Mileage Anxiety Research Articles

Electric delivery vehicle routing problem optimization model with time-varying traffic congestion

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods, while relieving the mileage anxiety of delivery personnel, the pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly , a novel mixed integer programming model (TD-EVRP) is established to step for its extended model of EVRP, which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window, vehicle load, and vehicle charging requirements. Secondly, a traffic congestion avoidance strategy is introduced into the model, which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally, the improved ant colony algorithm is designed to acquire the optimal solution.

Battery health-considered energy management strategy for a dual-motor two-speed battery electric vehicle based on a hybrid soft actor-critic algorithm with memory function

Energy management strategies (EMSs) ease mileage anxiety and improve the health performance of battery electric vehicles (BEVs). This study proposes a soft actor-critic (SAC)-based EMS for dual-motor two-speed BEV to minimize electrical energy consumption and battery capacity losses. In addition, two optimization tricks are employed to optimize the original SAC. The linear mapping trick is integrated into the actor-network of SAC to enable agents to search for optimal EMS in a discrete (driving mode)-continuous (torque distribution) hybrid action space. The SAC-based EMS is modeled as a partially observable Markov decision process (POMDP) to obtain more information about the real state of the environment. Based on this, another optimization trick, the long short-term memory (LSTM) network, is integrated into the actor and critic of SAC to make full use of both historical and current environmental information. Simulation results show that the proposed method learns the optimal EMS, its converged episodes are shortened to the 97th, the health performance of the battery is the closest to the dynamic programming (DP)-based EMS, and maintains battery operating at a range of 30–40°C. In addition, the proposed EMS has the best adaptability in test cycles, reaching 99.42% and 99.29% of DP-based EMS, respectively.

Electric-thermal collaborative control and multimode energy flow analysis of fuel cell hybrid electric vehicles in low-temperature regions

The energy flow distribution characteristics of electric vehicles operating in various propulsion modes and all climatic scenarios have not been thoroughly explored. To achieve effective electric-thermal collaborative energy management, intelligent control methods must be applied considering various climatic conditions to alleviate mileage anxiety. In this study, we developed a novel electric–thermal collaborative energy management strategy based on an improved deep neural network and energy quantification model to increase the global energy conversion efficiency. The complete energy consumption distribution characteristics are summarized under various strategies and propulsion modes based on an experiment data collected by the vehicle control unit that involves battery self-heating, cabin heating, acceleration consumption, and fuel consumption in the temperature range of −10°C-35 °C. Our findings indicate that, for a fuel cell hybrid bus in the cycle including the initial cabin heating process, the heating consumption in the pure electric mode was 9.9 kWh/cycle and 13 kWh/cycle when the ambient temperature is −2 °C and −10 °C, respectively, accounting for 33 % and 42 % of the total consumption, respectively. After using the waste heat from the fuel cell, the consumption of electric heating under the same conditions is only 3.7 kWh/cycle. In the high-temperature scenario, the cabin cooling consumption is 3.26 kWh/cycle, accounting for only 18 % of the total energy consumption. Finally, in low-temperature scenarios, the electric–thermal collaborative strategy reduced the cost by 14.7 % and 9.2 % in the pure electric and hybrid modes, respectively. Thus, our approach significantly improves energy utilization and conversion efficiency, especially at low temperatures.

Electrothermal Model Based Remaining Charging Time Prediction of Lithium-Ion Batteries against Wide Temperature Range

Battery remaining charging time (RCT) prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles (EVs). Also, it is of great significance to improve EV users' experience. However, the RCT for a lithium-ion battery pack in EVs changes with temperature and other battery parameters. This study proposes an electrothermal model-based method to accurately predict battery RCT. Firstly, a characteristic battery cell is adopted to represent the battery pack, thus an equivalent circuit model (ECM) of the characteristic battery cell is established to describe the electrical behaviors of a battery pack. Secondly, an equivalent thermal model (ETM) of the battery pack is developed by considering the influence of ambient temperature, thermal management, and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling. Finally, the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from − 20 ℃ to 45 ℃. The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%.

