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An X-by-wire chassis can improve the kinematic characteristics of human-vehicle closed-loop system and thus active safety especially under emergency scenarios via enabling chassis coordinated control. This paper aims to provide a complete and systematic survey on chassis coordinated control methods for full X-by-wire vehicles, with the primary goal of summarizing recent reserch advancements and stimulating innovative thoughts. Driving condition identification including driver's operation intention, critical vehicle states and road adhesion condition and integrated control of X-by-wire chassis subsystems constitute the main framework of a chassis coordinated control scheme. Under steering and braking maneuvers, different driving condition identification methods are described in this paper. These are the trigger conditions and the basis for the implementation of chassis coordinated control. For the vehicles equipped with steering-by-wire, braking-by-wire and/or wire-controlled-suspension systems, state-of-the-art chassis coordinated control methods are reviewed including the coordination of any two or three chassis subsystems. Finally, the development trends are discussed.
Lei Zhang; Zhiqiang Zhang; Zhenpo Wang; Junjun Deng; David G. Dorrell. Chassis Coordinated Control for Full X-by-Wire Vehicles-A Review. Chinese Journal of Mechanical Engineering 2021, 34, 1 -25.
AMA StyleLei Zhang, Zhiqiang Zhang, Zhenpo Wang, Junjun Deng, David G. Dorrell. Chassis Coordinated Control for Full X-by-Wire Vehicles-A Review. Chinese Journal of Mechanical Engineering. 2021; 34 (1):1-25.
Chicago/Turabian StyleLei Zhang; Zhiqiang Zhang; Zhenpo Wang; Junjun Deng; David G. Dorrell. 2021. "Chassis Coordinated Control for Full X-by-Wire Vehicles-A Review." Chinese Journal of Mechanical Engineering 34, no. 1: 1-25.
Steer-by-Wire (SBW) systems are an integral and essential component of intelligent electrified vehicles and are critical to ensure safe vehicle operations. In this paper, a fault-tolerant control method for SBW is proposed to mitigate the adverse influence of front wheel steering angle sensor faults based on the Kalman filtering technique. First, a linearized vehicle model is derived based on tire characteristics and is further combined with a steering actuator model to provide the modelling foundation for estimating the front wheel steering angle. Considering multi-source noises and their significant influence on the front wheel steering angle sensor, the Butterworth filter is employed to filter out the high-frequency noises. The front wheel steering angle estimation is realized by incorporating the measurements of the yaw rate and lateral acceleration of vehicle and the current of steering motor. In particular, a dynamic threshold mechanism is adopted to activate the fault diagnosis and fault-tolerant compensation based on the residual theory. Finally, a linear time-varying model predictive controller is presented to execute the trajectory tracking task for an intelligent electrified vehicle. The effectiveness of the proposed scheme is verified under the single lane change and double lane change maneuvers during the trajectory tracking.
Lei Zhang; Zihao Wang; Xiaolin Ding; Shaohua Li; Zhenpo Wang. Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking. IEEE Access 2021, 9, 65174 -65186.
AMA StyleLei Zhang, Zihao Wang, Xiaolin Ding, Shaohua Li, Zhenpo Wang. Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking. IEEE Access. 2021; 9 ():65174-65186.
Chicago/Turabian StyleLei Zhang; Zihao Wang; Xiaolin Ding; Shaohua Li; Zhenpo Wang. 2021. "Fault-Tolerant Control for Intelligent Electrified Vehicles Against Front Wheel Steering Angle Sensor Faults During Trajectory Tracking." IEEE Access 9, no. : 65174-65186.
In this paper, an enabling hybrid control-based acceleration slip regulation (ASR) method is proposed for four-wheel-independently-actuated electric vehicles by combining the advantages of the maximum-torque-based and slip-ratio-based ASR methods. Considering the dramatic fluctuation of tire slip ratio at low speeds caused by poor signal-to-noise ratio (SNR) for the vehicle speed, an adaptive maximum torque search method is employed to ensure the acceleration regulation performance at low speeds. With the increasing vehicle speed, the SNR would have diminishing influence so that the tire slip ratio gradually approaches its true value. Under such scenarios, the robust sliding mode control method is proposed to regulate the real-time slip ratio to its optimal value so as to maximize the tire-road adhesive force. In order to coordinate the driving torques and guarantee a smooth transition process in between, a finite state machine-based control scheme is further developed. Hardware-in-Loop test results show that the proposed hybrid control-based acceleration slip regulation scheme exhibits good performance and high reliability under various driving conditions.
