Zheng Chen

Kunming University of Science and Technology | KUST · Faculty of Transportation Engineering

This paper proposes a predictive energy management strategy of FC PHEV based on PMP and co-state boundaries. The model predictive control (MPC) problem is established and transformed as a two-point-boundary-value one by PMP theory, and the physical constraints of FC power, FC power varying rate, and battery current, are merged by methodical derivat...

Knowledge of temperature state of lithium-ion battery is essential to ensure its safe operation and mitigate the mileage anxiety of electric vehicle. For accurately acquiring the temperature state of lithium-ion batteries, this study develops a state of temperature (SOT) estimation framework, which accounts for multiple places temperature estimatio...

Intelligent plug-in hybrid electric vehicles (IPHEVs), with advanced environmental sensing and integrated communication capabilities, present tremendous autonomy in drive decision-making and high efficiency in energy economy. In this context, the key control of IPHEVs gradually evolves from energy management among the key components of powertrain t...

Driving style poses significant impacts on eco-driving performance of vehicles, especially for those with hybrid powertrains. By incorporating driving style recognition, an efficient velocity planning and energy management strategy is developed for a platoon of intelligent connected plug-in hybrid electric vehicles (PHEVs). Firstly, a high-fidelity...

Existing monocular depth estimation driving datasets are limited in the number of images and the diversity of driving conditions. The images of datasets are commonly in a low resolution and the depth maps are sparse. To overcome these limitations, we produce a Synthetic Digital City Dataset (SDCD) which was collected under 6 different weather drivi...

For saving fuel and extending the fuel cells (FC)/battery lifetime, this paper proposes a real-time cost-optimal predictive energy management strategy of FC hybrid electric vehicles based on continuation/general minimal residuals (C/GMRES) algorithm. To ensure the preferable continuation for algorithm application, the continuity method is proposed...

Accurate state of temperature estimation for lithium-ion batteries is essential to ensure its safe and efficient operations. In this study, a state of temperature estimation framework for cylindrical lithium-ion batteries is proposed based on square root cubature Kalman filter algorithm, wherein the internal and surface temperatures of power batter...

Nowadays, health diagnosis for lithium-ion batteries is critical to ensure their normal and safe operations. However, precise estimation of battery capacity is a challenging task, especially under complex and varying operation conditions. To tackle this problem, we propose an automatic feature extraction technique that utilizes random fragmented ch...

Electrification and intelligence dominate the developing trend of automobiles. Except the tremendous progress in electrification, autonomous driving is widely investigated with the advancement of perception and communication technologies. While, proper path planning and rigorous tracking control in autonomous driving remain challenging, especially...

With the development of communication and automation technologies, the great energy-saving potential of connected and automated vehicles (CAVs) has gradually been highlighted. By means of interactions with surrounding vehicles and infrastructure, CAVs can automatically plan ecological driving behaviours to significantly reduce energy consumption, w...

In order to expedite battery development cycles and optimize design, electrochemical model is widely employed to investigate battery performance. However, while these models have been proven efficient in reducing experimental costs, most of them do not sufficiently account for capacity degradation throughout the entire life cycle of the battery. To...

Online diagnosis of abnormal temperature is vital to ensure the reliability and operation safety of lithium-ion batteries, and this study develops a hybrid neural network and fault threshold optimization algorithm for their online surface temperature prediction and abnormal diagnosis. To be specific, a hybrid neural network incorporating convolutio...

The advancement of electromechanical coupling technologies has fostered the development of four-wheel-drive plug-in hybrid electric vehicles (4WD PHEVs) with multiple power sources, potentially enhancing travel efficiency. Moreover, sophisticated energy management strategies (EMSs) not only augment the efficacy of energy-saving control but also ens...

Considerable evidence suggests that the decline in physiological abilities prevalent in older drivers leads to a reduction in the visual and psychomotor functions required for safe driving. The purpose of this study is to further investigate the differences in driving behavior between older and younger drivers and to describe the change process of...

Accurate state of health (SOH) knowledge is critical for reliable operations of lithium-ion batteries. However, short-term random charging operations of lithium-ion batteries are not conducive to reliable SOH estimation. To solve it, a charging voltage prediction and machine learning based estimation are employed to supply precise estimation. First...

Eco-driving control poses great energy-saving potential at multiple signalized intersection scenarios. However, traffic uncertainties can often lead to errors in ecological velocity planning and result in increased energy consumption. This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersectio...

To address the problems of the random and incomplete charging process of the vehicle-mounted lithium-ion batteries, this paper proposes a machine learning method that can realize state of health (SOH) estimation under random charging conditions. Firstly, the complete voltage curve prediction in the constant-current (CC) charging phase under the sho...