A Novel Wired/Wireless Hybrid Multiport Energy Router for Dynamic EV Energy Internet With Grid-Tied and Islanded Operations

Energy routers (ERs) are indispensable in deploying energy Internet for electric vehicles (EVs), which flexibly handle the power flow regulation. Combined with the excellent characteristic of wireless power transfer (WPT) technology for non-contact energy transmission, this paper proposes a novel wired/wireless hybrid multi-port energy router (HMER) for future EV energy Internet. First, multiple bidirectional power ports are provided to control the power flow among the grid, various DC power sources, and EVs. Moreover, with grid-tied and islanded operations, the proposed HMER enables dynamic wireless energy exchange for EVs on the move. When moving along the energy exchange lane, the EVs can receive power from the HMER and conduct energy feedback, relieving mileage anxiety while creating economic benefits. Further, a mathematical model of the system is derived, and a control algorithm is introduced to perform decoupled power control for each transceiver. Finally, a SIC experimental prototype is constructed to verify the effectiveness of the proposed HMER system. Experimental results prove the transceivers can get bidirectional wireless energy interconnection on the energy exchange lane in both grid-tied mode and islanded mode, and each transceiver can realize independent power regulation.

Optimizing prices in trade-in strategies for vehicle retailers

In the vehicle industry, there are two trade-in services: TONF, that is trade-in for a new oil-fueled automobile (OFA), and TONE, that is trade-in for a new energy vehicle (NEV). So, when TONE and TONF coexist, how can companies make trade-offs among different trade-in strategies and formulate the corresponding optimal pricing. Therefore, considering the different of consumer’ mileage anxiety to NEVs, a supply chain trade-in game model composed of a manufacturer and a retailer was constructed. It not only discusses the threshold conditions for retailers to offer only TONE, TONF and both, but also gives the corresponding optimal pricing decision. We found the following: the retailer's trade-in strategy is not only affected by the production cost, but also related to the manufacturer’s recycling prices. Consumers’ mileage anxiety about NEVs will also have an important impact on the choices of retailers’ trade-in strategy, especially when the production cost is in the middle. Government subsidies for NEVs, durability and residual value gains of OFAs have a huge impact on retailers' choice of trade-in strategies when consumers have greater mileage anxiety, but have no effect when consumers have less mileage anxiety. When consumers have different mileage anxiety of NEVs, the retailer chooses the optimal trade-in strategy in the case of less mileage anxiety (Case Ⅱ) and may not be able to achieve ideal profits.

Development of charging technology for new energy vehicles

In the context of a global energy shortage, new energy vehicles are gradually becoming the mainstream trend of future automotive development. The development of fast and safe charging methods will bring a better experience to new energy vehicle users and gradually solve the problem of mileage anxiety for new energy vehicle users. At present, the main charging method of new energy vehicles is wired charging, but this charging mode charging time is slow and due to the external influence of the charging environment, causing the battery to overcharge the cars spontaneous combustion phenomenon, the application of wireless charging and power exchange technology to the future charging technology, wireless charging can bring higher charging efficiency and power exchange technology can bring similar to the fuel car replenishment energy experience. This paper first introduces the meaning of the three charging technologies and analyses their advantages and disadvantages. The three charging methods are described separately for wireless charging technology and compared with each other to arrive at the most suitable for future development. By comparing these three charging technologies, it can be seen that wireless charging technology is more suitable for the development trend of unopened new energy vehicle charging technology, and finally, the difficulties that need to be overcome for the future development of wireless charging are briefly described. This paper has important implications for developing new energy vehicle charging technology and popularizing the use of new energy vehicles.