Xiaolin Ding; Zhenpo Wang; Lei Zhang. Hybrid Control-Based Acceleration Slip Regulation for Four-Wheel-Independent-Actuated Electric Vehicles. IEEE Transactions on Transportation Electrification 2020, 7, 1976 -1989.
AMA StyleXiaolin Ding, Zhenpo Wang, Lei Zhang. Hybrid Control-Based Acceleration Slip Regulation for Four-Wheel-Independent-Actuated Electric Vehicles. IEEE Transactions on Transportation Electrification. 2020; 7 (3):1976-1989.
Chicago/Turabian StyleXiaolin Ding; Zhenpo Wang; Lei Zhang. 2020. "Hybrid Control-Based Acceleration Slip Regulation for Four-Wheel-Independent-Actuated Electric Vehicles." IEEE Transactions on Transportation Electrification 7, no. 3: 1976-1989.
Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially-available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state-of-the-art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics-, dynamics- and neural network-based estimators. A hardware-in-loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics-based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics-based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network-based estimator can perform well in all manoeuvres once the training datasets are properly selected.
Jizheng Liu; Zhenpo Wang; Lei Zhang; Paul Walker. Sideslip angle estimation of ground vehicles: a comparative study. IET Control Theory & Applications 2020, 14, 3490 -3505.
AMA StyleJizheng Liu, Zhenpo Wang, Lei Zhang, Paul Walker. Sideslip angle estimation of ground vehicles: a comparative study. IET Control Theory & Applications. 2020; 14 (20):3490-3505.
Chicago/Turabian StyleJizheng Liu; Zhenpo Wang; Lei Zhang; Paul Walker. 2020. "Sideslip angle estimation of ground vehicles: a comparative study." IET Control Theory & Applications 14, no. 20: 3490-3505.
Electrochemical energy storage systems are fundamental to renewable energy integration and electrified vehicle penetration. Hybrid electrochemical energy storage systems (HEESSs) are an attractive option because they often exhibit superior performance over the independent use of each constituent energy storage. This article provides an HEESS overview focusing on battery-supercapacitor hybrids, covering different aspects in smart grid and electrified vehicle applications. The primary goal of this paper is to summarize recent research progress and stimulate innovative thoughts for HEESS development. To this end, system configuration, DC/DC converter design and energy management strategy development are covered in great details. The state-of-the-art methods to approach these issues are surveyed; the relationship and technological details in between are also expounded. A case study is presented to demonstrate a framework of integrated sizing formulation and energy management strategy synthesis. The results show that an HEESS with appropriate sizing and enabling energy management can markedly reduce the battery degradation rate by about 40% only at an extra expense of 1/8 of the system cost compared with battery-only energy storage.
Lei Zhang; Xiaosong Hu; Zhenpo Wang; Jiageng Ruan; Chengbin Ma; Ziyou Song; David G. Dorrell; Michael G. Pecht. Hybrid electrochemical energy storage systems: An overview for smart grid and electrified vehicle applications. Renewable and Sustainable Energy Reviews 2020, 139, 110581 .
AMA StyleLei Zhang, Xiaosong Hu, Zhenpo Wang, Jiageng Ruan, Chengbin Ma, Ziyou Song, David G. Dorrell, Michael G. Pecht. Hybrid electrochemical energy storage systems: An overview for smart grid and electrified vehicle applications. Renewable and Sustainable Energy Reviews. 2020; 139 ():110581.
Chicago/Turabian StyleLei Zhang; Xiaosong Hu; Zhenpo Wang; Jiageng Ruan; Chengbin Ma; Ziyou Song; David G. Dorrell; Michael G. Pecht. 2020. "Hybrid electrochemical energy storage systems: An overview for smart grid and electrified vehicle applications." Renewable and Sustainable Energy Reviews 139, no. : 110581.