Precise state of health (SOH) estimation is pivotal for reliable operations of lithium-ion batteries in electric vehicles. However, the collected charging data is usually incomplete, which makes it difficult to generate health features and brings great challenges to SOH estimation. To conquer this defect, Gaussian process regression (GPR) is develo...

Efficient and accurate prediction of battery remaining capacity can guarantee the safety and reliability of electric vehicles (EVs). However, battery capacity is difficult to measure directly due to complex application scenarios and sophisticated internal physicochemical reactions. This study develops a hybrid deep learning approach for accurate re...

Transportation is one of the critical factors that leads to energy crisis, and over the years there has been a lot of research around the improvement of vehicle energy efficiency. To effectively reduce the overall energy consumption of continuous traffic flow in a road network, a collaborative eco-routing optimization strategy is proposed. The stra...

Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco-driving studies mainly focus on optimal control of single vehicle at individual signalized intersection, while rarely involving the cooperative optimization of a group...

Accurate estimation of state of health (SOH) is critical for safe and efficient operation of lithium-ion batteries in electric transport tools. However, the random charge/discharge behaviors complicate online SOH estimation and discount estimation accuracy. To overcome this difficulty, this study presents an ensemble learning and voltage reconstruc...

Facilitated by the advanced abilities in environment sensing and integrated communication, intelligent plug-in hybrid electric vehicles (IPHEVs) enable massive autonomy in decision-making. The evolution towards intelligence imposes stringent demand on optimal control in IPHEVs, of which the velocity planning and energy management is strongly couple...

Summery The worldwide penetration of electric bicycles has caused numerous charging accidents; however, online diagnosing charging faults remains challenging, due to non-standard chargers, non-uniform communication manners and inaccessible battery inner status. The development of Internet of Things enables to acquire the input current information o...

Plug-in hybrid electric vehicles (PHEVs) have been validated as a preferable solution to transportation due to its great advantages in fuel economy promotion, harmful emission reduction and mileage anxiety mitigation. While, designing an effective energy management strategy to allocate the power between battery and engine is critical to improve the...

Accurate state of health estimation of lithium-ion batteries is imperative for reliable and safe operations of electric vehicles. This study presents a hybrid attention and deep learning method for state of health prediction of lithium-ion batteries. First, the temperature difference curves are calculated from the charging data and subsequently smo...

Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. In this study, a real-time hierarchical effective and efficient co-optimization control strategy is designed...

Eco-driving control generates significant energy-saving potential in car-following scenarios. However, the influence of preceding vehicle may impose unnecessary velocity waves and deteriorate fuel economy. In this research, a learning-based method is exploited to achieve satisfied fuel economy for connected plug-in hybrid electric vehicles (PHEVs)...

An effective energy management strategy (EMS) in hybrid electric vehicles (HEVs) is indispensable to promote consumption efficiency due to time-varying load conditions. Currently, learning based algorithms have been widely applied in energy controlling performance of HEVs. However, the enormous computation intensity, massive data training and rigid...

Accurate capacity estimation of lithium-ion batteries is of great significance to guarantee their reliability and safety operation. In this paper, a fused capacity estimation method is devised via the co-operation of multi-machine learning algorithms. First, the peak value of incremental capacity curve is extracted as the health feature, and the su...

The widespread penetration of electric bicycles (E-bicycles) raises numerous charging safety concerns; however, online diagnosis of charging safety for E-bicycles remains challenging due to the limited data and involvement of multiple factors, such as battery, charger, charging mode and user behavior. To overcome this difficulty and promote chargin...

The widespread adoption of electric public buses has a positive effect on energy conservation and emission reduction, which is significant for sustainable development. This study aims to assess the safety and economy of electric buses based on drivers’ behavior. To this end, based on the remotely acquired travel data of buses, the driving operation...

To overcome the difficulty of real-time optimization during shifting process for dual clutch transmission, a clutch optimal torque prediction method based on Support Vector Regression is proposed. Firstly, a shifting dynamic model of dual clutch transmission system is established. Afterwards, the maximum jerk, friction work and shifting time are we...

In this paper, a hierarchical energy management control strategy is investigated for autonomous plug-in hybrid electric vehicle in vehicle-following environment. With the target of safety and comfort, the designed algorithm is divided into two layers. The grey neural network is leveraged in the upper layer controller to predict the future speed tre...

Vehicle launching has an important influence on driving performance of the vehicle. For vehicles with dual clutch transmissions (DCT), the clutch torque control is the key to the launching control. Therefore, a data-driven control method for DCT launching process based on adaptive neural fuzzy inference system (ANFIS) is proposed. Firstly, the vehi...

This paper presents a coordinated control strategy for braking and shifting of electric vehicles that are equipped with a two-speed automatic transmission. The coordinated control strategy mainly includes braking force distribution and synthetic shifting regulation for braking operations. Firstly, the vehicle model is established for investigation...

Accurate estimates of State of Health (SoH) are critical for characterizing the aging of lithium-ion batteries. This protocol combines feature extraction and a representative machine learning algorithm (i.e., least-squares support vector machine) for SoH prediction of lithium-ion batteries. We detail the step-by-step estimation process, followed by...

Intersections have attracted wide attention owing to their complexity and high rate of traffic accidents. In the process of developing L3-and-above autonomous-driving techniques, it is necessary to solve problems in autonomous driving decisions and control at intersections. In this article, a decision-and-control method based on reinforcement learn...

Fuel cell hybrid vehicles (FCHVs) offer great opportunities to reduce vehicle's operation cost and mitigate environmental impact. However, high-quality real-time energy management of FCHV is a difficult task due to different influences from complex traffic environments, such as dynamic changes of preceding and rear vehicle state, road slope and roa...

This study develops a combined method for co-estimation framework for state of charge and capacity of lithium-ion batteries considering wide temperature scope. In this framework, a second-order equivalent circuit model incorporating temperature compensation is established to characterize the battery's electrical performance. Next, the particle swar...

Energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is the key to improve the energy utilization efficiency and vehicle fuel economy. In this paper, a model predictive control (MPC) based EMS coupled with double Q-learning (DQL) is presented to allocate the power between multiple power sources for PHEV. Firstly, the powertra...

Advances in machine learning inspire novel solutions for the validation of complex vehicle models, and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel drive passenger vehicle configuration, is validated...

A simplified model based on the novel full homogenized macro-scale model (FHM) is proposed to reduce the computational time of the FHM model with trivial loss of fidelity. The simplified FHM model is compared with a simplified model based on the pseudo-two-dimensional (P2D) model. The FHM model is based on the homoge-nization theory, while the volu...

Accurate estimation of state of charge (SOC) is crucial for operation performance promotion of lithium-ion batteries. However, the variations of temperature and loading current directly impact the estimation accuracy of SOC. To fully account for these influences, this study proposes a hybrid compensation model and exploits an advanced algorithm for...

Data-driven methods have been widely employed for capacity estimation of lithium-ion batteries through exploiting machine learning models to build a mapping relationship between extracted health features and capacity. However, existing machine learning based approaches require plentiful and intricate data processing for feature extraction. To remed...

Rapid progress has been gained in the field of advanced communication technologies, which also promote parallel developments in the Internet of Vehicles (IoVs). In this context, vehicle-environment cooperative control can be integrated into next-generation vehicles to further improve the vehicles performance, in particular energy efficiency. Accura...

Accurate state of health estimation for lithium-ion batteries is crucial to ensure the safety and reliability of electric vehicles. This study presents an accurate state of health estimation method based on temperature prediction and gated recurrent unit neural network. First, the extreme learning machine method is leveraged to forecast the entire...

With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of dr...

Accurate and reliable estimation of battery capacity can help prevent overuse and ensure operation safety; and on this basis, the remaining useful life (RUL) prediction can supply the guidance for maintenance and replacement battery systems. In this paper, a synchronous estimation method for capacity and RUL is proposed based on the improved dual l...

Instantaneous application optimality is one of the indispensable indicators to assess energy management performance of plug-in hybrid electric vehicles (PHEVs). The momentary optimality, nevertheless, cannot be flexibly reachable under various driving environments due to the partial unobservabilities in control algorithms. To cope with it, a novel...

For plug-in hybrid electric vehicles, the equivalent consumption minimum strategy is typically regarded as a battery state of charge reference tracking method. Thus, the corresponding control performance is strongly dependent on the quality of state of charge reference generation. This paper proposes an intelligent equivalent consumption minimum st...

Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed towards precise and reliable SOH prediction method based on machine learning (ML) techniqu...

Advances in machine learning inspire novel solutions for the validation of complex vehicle models, and spur an easy manner to promote energy management performance of complexly configured vehicles, such as plug-in hybrid electric vehicles (PHEVs). A constructed PHEV model, based on the four-wheel drive passenger vehicle configuration, is validated...

Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is a...

Hybrid electric vehicles (HEVs), as a promising solution to mitigate environmental pollution and reduce fuel consumption, employ a combination of fuel and electric power as power supply for boosting the vehicle's fuel economy. Comparing to conventional internal combustion engine (ICE) driven vehicles, the additional propulsion power source in elect...

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic lane changing based on some certain assumptions, such as the lane-changing speed is constant or the terminal state is known in...

Two-speed or multiple-speed automatic transmissions can obviously improve the overall manipulating performance in terms of shifting quality and energy efficiency when equipped in electric vehicles (EVs). This study details the design of a two-speed clutch-less automatic transmission (2AT) for EVs and the motor controlled shifting mechanism. Firstly...

With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal...

The application of machine learning-based state of health (SOH) prediction is hindered by large demand of training data. To conquer this defect, a flexible and easy transferred SOH prediction scheme for lithium-ion battery packs is developed. Firstly, the charging duration for a predefined voltage range is hired as the health feature to quantify ca...

Preview control algorithm has been widely implemented in intelligent vehicle path-tracking controllers. The key challenge of developing such control is to determine the appropriate preview distance, which plays a vital role in achieving the optimal trade-off between two competing control objectives, tracking accuracy and driving stability. Addition...

Driving behaviors, induced by psychological activities and environment stimulation, impose the dominant impact on vehicle driving performance. To exhaustively improve the performance of electric vehicles (EVs), information unscrambled from various driving behaviors is recommended to be incorporated into the controlling process. In this context, a n...

This study investigates accurate state of charge estimation algorithms for lithium-ion batteries based on the long short-term memory recurrent neural network and transfer learning. The long short-term memory network with the five typical layer topology is firstly constructed to learn the dependency of state of charge on measured variables. The tran...

This paper presents an approach to the design of an optimal control strategy for plug-in hybrid electric vehicles (PHEVs) incorporating Internet of Vehicles (IoVs). The optimal strategy is designed and implemented by employing a mobile edge computing (MEC) based framework for IoVs. The thresholds in the optimal strategy can be instantaneously optim...

Accurate state of charge estimation is essential to improve operation safety and service life of lithium-ion batteries. This paper proposes a synthetic state of charge estimation method for lithium-ion batteries based on long short-term memory network modeling and adaptive H-infinity filter. Firstly, the long short-term memory network is exploited...

The multi-source electromechanical coupling renders energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions, and hinders the control strategies efficiently applied instantaneously, leading to massive challe...

In this paper, a cooperative optimization strategy is proposed for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles. Based on the established vehicle model, a mathematical analytical method is investigated to convert the driving cycles from the original time based profiles to the driving distance bas...

Adaptability to various driving conditions (TCs) is one of the essential indicators to assess the optimality of power management strategies (PMSs) of plug-in hybrid electric vehicles (PHEVs). In this study, a novel optimal PMS with the improved adaptability to TCs is proposed for PHEVs to achieve the energy-efficient control in momentary scenarios...

In this paper, an improved online particle swarm optimization (PSO) is proposed to optimize the traditional search controller for improving the operating efficiency of the permanent magnet synchronous motor (PMSM). This algorithm combines the advantages of the attraction and repulsion PSO and the distributed PSO that can help the search controller...

Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradations. This paper investigates the deep learning method for lithium-ion battery's capacity prediction...

Accurate estimation of state of charge (SOC) of lithium-ion battery packs remains challenging due to inconsistencies among battery cells. To achieve precise SOC estimation of battery packs, firstly, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed to characterize the battery electrical performance, and a rol...

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL prediction methods are mainly developed based on offline training, which are limited by sufficiency and reliability of available data. To address this problem, this paper presents a method for RUL...

Due to the significant influence of temperature on battery charging and discharging performance, exact evaluation of state of charge under complex temperature environment becomes increasingly important. This paper develops an advanced framework to estimate the state of charge for lithium-ion batteries with consideration of temperature variation. Fi...

This paper proposes a novel control framework and the corresponding strategy for power sources management in connected plug-in hybrid electric vehicles (cPHEVs). A mobile edge computation (MEC) based control framework is developed first, evolving the conventional on-board vehicle control unit (VCU) into the hierarchically asynchronous controller th...

In this paper, a stochastic model predictive control (MPC) method based on reinforcement learning is proposed for energy management of plug-in hybrid electric vehicles (PHEVs). Firstly, the power transfer of each component in a power-split PHEV is described in detail. Then an effective and convergent reinforcement learning controller is trained by...

This article presents a novel control algorithm for online optimal charging of lithium-ion battery by explicitly incorporating degradation mechanism into control, to reduce the degradation process. The health of battery directly relates to degradation and capacity fade in cycles of charging. We mainly focus on the growth of the solid electrolyte in...

Lithium-ion battery packs are widely deployed as power sources in transportation electrification solutions. To ensure safe and reliable operation of battery packs, it is of critical importance to monitor operation status and diagnose the running faults in a timely manner. This study investigates a novel fault diagnosis and abnormality detection met...