World Electr. Veh. J., Volume 14, Issue 7 (July 2023) – 35 articles

Creating SoC algorithms for Li-ion batteries based on neural networks requires a large amount of training data, since it is necessary to test the batteries under different conditions so that the algorithm learns the relationship between the different inputs and the output. Obtaining such data through laboratory tests is costly and time consuming; therefore, in this article, a neural network has been trained with data generated synthetically using electrochemical models. These models allow us to obtain relevant data related to different conditions at a minimum cost over a short period of time. By means of the different training rounds carried out using these data, it has been studied how the different hyperparameters affect the behaviour of the algorithm, creating a robust and accurate algorithm. To adapt this approach to new battery references or chemistries, transfer learning techniques can be employed. Full article

An interior permanent magnet synchronous motor (IPMSM) is a kind of drive motor with high power density that is suitable for electric vehicles. In this paper, the dq-axis current reaction time of IPMSM was investigated in order to improve the reaction time of the electric vehicle. Firstly, the mathematical model of the current-loop decoupling of IPMSM was presented. Secondly, the controller design of dq-axis current-loop decoupling of IPMSM was investigated by the methods of proportional integral (PI) and internal model control PI (IMC-PI). Thirdly, based on the methods of PI and IMC-PI, the influence of the inverter switching frequency on the dq-axis current reaction time of IPMSM was analyzed and simulated, and it was found that the inverter switching frequency only had a significant influence on the parameters set of the PI controller. Lastly, compared with the PI method, the results of the simulation and hardware experiment demonstrate that the dq-axis current reaction time of IPMSM was improved by the IMC-PI method, and the IMC-PI method had the advantage of simple parameters setting and was not influenced by the inverter switching frequency. Full article

Electric vehicles (EVs) are gaining more and more traction as a viable option in the automotive sector. This mode of transportation is currently on track, according to current trends, to totally replace internal combustion engine (ICE) cars in the not-too-distant future. The economic system, the energy infrastructure, and the environment are just a few of the areas where electric vehicles could have a major impact. The transportation industry produces the second-most carbon dioxide gas from the combustion of fossil fuels, making it the second-highest contributor to global warming. A lot of people are looking to EVs, or electric vehicles, as a possible game-changing answer to this problem. Since an electric motor drives the electric vehicle’s propeller instead of an internal combustion engine, electric vehicles can reduce their carbon dioxide (CO₂) emissions compared to traditional automobiles. If coupled with renewable energy sources, EVs might theoretically become emission-free automobiles. In this paper, we will examine the various EV drive circuit types, including their construction and the benefits and drawbacks of employing each. This article discusses the current state of battery technology with an emphasis on EV batteries. This article discusses the best electric motor for EVs in terms of efficiency, power density, fault tolerance, dependability, cost, and more. Next, we conduct in-depth research into the difficulties and potential rewards of EV adoption in the future. While improvements in areas like charging times and battery performance are encouraging, government regulation of EVs remains a big non-technical barrier. Full article

The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance. Full article

The increase in demand for the fast charging of electric vehicles (EVs) has promoted research and the application of high-power direct current (DC) fast charging stations, and research on the stability control of charging stations with a parallel structure of multi-controllable rectifier modules is of great significance. Taking into account the dynamic and steady-state performance, the parameters of the dual-loop controller based on virtual impedance control are designed to realize the power sharing of multi-controllable rectifier modules. The constant power load model of EV charging at a constant current is established, and the load capacity of the cascaded system of converters is studied by using the impedance analysis method. The experimental and simulation results verify the effectiveness and correctness of the power sharing control strategy and the cascaded system stability analysis. Full article

With the increasing demand for electric vehicle (EV) charging, there are cases of complicated trading between EV users and charging operators or electrical utilities. This paper proposes a new trading model based on consortium blockchain. Firstly, mutual trust interconnected transaction networks and channels between charging operators are established. The PBFT consensus algorithm is used to verify EV charging transactions, and smart contracts are used to complete the process. In this model, charging station nodes of each company can verify transactions, the interconnection and independent management of charging transactions are realized, users’ charging methods are enriched, and the flexibility of charging services is improved. Finally, this paper uses hyperledger fabric to build an experimental environment. The feasibility of the above method is verified with an actual distribution scenario of some charging stations in Beijing. Full article