In this paper, an enabling multi-sensor fusion-based vehicle speed estimator is proposed for four-wheel-independently-actuated electric vehicles (FWIA EVs) using a Global positioning and Beidou Navigation Positioning (GPS-BD) module and a low-cost IMU. For accurate vehicle speed estimation, an approach combing wheel speed- and GPS-BD information is firstly put forward to compensate for the impact of road gradient on the horizontal velocity of the GPS-BD module and the longitudinal acceleration of the IMU. Then, a multi-sensor fusion-based vehicle speed estimator is synthesized by employing three virtual sensors which generate three longitudinal vehicle speed tracks based on multiple sensor signals. Finally, the accuracy and reliability of the proposed multi-sensor fusion estimator are examined under a diverse range of driving conditions through hardware-in-the-loop (HIL) tests. The results show that the proposed method has high estimation accuracy, robustness and real-time performance.
Xiaolin Ding; Zhenpo Wang; Lei Zhang; Cong Wang. Longitudinal Vehicle Speed Estimation for Four-Wheel-Independently-Actuated Electric Vehicles Based on Multi-Sensor Fusion. IEEE Transactions on Vehicular Technology 2020, 69, 12797 -12806.
AMA StyleXiaolin Ding, Zhenpo Wang, Lei Zhang, Cong Wang. Longitudinal Vehicle Speed Estimation for Four-Wheel-Independently-Actuated Electric Vehicles Based on Multi-Sensor Fusion. IEEE Transactions on Vehicular Technology. 2020; 69 (11):12797-12806.
Chicago/Turabian StyleXiaolin Ding; Zhenpo Wang; Lei Zhang; Cong Wang. 2020. "Longitudinal Vehicle Speed Estimation for Four-Wheel-Independently-Actuated Electric Vehicles Based on Multi-Sensor Fusion." IEEE Transactions on Vehicular Technology 69, no. 11: 12797-12806.
In this paper, an adaptive model predictive control (AMPC) scheme with high computational efficiency is developed to improve the yaw stability for four-wheel-independently-actuated electric vehicles (FWIA EVs). A novel vehicle model is first established based on an autoregressive with exogenous input (ARX) model, which is independent of vehicle parameters and road conditions. The time-varying model parameters are identified by an unbiased estimation system via an instrumental variable (IV) method. The AMPC scheme is proposed based on the ARX vehicle model for direct yaw moment control (DYC). Then, a multi-objective optimization method is proposed to optimize torque allocation for yaw stability enhancement. Finally, the performance of the proposed scheme is verified under the double lane change and slalom maneuvers in Carsim. Simulation results show that the ARX-model-based unbiased estimation can effectively follow the reference while filtering out measurement noises. The yaw rate signal is smoother and the computational time is reduced by half under the proposed AMPC scheme in comparison to that under conventional dynamics-model-based MPC. In the meantime, the vehicle slip angle and the steering wheel angle are reduced, which indicates improved vehicle stability.
Jianyang Wu; Zhenpo Wang; Lei Zhang. Unbiased-estimation-based and computation-efficient adaptive MPC for four-wheel-independently-actuated electric vehicles. Mechanism and Machine Theory 2020, 154, 104100 .
AMA StyleJianyang Wu, Zhenpo Wang, Lei Zhang. Unbiased-estimation-based and computation-efficient adaptive MPC for four-wheel-independently-actuated electric vehicles. Mechanism and Machine Theory. 2020; 154 ():104100.
Chicago/Turabian StyleJianyang Wu; Zhenpo Wang; Lei Zhang. 2020. "Unbiased-estimation-based and computation-efficient adaptive MPC for four-wheel-independently-actuated electric vehicles." Mechanism and Machine Theory 154, no. : 104100.
Unmanaged cell inconsistency may cause accelerated battery degradation or even thermal runaway accidents in electric vehicles (EVs). Accurate cell inconsistency evaluation is a prerequisite for efficient battery health management to maintain safe and reliable operation and is also vital for battery second-life utilization. This paper presents a cell inconsistency evaluation model for series-connected battery systems based on real-world EV operation data. Open circuit voltage (OCV), internal resistance, and charging voltage curve are extracted as consistency indicators (CIs) from a large volume of electric taxis’ operation data. The Thevenin equivalent circuit model is adopted to delineate battery dynamics, and an adaptive forgetting factor recursive least squares method is proposed to reduce the fluctuation phenomenon in model parameter identification. With a modified robust regression method, the evolution characteristics of the three CIs are analyzed. The Mahalanobis distance in combination with the density-based spatial clustering of applications with noise is employed to comprehensively evaluate multi-parameter inconsistency state of a battery system based on the CIs. The results show that the proposed method can effectively assess cell inconsistency with high robustness and is competent for real-world applications.
