• Energy Research

Abstract This paper studies a distributed charging model based on day-ahead optimal internal price for PV-powered Electric Vehicle (EV) Charging Station (PVCS). Considering the feed-in-tariff of PV energy, the price of utility grid and the forecast model of PV based on back-propagation neural network (BPNN), a system operation model of PVCS is introduced, which consists of the profit model of PVCS operator (PO) and the cost model of EV users. The model proposed in this paper can be designed as a Stackelberg game model, where the PO acts as the leader and all EV users participated are regarded as the followers. An optimization strategy based on heuristic algorithm and nonlinear constrained programming are adopted by the PO and each EV user, respectively. Moreover, a real-time billing strategy is proposed to deal with the errors from the forecasted PV energy and the expected charging arrangements. Finally, through a practical case, the validity of the model is verified in terms of increasing operation profit and reducing charging cost.

The simulation plays an important role in understanding of electrochemical behavior and internal process of lithium ion batteries. The existing finite difference method (FDM) to conduct the simulation of electrochemical process is time-consuming and computationally expensive. In this paper, a novel numerical method is proposed to accelerate the solution of the electrochemical model for a lithium ion battery. It is implemented in three steps. In the first step, physical analogy of electrochemical process to an electric circuit is used to solve charge conservation equations. In the second and third step, control volume method is used to solve species conservation equations. The simulation results show that the proposed method is much faster than the FDM by 2.2 times while maintain high accuracy which is verified by simulation and experimental data as well.

Abstract: Non-uniformity of Lithium-ion cells in a battery pack is inevitable and has become the bottleneck to the pack capacity, especially in the fast charging process. Therefore, a balancing approach is essentially required. This paper proposes an active online cell balancing approach in a fast charging process using the state of charge (SOC) as balancing criterion. The goal of this approach is to complete pack balancing within the limited charging time. An adaptive extended Kalman filter (AEKF) is applied to estimate the pack cell SOC during the charging process to obtain accurate results under modeling errors and measurement noises. To implement the proposed AEKF, only one additional current sensor is required to obtain the current of each cell required for the SOC estimation. An experimental platform is established to verify the effectiveness of the proposed approach. The results show that the proposed balancing approach with the SOC as a balancing criterion can overcome the challenges of non-uniformity and flat voltage plateau and charge more capacity into a LiFePO4 battery pack than those with the terminal voltage as a balancing criterion in the fast charging process.

Lithium-ion batteries (LIBs) have emerged as the preferred energy storage systems for various types of electric transports, including electric vehicles, electric boats, electric trains, and electric airplanes. The energy management of LIBs in electric transports for all-climate and long-life operation requires the accurate estimation of state of charge (SOC) and capacity in real-time. This study proposes a multi-stage model fusion algorithm to co-estimate SOC and capacity. Firstly, based on the assumption of a normal distribution, the mean and variance of the residual error from the model at different ageing levels are used to calculate the weight for the establishment of a fusion model with stable parameters. Secondly, a differential error gain with forward-looking ability is introduced into a proportional–integral observer (PIO) to accelerate convergence speed. Thirdly, a fusion algorithm is developed by combining a multi-stage model and proportional–integral–differential observer (PIDO) to co-estimate SOC and capacity under a complex application environment. Fourthly, the convergence and anti-noise performance of the fusion algorithm are discussed. Finally, the hardware-in-the-loop platform is set up to verify the performance of the fusion algorithm. The validation results of different aged LIBs over a wide range of temperature show that the presented fusion algorithm can realize a high-accuracy estimation of SOC and capacity with the relative errors within 2% and 3.3%, respectively.

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

Lithium-ion batteries (LIBs) have found wide applications in a variety of fields such as electrified transportation, stationary storage and portable electronics devices. A battery management system (BMS) is critical to ensure the reliability, efficiency and longevity of LIBs. Recent research has witnessed the emergence of model-based fault diagnosis methods in advanced BMSs. This paper provides a comprehensive review on the model-based fault diagnosis methods for LIBs. First, the widely explored battery models in the existing literature are classified into physics-based electrochemical models and electrical equivalent circuit models. Second, a general state-space representation that describes electrical dynamics of a faulty battery is presented. The formulation of the state vectors and the identification of the parameter matrices are then elaborated. Third, the fault mechanisms of both battery faults (incl. overcharege/overdischarge faults, connection faults, short circuit faults) and sensor faults (incl. voltage sensor faults and current sensor faults) are discussed. Furthermore, different types of modeling uncertainties, such as modeling errors and measurement noises, aging effects, measurement outliers, are elaborated. An emphasis is then placed on the observer design (incl. online state observers and offline state observers). The algorithm implementation of typical state observers for battery fault diagnosis is also put forward. Finally, discussion and outlook are offered to envision some possible future research directions. Comment: Submitted to Renewable and Sustainable Energy Reviews on 09-Jan-2024

Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles. Recent studies have revealed the fractional order nature of lithium ion batteries, leading to fractional order modelling techniques. In this paper, a comprehensive review of the fractional order battery models and their applications in battery management of electric vehicles is provided from the perspectives of frequency and time domains. In the frequency domain, the fractional order models to fit electrochemical impedance spectroscopy data are investigated, followed by their applications in health diagnosis, battery heating and charging strategies In the time domain, the fractional order models adopted for voltage simulation are discussed, followed by their applications in battery state estimation and fault diagnosis. Finally, from the perspectives of time domain and frequency domain applications, critical challenges and research trends for future work in terms of fractional order modelling are highlighted to advance the development of next-generation battery management.

Abstract A wavelet transform (WT)-based energy management strategy (EMS) is developed to reduce the damages caused by transient and peak power demands on batteries in plug-in hybrid electric vehicles. A hybrid energy storage system (HESS) consisting of a battery pack, an ultracapacitor pack and two DC/DC converters is established based on MATLAB/Simulink. The WT-based EMS with different decomposition levels is evaluated by using simulation under the New European Driving Cycle (NEDC). Comparison results show that the 3-decomposition-level based EMS is the optimal selection. The developed EMS is further evaluated by using simulation under three typical driving cycles including HWFET, WVUBUS and MANHATTAN. To validate the feasibility of the developed EMS, a hardware-in-the loop (HIL) test bench is constructed to simulate the EMS. The results indicate that the developed WT-based EMS with 3 decomposition levels achieves better accuracy performances.