Innovation has always been the driving force behind social progress. Enterprises will adopt different types of technological innovations according to their goals, resources, and market strategies. The industry generally pays attention to the development and application of electric vehicle technology innovation, but a single method may not be able to fully explain the innovation of electric vehicle technology. Furthermore, the results of technological innovation must be presented in terms of market benefits. Otherwise, insufficient cash flow will lead to innovation interruption. Therefore, this study uses the innovation matrix proposed by Rothaermel to classify the matrix formed by the market and technology. This study collects 43 periodicals and special publications published in 2010–2022 and 40 related electric vehicle literature that can be downloaded, summarizes the literature content according to the innovation matrix using literature bibliometric perspective and analysis, and obtains (1) most of the innovative technologies of electric vehicles originated from the extension of previous technologies and (2) batteries and power supplements that are the key items of electric vehicles. The proportion of radical technological innovation is relatively high, and they are also the main factors of market sales. Theoretically, this study can provide a basis for studying the combination of Rothaermel’s “innovation matrix” and Ansoff’s “expansion matrix”. In practice, this is the first time the electric vehicle industry is taken as an example, combining the two models, aiming at technology/production/market/performance for electric vehicle industry managers, the technological innovation direction, and the formulation of market strategy operations and advanced deployment. Full article

Based on the analysis of the operating conditions of the tractor, a Hybrid four-wheel drive tractor is proposed, and formulate the torque distribution control strategy based on fuzzy control, to control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range of the tractor. The vehicle model of the Hybrid four-wheel drive tractor is established in AVL-CRUISE software, and the torque distribution control strategy based on fuzzy control is established in MATLAB/Simulink software. The AVL-CRUISE and MATLAB/Simulink co-simulation was carried out based on the plowing condition of the tractor. The simulation results show that the torque distribution control strategy based on fuzzy control can control the driving wheel slip rate of the Hybrid four-wheel drive tractor in the high traction efficiency operating range, the power performance of the Hybrid four-wheel drive tractor is improved, while the engine runs smoothly and is always in the high-efficiency range of engine operation, and the economy is better. Full article

In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To improve the adaptability of the distributed electric vehicle adaptive cruise control (ACC) strategy to complicated and volatile conditions, a multimode ACC strategy with emergency lane-changing function is proposed. Firstly, the ACC is divided into four modes aimed at the problem of complex conditions, and a switching strategy is designed to control the switching of them. Simultaneously, the car-following mode is divided in greater detail based on time to collision (TTC), and the acceleration weighted average algorithm is adopted for accuracy and output continuity during switching. Then, the ACC is established with a hierarchical control framework, in which a PID-based cruise mode and a multi-objective optimized car-following mode based on model predictive control (MPC) are devised. The target brake wheel cylinder pressure is selected as the emergency brake pressure in takeover mode. In addition to the MPC-based system, the emergency lane-changing mode incorporates a yaw moment controller in the upper-level controller to improve body stability during emergency lane changing in the upper-level controller. In the lower-level controller, the upper-level output is converted into driving torque, wheel cylinder pressure, and front wheel angle to control vehicle travel and generate additional yaw moment. Finally, the results indicate that the presented multimode switching strategy can adapt to complex and instable transportation environments. In the cruise control scenario, the host vehicle can rapidly reach cruising speed within 5 s. In the car-following scenario, the host vehicle can stably follow the preceding vehicle with an acceleration of −5–3.5 m/s² and a jerk of −2–2 m/s³ throughout the entire process, maintaining a safe distance from the preceding vehicle. In emergency lane-changing scenarios, vehicles with body stability control can better follow the lane-changing trajectory, and tracking accuracy is improved by 65%. Simultaneously, parameters such as front wheel angle, yaw rate, sideslip rate, and lateral acceleration remain within the normal range. In mixed switching scenarios, each mode can be correctly switched according to diverse operating conditions, and obstacle avoidance can be accomplished through horizontal and vertical strategies, which also verify the effectiveness and rationality of the control strategy proposed. Full article