Qiushi Wang; Zhenpo Wang; Lei Zhang; Peng Liu; Zhaosheng Zhang. A Novel Consistency Evaluation Method for Series-Connected Battery Systems Based on Real-World Operation Data. IEEE Transactions on Transportation Electrification 2020, 7, 437 -451.
AMA StyleQiushi Wang, Zhenpo Wang, Lei Zhang, Peng Liu, Zhaosheng Zhang. A Novel Consistency Evaluation Method for Series-Connected Battery Systems Based on Real-World Operation Data. IEEE Transactions on Transportation Electrification. 2020; 7 (2):437-451.
Chicago/Turabian StyleQiushi Wang; Zhenpo Wang; Lei Zhang; Peng Liu; Zhaosheng Zhang. 2020. "A Novel Consistency Evaluation Method for Series-Connected Battery Systems Based on Real-World Operation Data." IEEE Transactions on Transportation Electrification 7, no. 2: 437-451.
There is an increasing awareness of the need to reduce traffic accidents and fatality rates due to vehicle rollover incidents. Accurate detection of impending rollover is necessary to effectively implement vehicle rollover prevention. To this end, a real-time rollover index and a rollover tendency evaluation system are needed. These should give high accuracy and be of a low application cost. This paper proposes a rollover evaluation system taking lateral load transfer ratio (LTR) as the rollover index, with inertial measurement unit (IMU) as the system input. A nonlinear suspension model and a rolling plane vehicle model are established for state and parameter estimation. An adaptive extended Kalman filter (AEKF) is utilized to estimate the roll angle and rate, which adjusts noise covariance matrices to accommodate the nonlinear model characteristic and the unknown noise characteristic. In the meantime, the recursive least squares method with forgetting factor (FFRLS) is utilized to identify the height of the center of gravity (CG). The Butterworth filter is used to filter out the high frequency noise of acceleration signal and the index of LTR is accordingly calculated based on the estimation results. The proposed scheme is verified and compared through hardware-in-loop (HIL) tests. The results show that the developed scheme performs well in a variety of operating conditions.
Cong Wang; Zhenpo Wang; Lei Zhang; Dongpu Cao; David G. Dorrell. A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation. IEEE Transactions on Industrial Informatics 2020, 17, 4003 -4013.
AMA StyleCong Wang, Zhenpo Wang, Lei Zhang, Dongpu Cao, David G. Dorrell. A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation. IEEE Transactions on Industrial Informatics. 2020; 17 (6):4003-4013.
Chicago/Turabian StyleCong Wang; Zhenpo Wang; Lei Zhang; Dongpu Cao; David G. Dorrell. 2020. "A Vehicle Rollover Evaluation System Based on Enabling State and Parameter Estimation." IEEE Transactions on Industrial Informatics 17, no. 6: 4003-4013.
Fault diagnosis for battery systems is essential for ensuring safe operation of electric vehicles (EVs). In this study, a novel model for battery fault diagnosis is established by combining the long short-term memory recurrent neural network (LSTM) and the equivalent circuit model (ECM). The modified adaptive boosting method is utilized to improve diagnosis accuracy, and a prejudging model is employed to reduce computational time and improve diagnosis reliability. Considering the influence of drivers’ behaviors on battery systems, the proposed diagnosis scheme is able to achieve potential failure risk assessment and accordingly to issue early thermal runaway warning. A large volume of real-world operation data is acquired from the National Monitoring and Management Center for New Energy Vehicles (NMMC-NEV) in China to verify its robustness, feasibility and reliability. The results show that the proposed method exhibits superior performance, and can achieve accurate fault prediction for potential battery cell failure and precise locating of thermal runaway cells.
Da Li; Zhaosheng Zhang; Peng Liu; Zhenpo Wang; Lei Zhang. Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model. IEEE Transactions on Power Electronics 2020, 36, 1303 -1315.
AMA StyleDa Li, Zhaosheng Zhang, Peng Liu, Zhenpo Wang, Lei Zhang. Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model. IEEE Transactions on Power Electronics. 2020; 36 (2):1303-1315.
Chicago/Turabian StyleDa Li; Zhaosheng Zhang; Peng Liu; Zhenpo Wang; Lei Zhang. 2020. "Battery Fault Diagnosis for Electric Vehicles Based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model." IEEE Transactions on Power Electronics 36, no. 2: 1303-1315.