Route Optimization of Electric Vehicles Based on Reinsertion Genetic Algorithm

The "mileage anxiety" of EVs has led to do the study on the electric vehicles routing problem (EVRP). In this paper, the remove-reinsert genetic algorithm (RI-GA) is applied to the EVRP with a time window (EVRPTW) for saving energy and reducing transportation costs. The initialization and the variation of the genetic algorithm are improved to obtain a routing schedule with less energy consumption. The neighborhood search method improves the population initialization to obtain high-quality initialized populations. Removal and reinsertion methods are proposed to destruct and repair the routing. During the removal process of RI-GA, the relevance removal strategy is designed. By comparing the correlation between nodes, the nodes with high correlation are removed simultaneously, providing directionality for the removal of the customer stops. The relationship between individual fitness and population average fitness is compared and the number of nodes removed is automatically adjusted. The minimum energy consumption incremental reinsertion method is employed in the reinsertion process of RI-GA to obtain the routing schedule with less energy consumption. Compared to the least-cost convergence of the original GA and RI-GA, the convergence speed of RI-GA is faster by 5 times, and the routing cost is reduced by 6%. The comparison with Solomons data demonstrates the advantages of the RI-GA, with a reduction of about 10% in route distance. The superiority of this improved algorithm becomes more apparent as the number of customer stops increases and the transportation routes become more complex, and the reduction in energy consumption increases from 7.95% to 12.33%. Finally, the performance of the RI-GA is verified in the apple transportation problem in Hudson Valley, New York, which proves the practicality of this algorithm.

State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature

As the main energy storage component of electric vehicles (EV), lithium-ion battery state estimation is an essential part of the battery management system (BMS). State of Energy (SOE) is one of the important state parameters, and its accurate estimation effectively reduces the potential safety hazards in the use of lithium-ion batteries, improves the efficiency of energy utilization, and alleviates the mileage anxiety of drivers. To solve the problem that the prediction of SOE of lithium-ion batteries is greatly influenced by temperature, a novel method called adaptive fuzzy control forgetting factor recursive least squares-Adaptive extended Kalman filtering (AFCFFRLS-AEKF) is formed. A fuzzy logic controller is designed for adaptive adjustment of the online parameter recognition forgetting factor with the change of working conditions. To solve the problem that the open-circuit voltage (OCV) changes with the influence of temperature in the variable temperature range, the regression analysis method is used in modeling to realize the regression analysis of OCV in a wide temperature range. Estimation accuracy is verified under two working conditions. The error of the estimation considering the temperature effect converges within 1 %, which achieves higher estimation accuracy and stronger robustness.

Optimal Routing and Charging of Electric Logistics VehiclesBased on Long-Distance Transportation and Dynamic Transportation System

The application of electric vehicles (EVs) in the logistics industry has become more extensive. However, the mileage limitation of electric logistics vehicles (ELVs) and the long-distance distribution of ELVs have become urgent problems. Therefore, this paper proposes a long-distance distribution model for ELVs based on dynamic traffic information considering fleet mileage, distribution time and total distribution cost as the optimisation objectives, thus reasonably planning road selection and charging, and alleviating “mileage anxiety” in the long-distance distribution of ELVs. The model proposed in this paper comprehensively considers the characteristics of the high-speed and low-speed roads, the changes in road traffic flow on weekdays and non-weekdays, the time-of-use electricity price of electric vehicle charging stations (EVCSs) and uses the M/M/s queuing theory model to determine the charging waiting time. Finally, a real traffic network is taken as an example to verify the practicability and effectiveness of this model.