The state of health (SOH) of a lithium ion battery is critical to the safe operation of such batteries in electric vehicles (EVs). However, the regeneration phenomenon of battery capacity has a significant impact on the accuracy of SOH estimation. To overcome this difficulty, in this paper we propose a method for estimating battery SOH based on incremental energy analysis (IEA) and bidirectional long short-term memory (BiLSTM). First, the IE curve that effectively describes the complex chemical characteristics of the battery is obtained according to the energy data calculated from the constant current (CC) charging phase. Then, the relationship between the IE curve and battery SOH degradation characteristics is analyzed and the peak height of the IE curve is extracted as the aging characteristic of the battery. Further, Pearson correlation analysis is utilized to determine the linear correlation between the proposed aging characteristics and the battery SOH. Finally, BiLSTM is employed to capture the underlying mapping relationship between peak characteristics and SOH, and a battery SOH estimation model is developed. The results demonstrate that the proposed method is able to estimate battery SOH under two different charging conditions with a root mean square error less than 0.5% and coefficient of determination above 98%. Additionally, the method is combined with Pearson correlation analysis to select an aging characteristic with high correlation, reducing the required data input and computational burden. Full article

The ability of advanced driver assistance systems (ADAS) and autonomous vehicles to make human-like decisions can be enhanced by providing more detailed information about vehicles and in-vehicle users’ states. In this paper, the driving status domains of vehicles in left turn across path/opposite direction (LTAP/OD) scenarios are subdivided into comfort, discomfort, extreme, and crash, and the boundaries of each status domain are quantified and visualized. First, real unprotected left turn road segments are chosen for the actual vehicle testing. Subjective passenger comfort evaluation results and objective motion state data of vehicles during the experiment are organized and analyzed by statistics. In addition, the pictorials are plotted to determine the comfort and extreme status domain boundaries based on motion state parameters. Second, based on the unprotected left turn kinematic analysis and modeling, as well as a skilled driver risk perception and operational model, the Safe Collision Plots (SCP) of conflicting vehicles in LTAP/OD scenarios are quantified and expressed as pictorial examples. By combining objective motion parameters and passenger experience, intuitively quantifying each driving status domain of vehicles can provide more fine-grained information for the design parameters of ADAS and autonomous vehicles and increase public trust and acceptance of them. Full article

Mobility as a Service (MaaS) and, more recently, electric Mobility as a Service (eMaaS) have increasingly been put forward to meet the economic, social, and environmental challenges linked to mobility. First, however, monitoring and evaluating such a scheme’s performance is crucial, mainly through the definition of appropriate indicators. In this study, a standardised methodological approach is presented for the assessment of an eMaaS scheme. In addition, this methodological approach contains a range of innovative Key Performance Indicators (KPIs). The proposed KPIs refer to the evaluation of the scheme based on four pillars: (a) society, (b) users, (c) operators, and (d) internal operation. The methodology for evaluating the proposed KPIs includes identifying the available sources for data collection. For example, data can be collected through questionnaire surveys, focus group discussions, and the system’s central dashboard. An appropriate set of indicators to evaluate a system from various perspectives is necessary to assess an eMaaS scheme in real-life conditions. Furthermore, the evaluation of the overall operation of the scheme will contribute to drawing valid conclusions (e.g., user acceptance, economic viability) for the implementation of eMaaS in urban areas. Full article

A battery cell equalisation system for automotive applications based on a supercapacitors energy storage SCES tank is proposed. The main advantages of the developed system are the utilisation of the regenerative brake energy for battery cell equalisation, reduction in the number of DC–DC converters, the flexible operation expressed by the possibility to address each battery cell with bi-directional switches, and acceptable efficiency in all modes of operation. The energy transfer between the SCES and battery cells is precisely analysed with modelling and simulations in steady-state and transient conditions. Power loss is estimated per sub-system, systemising the loss reduction techniques and achieving the maximum efficiency. The required DC–DC converters are described and designed according to the specific modes of operation in the developed application. Finally, the experimental verification is provided using a small physical model. Full article

In-wheel motors for new energy vehicles are close to the brake, which results in a high ambient temperature. Thus, there is a high demand for cooling systems. This paper designs an oil-spray-cooled system based on the flat structural characteristics of an in-wheel motor. A computational fluid dynamics method with a two-phase volume-of-fluid model is applied to simulate the transient process of oil spraying from nozzles onto the stator carrier and then dripping to the end windings. The spatially distributed fluid interfaces with location and shape fidelity are derived. Considering the big difference of thermal inertia between the motor solid and oil fluid, the mixed timescale method is applied to calculate the temperature fields of the fluid and solid. Finally, a prototype is fabricated and tested to verify the proposed oil-cooling system and simulation method. Full article

