Ran Gu

Bio: Ran Gu is an academic researcher from McMaster University. The author has contributed to research in topics: Battery (electricity) & Voltage. The author has an hindex of 9, co-authored 16 publications receiving 239 citations. Previous affiliations of Ran Gu include Beijing Information Science & Technology University.

xEV Li-Ion Battery Low-Temperature Effects—Review

Abstract: The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios.

Battery State-of-Power Peak Current Calculation and Verification Using an Asymmetric Parameter Equivalent Circuit Model

Abstract: In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is presented for use with battery state estimation (BSE) algorithm development; particular focus is given to state-of-power (SOP) or peak power availability reporting. A practical optimization-based method is presented for model parameterization fitting. Two novel model-based SOP algorithms are proposed to improve voltage-limit-based power output accuracy in larger time intervals. The first approach considers first-order extrapolation of resistor values and open-circuit voltage (OCV) based on the instantaneous equivalent circuit model parameters of the cell. The second proposed approach, which is referred to as multistep model predictive iterative (MMPI) method, incorporates the cell model in a model predictive fashion. Finally, a SOP verification methodology is presented that incorporates drive cycle data to realistically excite the battery model. Simulation results compare the proposed SOP algorithms to conventional approaches, where it is shown that higher accuracy can be achieved for larger time horizons.

On the Suitability of Electrochemical-Based Modeling for Lithium-Ion Batteries

Abstract: Modeling of lithium-ion cells is a key task in the development of a battery management system to achieve battery pack safety and reliable operation. Electrochemical-based approaches enable modeling of internal electrochemical processes within the lithium-ion cell. Several reduced-order electrochemical-based modeling approaches amenable for online battery state estimation are reviewed in this paper. In particular, aging effects such as the solid-electrolyte interface layer growth is modeled. The single-particle-model (SPM) method is extended using the following novelties: 1) numerical solution of the diffusion equation in the solid phase; 2) sensitivity on the numerical solution accuracy considering the number of shell partitions; 3) a new parameterization method that identifies pertinent parameters; 4) state-of-charge and state-of-health estimation algorithms based on hybrid SPM (HSPM); and 5) validations of SPM- and HSPM-based estimation algorithms using drive cycle data.

State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries

Abstract: Pragmatic approaches are proposed to enhance battery state estimation using Kalman filter (KF) and extended KF. Notable novelties introduced include: the use of state/parameter constraints, asymmetric equivalent circuit model behaviour, inclusion of nominal models, and current sensor measurement bias estimation and compensation. The so-called delta parameters are estimated to handle cell variations, aging, and online deviation of parameters. Strategic simplifications that enable the use of traditional KF algorithm are described. Unique filter structures are presented for state-of-charge and state-of-health estimation, the latter focuses on capacity and impedance estimation. The performance of the proposed approaches is demonstrated on experimental drive-cycle data designed for electric vehicle (EV) and hybrid EV applications.

Investigation of DC-Link Voltage and Temperature Variations on EV Traction System Design

Abstract: DC-link voltage and temperature variations are critical issues when designing an electric vehicle (EV) traction system. However, systems are generally reported at fixed voltage and temperature and may not, therefore, be fully specified when considering the variation of these parameters over full vehicle operating extremes. This paper presents an assessment of power-train options based on the Nissan Leaf vehicle, which is taken as a benchmark system providing experimental validation of the study results. The Nissan Leaf traction machine is evaluated and performance assessed by considering dc-link voltage and temperature variations typical of an automotive application, showing that the system lacks performance as battery state of charge decreases. An alternative traction machine design is proposed to satisfy the target performance. The vehicle power-train is then modified with the inclusion of a dc/dc converter between the vehicle battery and dc-link to maintain the traction system dc-link voltage near constant. A supercapacitor system is also considered for improved system voltage management. The trade-offs for the redesigned systems are discussed in terms of electronic and machine packaging, and mitigation of faulted operation at high speeds.

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

Abstract: State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). We showcase the LSTM-RNN's ability to encode dependencies in time and accurately estimate SOC without using any battery models, filters, or inference systems like Kalman filters. In addition, this machine-learning technique, like all others, is capable of generalizing the abstractions it learns during training to other datasets taken under different conditions. Therefore, we exploit this feature by training an LSTM-RNN model over datasets recorded at various ambient temperatures, leading to a single network that can properly estimate SOC at different ambient temperature conditions. The LSTM-RNN achieves a low mean absolute error (MAE) of 0.573% at a fixed ambient temperature and an MAE of 1.606% on a dataset with ambient temperature increasing from 10 to 25 $^{\circ }$ C.

Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus

Abstract: Lithium-ion batteries have emerged as the state-of-the-art energy storage for portable electronics, electrified vehicles, and smart grids. An enabling Battery Management System holds the key for efficient and reliable system operation, in which State-of-Charge (SOC) estimation and State-of-Health (SOH) monitoring are of particular importance. In this paper, an SOC and SOH co-estimation scheme is proposed based on the fractional-order calculus. First, a fractional-order equivalent circuit model is established and parameterized using a Hybrid Genetic Algorithm/Particle Swarm Optimization method. This model is capable of predicting the voltage response with a root-mean-squared error less than 10 mV under various driving-cycle-based tests. Comparative studies show that it improves the modeling accuracy appreciably from its second- and third-order counterparts. Then, a dual fractional-order extended Kalman filter is put forward to realize simultaneous SOC and SOH estimation. Extensive experimental results show that the maximum steady-state errors of SOC and SOH estimation can be achieved within 1%, in the presence of initial deviation, noise, and disturbance. The resilience of the co-estimation scheme against battery aging is also verified through experimentation.

Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine

Abstract: The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases.