Yonggang Liu

Chongqing University | CQU · Department of Automobile Engineering

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...

To improve the shift quality of dual clutch transmission (DCT) vehicles and reduce the jerk and sliding friction in the shift process, a real-time planning of clutch optimal engagement trajectory and control method in the shift process is proposed. This method combines pseudo-spectral optimization, machine learning, and model predictive control. Fi...

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...

Detecting the fork displacement sensor fault is critical for ensuring the reliability and safety of a dual clutch transmission (DCT). In this paper, a deep learning method is proposed to monitor the state of the fork displacement sensor. Firstly, the fork displacement prediction algorithm is developed based on the deep long short-term memory (LSTM)...

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...

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...

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...

The unsuitably designed powertrain mount may cause jittering and shrugging during the starting and shifting processes of the vehicle, which seriously affects the comfort of using the vehicle. However, the influence of mounts on vehicles has been neglected in previous studies. In view of the above problems, this study establishes a DCT vehicle coupl...

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...

In this study, a dual-clutch transmission start control strategy based on pseudo-spectral optimization and data-driven control is proposed to respond to the time-varying start intentions, to reduce friction and jerk, and to improve the start quality. First, taking the jerk, start time, and friction work as the optimization indexes, the adaptive pse...

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...

Speed sensors in the dual clutch transmission (DCT) play an essential role in designing the vehicle launching and gear shifting strategy. The speed information can also be used to monitor whether the DCT system operates normally. The vehicle performance will degrade rapidly once the speed sensor occurs fault, which may lead to poor driving experien...

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...

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...

Gear-shift decision-making is the key to affect the power, economy and comfort of the vehicle. At present, gear-shift decision-making mostly relies on manual calibration, and it is impossible to make reasonable decisions based on real-time changes in driving intentions. The degree of intelligence needs to be improved. This paper proposes an online...

A powertrain mount system exhibits an obvious influence on the starting and shifting performances of dual clutch transmission (DCT) vehicles. However, the law and mechanism of influence have not been clarified. In this study, a dynamic model of a DCT vehicle including a powertrain mount is built. In addition, the law and mechanism of influence of a...

Speed sensors in a dual clutch transmission play an important role in the launch and gear-shift processes. Speed sensor faults seriously affect vehicle performance, resulting in riding discomfort, apart from being a threat to the safety of the system. To diagnose speed sensor faults in a dual clutch transmission vehicle, this paper proposes a fault...

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...

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...

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...

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...

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...

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...

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...

In this study, a machine learning method is proposed for online diagnosis of battery state of health. A prediction model for future voltage profiles is established based on the extreme learning machine algorithm with the short-term charging data. A fixed size least squares-based support vector machine with a mixed kernel function is employed to lea...

Accurate estimation of inner status is vital for safe reliable operation of lithium-ion batteries. In this study, a temperature compensation-based adaptive algorithm is proposed to simultaneously estimate the multi-state of lithium-ion batteries including state of charge, state of health and state of power. In the proposed co-estimation algorithm,...

Online optimal energy management of plug-in hybrid electric vehicles has been continually investigated for better fuel economy. This paper proposed a predictive energy management strategy based on multi neural networks for a multi-mode plug-in hybrid electric vehicle. To attain it, firstly, the offline optimal results prepared for knowledge learnin...

In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived offline by the Pontryagin’s minimum pr...

State of health is one of the most critical parameters to characterize inner status of lithium-ion batteries in electric vehicles. In this study, a uniform estimation framework is proposed to simultaneously achieve the estimation of state of health and optimize the healthy features therein, which are excavated based on the charging voltage curves w...

The equivalent fuel consumption minimum strategy (ECMS) based on the Pontryagin's minimum principle (PMP) enables real-time energy management optimization of plug-in hybrid electric vehicles (PHEVs). However, it remains challenging to accurately determine the equivalent factor (EF). In this study, an analytical expression of the optimal EF boundary...

In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe t...

Precise estimation of state of health (SOH) are of great importance for proper operation of lithium-ion batteries equipped in electric vehicles. For real applications, it is however difficult to estimate battery SOH due to stochastic operation, which in turn speeds up aging process of the battery. To attain the precise SOH estimation, an efficient...

In this study, a blended energy management strategy considering influences of driving conditions is proposed to improve the fuel economy of plug-in hybrid electric vehicles. To attain it, dynamic programming is firstly applied to solve and quantify influences of different driving conditions and driving distances. Then, the driving condition is iden...

Identification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this end, the driving intentions when the vehicle runs on...

As one of the most promising vehicle automatic transmission, the dual clutch transmissions (DCT) have become a research hotspot. In order to formulate different shift schedules of DCT to meet economic and comfort requirements, it is necessary to classify and identify driving styles based on vehicle driving data. Accurate classification of driving s...

In order to improve the fuel economy of vehicles equipped with a dual clutch transmission, this paper proposes a real-time gearshift schedule optimization method based on dynamic programming (DP) and future vehicle speed prediction. The global condition information is necessary for DP algorithm, which makes it difficult to be applied to the real-ti...

Energy management strategies play a critical role in performance optimization of plug-in hybrid electric vehicles (PHEVs). In order to attain effective energy distribution of PHEVs, a predictive energy management strategy is proposed in this study based on real-time traffic information. First, an exponentially varied model for the velocity predicti...