Electric vehicles (EVs) enable the integration of powertrains with multiple motors, allowing for the adjustment of torque delivered to each wheel. This approach permits the implementation of torque vectoring techniques (TV) to enhance the vehicle’s stability and cornering response, providing better control of yaw moments. This study utilizes comprehensive telemetry data and an advanced simulator model to assess the influence of torque vectoring (TV) on a Formula SAE (Society of Automotive Engineers) competition vehicle. The telemetry data were collected from a fully instrumented 2WD car, which was then employed to calibrate the simulation model. The calibrated model was subsequently utilized to predict the performance enhancements that could be achieved by implementing a 4WD system. The methodology proved to be a valuable contribution to vehicle design development. This approach also helps evaluate the potential advantages of torque vectoring for drivers with limited experience. Full article

This paper proposes a model for the electromagnetic performance of the dual air-gap liquid-cooled eddy current retarder (DAL-ECR) considering the transient permeability. First, the structure and working principle of the DAL-ECR are introduced. Next, the analysis model of the static air-gap flux density considering the flux leakage and end effect is established based on the piecewise function method and the magnetic equivalent circuit (MEC). Then, based on the skin effect of the electromagnetic field in the retarder, an iterative method for solving the transient permeability of the stator is proposed. According to Faraday’s and Ampere’s laws, the analysis model of the static air-gap flux density, and the transient permeability, the analysis model of the transient air-gap flux density is established. The braking torque of the DAL-ECR is then calculated while taking the actual path of the eddy current and the skin effect on the permeability of the stator into consideration. Finally, the calculation accuracy of the model was verified by the finite element method (FEM) and the bench test. Full article

In recent years, modular multilevel converters (MMCs) have been increasingly used in the field of electric vehicle charging and discharging due to their unique performance advantages. However, the unique cascade structure of MMCs raises the problem of the circulating current. Due to chemical processes, aging effects, etc., the capacitance parameters of the upper and lower bridge arms will be asymmetric, which will introduce odd harmonics into the circulating current, increasing system losses and threatening the system reliability. To address this phenomenon, this paper proposes a circulating current suppression strategy with additional virtual impedance (VI) based on a repetitive controller (RC). The corresponding simulation models were built for the comparative study of different circulating current suppression strategies. The results show that the VI-RC circulating current suppression strategy can significantly reduce the odd and even harmonics in the circulating current under asymmetric conditions, and the total harmonic distortion (THD) of the bridge arm current is only 0.98%, which verifies the effectiveness of the proposed strategy. Full article

This study proposes a novel approach for predicting the State of Health (SoH) and Remaining Useful Life (RUL) of lithium-ion batteries. The low accuracy of SoH and RUL is due to the challenges of establishing effective feature engineering for battery attributes. To address this issue, a SoH and RUL prediction model based on curve compression and CatBoost is proposed. Firstly, an improved threshold selection method based on curvature analysis is introduced to enhance the compression performance of battery attributes under different cycles. Secondly, to ensure that the extracted feature sequences have the same length, spline interpolation and local anomaly factor detection techniques are utilized to fill or eliminate feature points for feature length normalization. Finally, a dynamic time regularization algorithm is applied to calculate the shortest distance between the feature sequence and the original curve to determine the optimal feature length for input into the CatBoost prediction model. The experimental results demonstrate that the proposed approach outperforms other prediction models in the research object dataset, achieving $R^2$ values higher than 0.98 and MSE values around 1 × 10⁻⁵. The proposed approach also achieves better prediction results in the validation object dataset, indicating its strong generalization capability. Additionally, the proposed model shows significant robustness by accurately predicting SoH and RUL under noisy environmental conditions. Overall, the proposed model shows significant potential to accurately predict SoH and RUL by efficiently addressing the challenges associated with feature engineering for battery attributes, reducing the impact of background noise on prediction results, and exhibiting strong robustness. Full article