An integrated thermal management strategy for cabin and battery heating in range-extended electric vehicles under low-temperature conditions

The integrated thermal management strategy under low-temperature conditions can effectively alleviate mileage anxiety associated with electric vehicles and improve the thermal comfort of the cabin. In this study, an integrated thermal management topology for range-extended electric vehicles is proposed, which recovers the waste heat from the range extender and the electric drive system for the heating of the battery and the cabin to improve energy utilization. Considering the driving modes and heating demands, the working modes and switching rules are designed. Especially, a control-oriented coupling system model is established for pure-electric mode. Taking cabin heat load demand, battery heat generation, and electric drive system heat generation as disturbances, a model predictive controller is designed for the heating power of the heater. An integrated thermal management system model verified by experiments is established in AMEsim. The initial temperature of the environment and vehicle is −20 °C, and the target temperatures of the cabin and the battery are set at 20 °C and 25 °C respectively. The feasibility of the strategy is verified by the co-simulation of Simulink and AMEsim under 4 WLTC cycles. The results show that in the range-extended mode, the integrated system shortens the battery heating time and reduces fuel consumption by more than 10.2 % compared with the independent system. In pure-electric mode, the heating speed of the proposed model predictive control strategy is slightly faster than that of the on–off strategy, the heating energy consumption is reduced by 20.95 %, and the total energy consumption for propulsion and thermal management decreased by 2.84 %.

Sandwich self-heating structure-based lithium-ion battery system and its application in the fuel cell bus for Beijing Winter Olympic Games

Hydrogen fuel cell vehicles (HFCVs), consisting of dual energy sources of fuel cell system (FCS) and lithium-ion battery system (LIBS), have become the technological frontier of electric vehicles due to the advantages of no mileage anxiety, fast hydrogen refueling, and zero emissions. However, the deterioration of battery performance at low temperatures makes it difficult to start the FCS and seriously hinders the application range of HFCVs. Herein, a sandwich self-heating structure-based LIBS is proposed to realize rapid self-heating and performance recovery of the LIBS without relying on external energy and achieve a fast cold start of the HFCV at extremely low temperatures. Furthermore, the proposed LIBS were successfully applied to all fuel cell buses in the core area of the 2022 Beijing Winter Olympic Games in batches. Firstly, compared with the mainstream heating methods, this sandwich self-heating structure has the advantages of fast heating speed (5.9 °C/min), high heating efficiency (78.75 %), and good temperature uniformity. More importantly, this structure has a small impact on the life and energy density of the LIBS. Secondly, in response to the severe road conditions in the Winter Olympic Games area, we matched the FCS and sandwich self-heating LIBS parameters based on multi-objective optimization and realized fast cold start and rapid performance improvement of fuel cell buses. Thirdly, the sandwich self-heating structure can realize the cold start of FCS with a smaller LIBS, which provides a new possibility to reduce the cost and light weight of HFCVs. Finally, the configuration is also applicable to pure electric vehicles, showing great application potential.

Review on Thermal Management of Lithium-Ion Batteries for Electric Vehicles: Advances, Challenges, and Outlook

Due to strict regulations and the requirement to reduce greenhouse gas emissions, electric vehicles (BEVs) are a promising mode of transportation. The lithium battery is the most important power source for an electric vehicle, but its performance and life are greatly restricted by temperature. To ensure the safety of automobile operation and alleviate mileage anxiety, it is urgent to understand the current situation and predict the development and challenge of battery thermal management system. This work reviews the existing thermal management research in five areas, including cooling and heating methods, modeling optimization, control methods, and thermal management system integration for lithium batteries. Battery thermal management types include air-based, liquid-based, PCM-based, heat-pipe-based, and direct cooling. Designing a better battery thermal management system not only needs to be optimized using algorithms on the model but also it uses intelligent algorithms for precise control to achieve safety and reduce energy consumption. This work also reviews the differences in thermal management systems between square and cylindrical batteries and summarizes the development trend of modularity in battery thermal management systems.