Zhang Lei; Yu Wen; Wang Zhenpo; Ding Xiaolin. Fault Tolerant Control Based on Multi-methods Switching for Four-wheel-independently-actuated Electric Vehicles. Chinese Journal of Mechanical Engineering 2020, 56, 227 -239.
AMA StyleZhang Lei, Yu Wen, Wang Zhenpo, Ding Xiaolin. Fault Tolerant Control Based on Multi-methods Switching for Four-wheel-independently-actuated Electric Vehicles. Chinese Journal of Mechanical Engineering. 2020; 56 (16):227-239.
Chicago/Turabian StyleZhang Lei; Yu Wen; Wang Zhenpo; Ding Xiaolin. 2020. "Fault Tolerant Control Based on Multi-methods Switching for Four-wheel-independently-actuated Electric Vehicles." Chinese Journal of Mechanical Engineering 56, no. 16: 227-239.
This paper presents a novel battery aging assessment method based on the incremental capacity analysis (ICA) and radial basis function neural network (RBFNN) model. The RBFNN model is used to depict the relationship between battery aging level and its influencing factors based on real-world operation data sets of electric city transit buses. The ICA method together with the Gaussian window (GW) filter method is used to derive the peak values of IC curves which are utilized to represent the battery aging level. The support vector regression (SVR) method is used in several scenarios for data preprocessing. The considered influencing factors include accumulated mileage of vehicles and initial charging SOC, average charging temperature, average charging current and average operating temperature of battery systems. The datasets collected from real-world electric city buses are used for RBFNN model training, validation and test. The results show that an average prediction error of 4.00% is reached, and the derived model has a confidential interval of 92% with the prediction of 10%. This work provides insights for battery aging prediction based on massive real-time operation data.
Chengqi She; Zhenpo Wang; Fengchun Sun; Peng Liu; Lei Zhang. Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network. IEEE Transactions on Industrial Informatics 2019, 16, 3345 -3354.
AMA StyleChengqi She, Zhenpo Wang, Fengchun Sun, Peng Liu, Lei Zhang. Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network. IEEE Transactions on Industrial Informatics. 2019; 16 (5):3345-3354.
Chicago/Turabian StyleChengqi She; Zhenpo Wang; Fengchun Sun; Peng Liu; Lei Zhang. 2019. "Battery Aging Assessment for Real-World Electric Buses Based on Incremental Capacity Analysis and Radial Basis Function Neural Network." IEEE Transactions on Industrial Informatics 16, no. 5: 3345-3354.
In this article, a robust control scheme for an in-wheel-motor-drive electric vehicle (IWMD EV) is put forward to enhance vehicle lateral stability considering network-induced time delays. A robust sliding mode controller (RSMC) is devised, and the derived control law is partitioned into two portions, i.e., the continuous and discontinuous parts. A Linear-Quadratic- Regulator (LQR) problem with network-induced time delays is formulated with the objectives of minimizing the reference states tracking errors and reducing the control efforts. Then, it is transformed into an iterative solution derivation of a two-point boundary value problem without delays, and the derived solution is obtained and constitutes the continuous part of the control law. Meanwhile, the global sliding mode theory is applied to deriving the discontinuous part of the control law, which has robustness to vehicle parameters variation and modeling uncertainties. The proposed control scheme exhibits better performance in dealing with network-induced time delays compared with the original optimal LQR controller strategies in simulation and Hardware-in-the-Loop (HIL) tests.
Lei Zhang; Yachao Wang; Zhenpo Wang. Robust Lateral Motion Control for In-Wheel-Motor-Drive Electric Vehicles With Network Induced Delays. IEEE Transactions on Vehicular Technology 2019, 68, 10585 -10593.
AMA StyleLei Zhang, Yachao Wang, Zhenpo Wang. Robust Lateral Motion Control for In-Wheel-Motor-Drive Electric Vehicles With Network Induced Delays. IEEE Transactions on Vehicular Technology. 2019; 68 (11):10585-10593.
Chicago/Turabian StyleLei Zhang; Yachao Wang; Zhenpo Wang. 2019. "Robust Lateral Motion Control for In-Wheel-Motor-Drive Electric Vehicles With Network Induced Delays." IEEE Transactions on Vehicular Technology 68, no. 11: 10585-10593.
