving strategies and vehicle design. Future research should explore AI-driven eco-driving applications and real-time optimization to improve sustainable mobility.

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller.

<div>Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This article reviews the state of the art of EV energy consumption models, aiming to provide guidance for the future development of EV applications. We summarize influential variables of EV energy consumption in four categories: vehicle component, vehicle dynamics, traffic, and environment-related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data driven vs. rule based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilize machine learning technologies to estimate EV energy consumption based on a large volume of real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, trucks, and nonroad vehicles), energy estimation models that are suitable for applications related to vehicle-to-grid integration, and multiscale energy estimation models as a holistic modeling approach.</div>

Author(s): Wu, Guoyuan; Zhao, Zhouqiao; Wang, Ziran; Barth, Matthew J. | Abstract: With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpage

A three-heat source segmented heating control strategy based on waste heat recovery technology for electric vehicles

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy.

Electrifying transportation in the form of the large-scale development of electric vehicles (EVs) plays a pivotal role in reducing urban atmospheric pollution and alleviating fossil fuel dependence. However, the rising scale of EV deployment is exposing problems that were previously hidden in small-scale EV applications, and the lack of large-scale EV operating data deters relevant explorations. Here, we report several issues related to the battery utilization and energy consumption of urban-scale EVs by connecting three unique datasets of real-world operating states of over 3 million Chinese EVs, operational data, and vehicle feature data. Meanwhile, by incorporating climatic data and EV data outside China, we extend our models to several metropolitan areas worldwide. We find that blindly increasing the battery energy of urban EVs could be detrimental to sustainable development. The impact of changes in the energy consumption of EVs would be exacerbated in large-scale EV utilization, especially during seasonal shifts. For instance, even with a constant monthly driving demand, the average energy consumption of Beijing light-duty EVs would change by up to 21% during winter-spring shifts. Our results may also prove useful for research on battery resources, urban power supply, environmental impacts, and policymaking.

Density-based clustering multiple linear regression model of energy consumption for electric vehicles