Autonomous mini-buses are low-cost passenger vehicles that travel along designated routes in industrial parks. In order to achieve this goal, it is necessary to implement functionalities such as lane-keeping and obstacle avoidance. To address the challenge of deploying deep learning algorithms to detect environmental information on low-performance computing units, which leads to difficulties in model deployment and the inability to meet real-time requirements, a lightweight algorithm called YOLOP-E based on the YOLOP algorithm is proposed. (The letter ‘E’ stands for EfficientNetV2, and YOLOP-E represents the optimization of the entire algorithm by replacing the backbone of the original model with EfficientNetV2.) The algorithm has been optimized and improved in terms of the following three aspects: Firstly, the YOLOP backbone network is reconstructed using the lightweight backbone network EfficientNet-V2, and depth-wise separable convolutions are used instead of regular convolutions. Secondly, a hybrid attention mechanism called CABM is employed to enhance the model’s feature-representation capability. Finally, the Focal EIoU and Smoothed Cross-Entropy loss functions are utilized to improve detection accuracy. YOLOP-E is the final result after the aforementioned optimizations are completed. Experimental results demonstrate that on the BDD100K dataset, the optimized algorithm achieves a 3.5% increase in mAP50 and a 4.1% increase in mIoU. During real-world vehicle testing, the detection rate reaches 41.6 FPS, achieving the visual perception requirements of the autonomous shuttle bus while maintaining a lightweight design and improving detection accuracy. Full article

We address the value of engaging stakeholders in energy and mobility transitions by using models. As a communication medium, models can facilitate the collaborative exploration of a future between modeling researchers and stakeholders. Developing models to engage stakeholders requires an understanding of state-of-the-art models and the usability of models from the stakeholder perspective. We employ mixed methods in our research. We present the overview of models that have been proposed to make sense of the transitions in the scientific literature through a systematic literature mapping (n = 105). We interviewed 10 stakeholders based in The Netherlands to elaborate on use cases in which models can benefit stakeholders in practice and the characteristics of usable models. We conclude our research by elaborating on two challenges of model design that modeling research can consider to engage stakeholders. First, we argue that understanding the epistemic requirements of both modeling researchers and stakeholders that models can simultaneously meet is crucial (e.g., questions addressed using models and assumptions). Second, we seek technical solutions for producing models in a time-wise manner and developing interfaces that allow models distant in formalism and represented phenomena to communicate in tandem. Our research creates awareness of the model design aspect by considering its usability. Full article

In order to solve the problem of vehicle stability control after a single-wheel brake failure in the brake-by-wire system, a control strategy of braking force redistribution with a yaw moment is proposed to ensure the braking efficiency and stability of vehicles. In this strategy, a two-layer architecture is adopted. In the upper layer control, a fault factor is introduced to represent the real-time failure degree of the wheel, and the driver’s braking intention is perceived through the pedal travel and pedal speed of the driver. The braking force redistribution algorithm of the remaining three wheels is designed based on the wheel failure degree and braking intensity. In the lower control, according to the state parameters of the vehicle, the additional yaw moment, which controls the yaw rate and the sideslip angle of the vehicle, is calculated by using the sliding mode control theory, and the yaw moment is reasonably allocated to the normal wheel. By using MATLAB/Simulink and Carsim co-simulation, different braking strength and failure types are selected for simulation analysis. The simulation results show that the proposed control strategy can improve the braking efficiency and stability of the vehicle under different braking conditions. Full article

Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because of the positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. This review offers a thorough evaluation of the existing body of information on the topic of electric vehicles’ (EVs’) future interactions with smart grids. The combination of the potential deployment of EVs and the smart grid’s conceptual goal presents challenges for electric grid-related infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart-charging systems are also fully examined. A quickly growing percentage of distributed energy is derived from wind and solar (photovoltaic) energy. The variable power output of wind and solar energy introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we performed a comprehensive review of the literature to learn more about this exciting research gap that needs to be filled and to identify recently developed solutions to the problems related to EVs. Additionally, in this review article, we take a close look at the practicality of V2G technology. The smart grid is a developing concept that will likely have large implications for the world’s energy infrastructure, and this study thoroughly analyzes how EVs interact with it. Full article