Braking and steering are common maneuvers performed by drivers during driving. This paper presents a hierarchy control strategy to coordinate the braking and steering performance for in-wheel motor drive electric vehicles (IWMD EVs). A particle swarm optimization-based nonlinear predictive control (PSO-NMPC) scheme is proposed to calculate the required longitudinal force, lateral force and yaw moment of the vehicle in the upper controller. In the lower controller, the PSO algorithm is again utilized to realize the required forces and yaw moment through optimal torque allocation and brake actuator regulation while maintaining vehicle stability and maximizing the regenerative braking recovery. A fault-tolerance mechanism is also incorporated to enhance the robustness of the proposed method. Finally, the effectiveness of the proposed scheme is examined under various braking execution scenarios through the Carmaker-Simulink co-simulation. The results show that the proposed scheme outperforms other state-of-the-art methods in all-round aspects.
Junjun Zhu; Zhenpo Wang; Lei Zhang; David Dorrell. Braking/steering coordination control for in-wheel motor drive electric vehicles based on nonlinear model predictive control. Mechanism and Machine Theory 2019, 142, 103586 .
AMA StyleJunjun Zhu, Zhenpo Wang, Lei Zhang, David Dorrell. Braking/steering coordination control for in-wheel motor drive electric vehicles based on nonlinear model predictive control. Mechanism and Machine Theory. 2019; 142 ():103586.
Chicago/Turabian StyleJunjun Zhu; Zhenpo Wang; Lei Zhang; David Dorrell. 2019. "Braking/steering coordination control for in-wheel motor drive electric vehicles based on nonlinear model predictive control." Mechanism and Machine Theory 142, no. : 103586.
Energy storage system plays an important role in modern power systems for mitigating the variation and intermittency of renewable energy sources. The Lithium-ion battery is currently the most widely used solution for energy storage system. However, its high cost is considered as one of the major barriers hampering the integration of renewable energy and the adoption of electric vehicles. Reusing electric vehicle batteries seems a promising solution to the aforementioned problem. Based on a dynamic degradation model of Lithium-ion batteries, this paper first compares the profits that second-life and fresh batteries can bring to the wind farm. Model predictive control is adopted to solve an hourly optimal wind scheduling problem and maximize the profit of wind farm owner. The optimal size of battery is determined and then the comparison of second-life and fresh batteries is conducted taking the battery degradation, the profit of the wind farm owner, and various remaining capacities of battery into account. Two case studies in USA and Denmark are conducted and the analysis shows that given the current prices of wind energy and Lithium-ion batteries, reusing batteries is not worthwhile for the studied wind farms, but it may outperform fresh batteries in the future if the wind energy price decreases much faster than the battery price.
Ziyou Song; Shuo Feng; Lei Zhang; Zunyan Hu; Xiaosong Hu; Rui Yao. Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios. Applied Energy 2019, 251, 113411 .
AMA StyleZiyou Song, Shuo Feng, Lei Zhang, Zunyan Hu, Xiaosong Hu, Rui Yao. Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios. Applied Energy. 2019; 251 ():113411.
Chicago/Turabian StyleZiyou Song; Shuo Feng; Lei Zhang; Zunyan Hu; Xiaosong Hu; Rui Yao. 2019. "Economy analysis of second-life battery in wind power systems considering battery degradation in dynamic processes: Real case scenarios." Applied Energy 251, no. : 113411.
Xiaoyu Li; Zhenpo Wang; Lei Zhang. Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles. Energy 2019, 174, 33 -44.
AMA StyleXiaoyu Li, Zhenpo Wang, Lei Zhang. Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles. Energy. 2019; 174 ():33-44.
Chicago/Turabian StyleXiaoyu Li; Zhenpo Wang; Lei Zhang. 2019. "Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles." Energy 174, no. : 33-44.
Wang Zhenpo; Ding Xiaolin; Zhang Lei. Overview on Key Technologies of Acceleration Slip Regulation for Four-wheel-independently-actuated Electric Vehicles. Chinese Journal of Mechanical Engineering 2019, 55, 99 -120.
AMA StyleWang Zhenpo, Ding Xiaolin, Zhang Lei. Overview on Key Technologies of Acceleration Slip Regulation for Four-wheel-independently-actuated Electric Vehicles. Chinese Journal of Mechanical Engineering. 2019; 55 (12):99-120.