A state of health estimation framework based on real-world electric vehicles operating data

The accurate estimation of battery state of health (SOH) is essential to alleviate mileage anxiety for the drivers of electric vehicles (EVs) and achieve echelon use of the battery. The data collected in real-world reflects complex operating conditions, which make the study of real-world EVs possible. In this paper, a framework for the SOH estimation of real-world EVs considering parameters optimization is proposed. First, the battery's capacity is calculated utilizing the deep charging process, and the non-linear trend of capacity decay is described by Generalized Additive Models (GAM). Then, the internal and external health features characterizing battery aging are extracted from the EVs data. The quality of the health features is evaluated by correlation analysis, and the parameters of the health features are selected with Bayesian optimization. Finally, the PSO-ELM model for SOH estimation is established by optimizing the weights and biases of extreme learning machine (ELM) by particle swarm optimization (PSO). The experimental results show that the framework proposed is able to accurately estimate the SOH of the real-world vehicle battery pack with a maximum error of less than 0.4 %.

Recent Achievements on the Liquid Electrolytes for Fast‐Charging Lithium Metal Batteries

Lithium metal batteries (LMBs) with fast‐charging capabilities can ease mileage anxiety, which is essential for the popularization of electric vehicles. However, the uncontrollable growth of Li dendrites and the repeated formation of electrode electrolyte interfaces lead to safety risks and low coulombic efficiency, which hinder the practical application of LMBs. Liquid electrolytes play an extremely important role in solving these problems. This review focuses on the recent achievements of liquid electrolytes for fast‐charging LMBs. The main factors limiting the fast transportation of Li ions are first introduced. The recent progress in electrolyte regulation for fast‐charging LMBs, including Li salts, electrolyte additives, and novel organic solvents, is summarized. Finally, a general conclusion and perspectives are proposed.

Charging Behavior Analysis Based on Operation Data of Private BEV Customers in Beijing

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives.

Bi-level programming model approach for electric vehicle charging stations considering user charging costs

• A new bi-level planning model is proposed that integrates the interests of both charging station investors and EV users, where the upper model has the global economic return as the optimization objective, and the lower model has the EV user's satisfaction with the charging service (charging cost) as the optimization objective. The global economic cost is optimized while the service satisfaction of the user is improved. • The time cost, economic cost, and mileage anxiety are considered together to characterize the service satisfaction of EV users. Specifically, the charging queuing time, driving distance, desired charging volume, and actual charging volume are used to reflect the charging cost of EV users and to reflect the autonomy of EV users more accurately, and a combination of fast and slow charging posts are used for planning to meet the needs of different users and grid constraints, to improve the operational efficiency of charging stations and reduce the charging queuing time of EV users. • The conventional method and the bi-level planning method are solved separately for a region of Beijing, and the effectiveness of the proposed method is verified by comparing factors such as investors' returns, users' charging costs, and queuing times. The rapid development of electric vehicles (EV) has placed greater demands on the planning and construction of public electric vehicle charging stations (EVCS). As EV users are highly autonomous in their charging behavior, the interests of investors and EV users are mutually affected and challenging to balance. Therefore, this paper proposes a bi-level planning model to balance the interests of investors and EV users and optimize global economic costs while improving the service satisfaction of users. The upper-level model aims to optimize the economic cost. In contrast, the lower-level model aims to optimize the service satisfaction of EV users and characterizes the charging satisfaction of EV users through the costs of charging queuing time, distance traveled, desired to charge volume, and actual charging volume, to more accurately reflect the autonomy of EV users. A combination of fast and slow charging piles is also used for planning to meet the needs of different users and improve charging stations' operational efficiency. Finally, a case study is conducted in an area of Beijing to verify that the optimization model has the advantages of low global economic cost, short charging queuing time for users, and high service satisfaction.

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

Social Internet of Vehicles (SIoV) falls under the umbrella of social Internet of Things (IoT), where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology, which brings new opportunities and challenges, e.g., collaborative power trading can address the mileage anxiety of electric vehicles. However, it relies on a trusted central party for scheduling, which introduces performance bottlenecks and cannot be set up in a distributed network, in addition, the lack of transparency in state-of-the-art Vehicle-to-Vehicle (V2V) power trading schemes can introduce further trust issues. In this paper, we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center. Based on the game theory, we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare. We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching. The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Purpose This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.