Aiming at the problem that, under certain extreme conditions, relying on tire force or tire angular velocity to represent the longitudinal velocity of the unmanned vehicle will fail, this paper proposes a longitudinal velocity estimation method that fuses LiDAR and inertial measurement unit (IMU). First, in order to improve the accuracy of LiDAR odometry, IMU information is introduced in the process of eliminating point cloud motion distortion. Then, the statistical characteristic of the system noise is tracked by an adaptive noise estimator, which reduces the model error and suppresses the filtering divergence, thereby improving the robustness and filtering accuracy of the algorithm. Next, in order to further improve the estimation accuracy of longitudinal velocity, time-series analysis is used to predict longitudinal acceleration, which improves the accuracy of the prediction step in the unscented Kalman filter (UKF). Finally, the feasibility of the estimation method is verified by simulation experiments and real-vehicle experiments. In the simulation experiments, medium- and high-velocity conditions are tested. In high-velocity conditions (0–30 m/s), the average error is 1.573 m/s; in the experiment, the average error is 0.113 m/s. Full article

In this contribution, a tool developed for the study of the efficiency maps of shifted inductances axes permanent magnet synchronous motors (SIAPMSMs) is presented and made available to the readers. The research builds upon a shared foundation that has already been established in previous works focused on the power capability of synchronous machines. This contribution is an extension of previous works dedicated to the power capabilities of shifted inductances axes synchronous motors where the losses were not considered. In this new contribution, the SIAPMSM models, which include the electromagnetic losses (joule and iron losses), are discussed. The mechanical losses are not included. The classical permanent magnet synchronous machines can be considered a particular case of a SIAPMSM. They will be used to validate the developed tools. The validation study is conducted by comparing the power capabilities and the efficiency maps of SIAPMSMs and classical PMSMs obtained by the newly developed modeling tool and a previously developed modeling tool. Full article

A modular approach to the construction of electric machines, drive systems, power supply systems is a new direction of modern technology development. Especially, the modular approach is promising for electric vehicles due to such positive aspects as increased efficiency, fault tolerance, overall reliability, safety, enhanced control capabilities, etc. In this work, the modular approach is comprehensively applied to an EV powertrain system, which includes a dual three-phase (DTP) BLDC motor with two machine modules of an asymmetric configuration, two battery modules and a supercapacitor module (SCM). The proposed H–H configuration of modular EV powertrain system includes four voltage source inverters that combine the power modules with the open ends of the windings (OEW) of the module machine armature, and provide control of their operation. Based on the developed mode system of the OEW machine module operation for EV traction and braking, a general control algorithm for the proposed configuration of the modular EV powertrain system has been developed. It combines the control of the operating modes with the functions of maintaining the required SOC level of the SCM and equalizing the SOCs of the two battery modules. The conducted simulation and experimental studies confirmed the workability and effectiveness of the proposed solutions. Full article

A ramp merging decision as an important part of the lane change model plays a crucial role in the efficiency and safety of the entire merging process. However, due to the inevitability of on-ramp merging, the limitations of the road environment, and the conflict between the merging vehicle and the following vehicle on the main road, it is difficult for human drivers to make optimal decisions in complex merging scenarios. First, based on the NGSIM dataset, a gain function is designed to represent the interaction between the ego vehicle (EV) and the surrounding vehicles, and the gain value is then used as one of the characteristic parameters. The K-means algorithm is employed to conduct a cluster analysis of the driving style under the condition of changing lanes. This paper models the interaction and conflict between the ego vehicle (vehicle merging) and the mainline lagging vehicle as a complete information non-cooperative game process. Further, various driving styles are coupled in the ramp decision model to mimic the different safety and travel efficiency preferences of human drivers. After EV decision-making, a quintic polynomial method with multi-constraints is proposed to implement merging trajectory planning. The proposed algorithm is tested and analyzed in an on-ramp scenario, and the results demonstrate that drivers with different driving styles can make correct decisions and complete the ramp merging. The changing trend of the speed and trajectory tests are also in line with the features of the driver’s driving style, offering a theoretical foundation for individualized on-ramp merging decisions. Full article