Chicago/Turabian StyleWang Zhenpo; Ding Xiaolin; Zhang Lei. 2019. "Overview on Key Technologies of Acceleration Slip Regulation for Four-wheel-independently-actuated Electric Vehicles." Chinese Journal of Mechanical Engineering 55, no. 12: 99-120.
This paper presents a novel hybrid Elman-LSTM method for battery remaining useful life prediction by combining the empirical model decomposition algorithm and long short-term memory and Elman neural networks. The empirical model decomposition algorithm is employed to decompose the recorded battery capacity verse cycle number data into several sub-layers. The recurrent long short-term memory and Elman neural networks are then established to predict high- and low-frequency sub-layers, respectively. Comprehensive battery test datasets have been collected and used for model parameterization and performance evaluation. The comparison results indicate that the proposed hybrid Elman-LSTM model yields superior performance relative to the other counterparts and can predict the battery remaining useful life with high accuracy. The relative prediction errors are 3.3% and 3.21% based on two unseen datasets, respectively.
Xiaoyu Li; Lei Zhang; Zhenpo Wang; Peng Dong. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. Journal of Energy Storage 2018, 21, 510 -518.
AMA StyleXiaoyu Li, Lei Zhang, Zhenpo Wang, Peng Dong. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. Journal of Energy Storage. 2018; 21 ():510-518.
Chicago/Turabian StyleXiaoyu Li; Lei Zhang; Zhenpo Wang; Peng Dong. 2018. "Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks." Journal of Energy Storage 21, no. : 510-518.
This paper presents a modified particle filter (MPF) to estimate vehicle states and parameter with high precision and robustness under complex noises and sensor fault conditions. To deal with the particle impoverishment issue, the vector particle swarm of the multivariable system is separated into univariate particle swarms, which are diversified with the selection, crossover and mutation operations of the genetic algorithm (GA) while maintaining the mean value and enlarging the standard deviation. The effectiveness of the proposed estimation scheme is verified under the scenarios of the stochastic and needling noises and acceleration sensor faults through the Carmaker-Simulink joint simulations based on typical maneuvers, outperforming the commonly-used vehicle state estimators including the unscented Kalman filter (UKF) and the unscented particle filter (UPF).
Junjun Zhu; Zhenpo Wang; Lei Zhang; Wenliang Zhang. State and parameter estimation based on a modified particle filter for an in-wheel-motor-drive electric vehicle. Mechanism and Machine Theory 2018, 133, 606 -624.
AMA StyleJunjun Zhu, Zhenpo Wang, Lei Zhang, Wenliang Zhang. State and parameter estimation based on a modified particle filter for an in-wheel-motor-drive electric vehicle. Mechanism and Machine Theory. 2018; 133 ():606-624.
Chicago/Turabian StyleJunjun Zhu; Zhenpo Wang; Lei Zhang; Wenliang Zhang. 2018. "State and parameter estimation based on a modified particle filter for an in-wheel-motor-drive electric vehicle." Mechanism and Machine Theory 133, no. : 606-624.
An accurate battery state-of-health (SOH) monitoring is crucial to guarantee safe and reliable operation of electric vehicles (EVs). In this paper, an incremental capacity analysis (ICA) method for battery SOH estimation is proposed. This uses grey relational analysis in combination with the entropy weight method. First, an interpolation method is employed to obtain incremental capacity (IC) curves. The health indexes are then extracted from the partial IC curves for grey relational analysis, and the entropy weight method is used to evaluate the significance of each health index. The battery SOH is assessed by calculating the grey relational degree between the reference and comparative sequences. Experimental tests are conducted on two battery cells with the same specifications to verify the efficacy of the proposed method. The results show that the maximum estimation error is limited to within 4%, thus proving its effectiveness.
Xiaoyu Li; Zhenpo Wang; Lei Zhang; Changfu Zou; David Dorrell. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis. Journal of Power Sources 2018, 410-411, 106 -114.
AMA StyleXiaoyu Li, Zhenpo Wang, Lei Zhang, Changfu Zou, David Dorrell. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis. Journal of Power Sources. 2018; 410-411 ():106-114.
Chicago/Turabian StyleXiaoyu Li; Zhenpo Wang; Lei Zhang; Changfu Zou; David Dorrell. 2018. "State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis." Journal of Power Sources 410-411, no. : 106-114.