The dual-carbon strategy advocates a green, environmentally friendly, and low-carbon lifestyle. In the field of transportation, electric vehicles (EVs) have been regarded as an effective solution to reduce carbon emissions and to conserve energy. Developing a reasonable charging guidance scheme for users is a feasible way to solve problems, such as the range anxiety of EV users, and has a great application value for the promotion of EVs in the future. In practical situations, how to develop charging induction schemes for users that better meet their needs according to the type of user and their multi-dimensional preferences is the focus of this paper. To this end, this study utilized charging behavioral data to investigate the multi-dimensional charging preference of users based on the collaborative filtering algorithm. Then, a multi-objective optimization model was established based on the preference degree of each charging station and the integrated travel cost. An NSGA-III framework was used to design the algorithm to solve the proposed model. The algorithm was tested using simulation experiments that were designed based on the road network and charging stations in Beijing. The final result is an experimental analysis of the weight matrices for the three different preferences of minimum energy consumption cost, minimum time cost, and minimum fee cost, which yields a difference of about 4.4% between the optimal energy consumption cost and the maximum energy cost, about 2.9% between the optimal time cost and the maximum time cost, and about 10% between the optimal fee cost and the maximum fee cost under these three different preferences, respectively. The proposed multi-objective optimization model is able to provide users with reliable charging station selection by incorporating their personalized charging preference characteristics and charge guidance schemes. Full article

Developing electric vehicle (EV) energy storage technology is a strategic position from which the automotive industry can achieve low-carbon growth, thereby promoting the green transformation of the energy industry in China. This paper will reveal the opportunities, challenges, and strategies in relation to developing EV energy storage. First, this paper clarifies the strategic value and potential of developing EV energy storage under the carbon neutrality goal. Second, this paper demonstrates strategic opportunities and challenges during the development. Third, this paper proposes methods for creating a good market environment and business models. Finally, this paper suggests that relevant policies and regulations should be formulated and charts the course of technology development. The results show that EV energy storage technology has potential in terms of technology, the scale of development, and the user economy. The proposal of the carbon neutrality goal, the increasing market share of EVs, lower-cost and higher-efficiency batteries, etc., have all further accelerated the development of EV energy storage. The EV energy storage field should focus on developing battery technology, make advancements toward delivering longer cycle lives and improving the safety and availability of battery materials, and ramp up the R&D efforts with respect to developing vehicle-to-grid (V2G) management technologies. Simultaneously, it is necessary to create a business ecosystem centered on V2G operating platforms, constituting a process to which various players can contribute and achieve mutually beneficial results. It is also essential to formulate top-level strategic plans across industries and organizations, develop an electricity-trading mechanism as soon as possible, and promote the implementation of technical standards related to EV energy storage. Full article

The temperature of an electric vehicle battery system influences its performance and usage life. In order to prolong the lifecycle of power batteries and improve the safety of electric vehicles, this paper designs a liquid cooling and heating device for the battery package. On the device designed, we carry out liquid cooling experiments and preheating experiments. Then, a three-dimensional numerical model for the battery package is built, and its effectiveness is validated by comparing the simulation results with the experimental outcomes in terms of battery surface temperature and temperature difference. Furthermore, we investigate the influences of the liquid flow rate and the inlet temperature on the maximum temperature and temperature difference of batteries by the cooling models and preheating models. Results show that: at the cooling stage, it is able to keep each battery working at an optimal temperature under different discharge conditions by changing the flow and the inlet temperature of liquid; at the heating stage, large flow rates and high inlet temperatures are able to speed up the preheating process, thereby saving time of the drivers. Full article

Models play a crucial role in explaining internal processes, estimating states, and managing lithium-ion batteries. Electrochemical models can effectively illustrate the battery’s mechanism; however, their complexity renders them unsuitable for onboard use in electric vehicles. On the other hand, equivalent circuit models (ECMs) utilize a simple set of circuit elements to simulate voltage–current characteristics. This approach is less complex and easier to implement. However, most ECMs do not currently account for the nonlinear impact of operating conditions on battery impedance, making it difficult to obtain accurate wideband impedance characteristics of the battery when used in online applications. This article delves into the intrinsic mechanism of batteries and discusses the influence of nonstationary conditions on impedance. An ECM designed for non-steady state conditions is presented. Online adaptive adjustment of model parameters is achieved using the forgetting factor recursive least squares (FFRLS) algorithm and varied parameters approach (VPA) algorithm. Experimental results demonstrate the impressive performance of the model and parameter identification method, enabling the accurate acquisition of online impedance. Full article