Lithium-ion Battery Management System Research Articles

Design and implementation of a battery management system for lithium-ion batteries in electric vehicles

Design and implementation of a battery management system for lithium-ion batteries in electric vehicles

State of charge estimation of lithium-ion battery based on double deep Q network and extended Kalman filter

The state of charge (SOC), as an important parameter of the lithium-ion battery management system (BMS), is an important factor affecting the current battery detection and safety issues. Due to the non-linear characteristics of the lithium-ion battery charging and discharging process, it is difficult to model the BMS and estimate the SOC accurately. This paper takes lithium-ion battery as the research object, designs the Markov decision process (MDP) model of lithium-ion battery energy storage prediction, establishes the second-order RC equivalent circuit model (ECM) and extended Kalman filter (EKF), and proposes a method based on double deep Q network (double DQN) to optimize the EKF parameters SOC estimation method to solve the problem of dimensionality disaster and value function overestimation generated in deep reinforcement learning (DRL). Through the effective verification of the lithium-ion battery SOC estimation algorithm, the simulation results show that compared with the traditional deep Q network (DQN) algorithm, double DQN has better convergence, adaptive ability and better estimation accuracy. It can provide new ideas for the accurate estimation of SOC.

Heat pipe air-cooled thermal management system for lithium-ion batteries: High power applications

Thermal management of lithium-ion (Li-ion) batteries in Electrical Vehicles (EVs) is important due to extreme heat generation during fast charging/discharging. In the current study, a sandwiched configuration of the heat pipes cooling system (SHCS) is suggested for the high current discharging of lithium-titanate (LTO) battery cell. The temperature of the LTO cell is experimentally evaluated in the 8C discharging rate by different cooling strategies. Results indicate that the maximum cell temperature in natural convection reaches 56.8 °C. In addition, maximum cell temperature embedded with SCHS for the cooling strategy using natural convection, forced convection for SHCS, and forced convection for cell and SHCS reach 49 °C, 38.8 °C, and 37.8 °C which can reduce the cell temperature by up to 13.7%, 31.6%, and 33.4% respectively. A computational fluid dynamic (CFD) model using COMSOL Multiphysics® is developed and comprehensively validated with experimental results. This model is then employed to investigate the thermal performance of the SHCS under different transient boundary conditions.

Design of cell spacing in lithium-ion battery module for improvement in cooling performance of the battery thermal management system

In the electronics applications rechargeable Lithium-ion batteries having a high energy density is a favorable choice. But due to high energy density internal heat generation is a big problem which tends to increase with increasing the discharge rates. This fact makes thermal management an essential part of the Lithium-ion battery management system. The current study focuses on the use of forced air for cooling of a battery module consisting of 30 Lithium-ion batteries. Numerical models for a single Lithium-ion battery and a battery module cooling system are built for analysis of the system and are validated using data from previous studies. The effect of variation in longitudinal and transverse spacing between batteries on various evaluation parameters is studied. Further, for the optimized battery module the effect of flow velocity and discharge rate is studied resulting in a graph of Nusselt number versus Reynolds number. It is observed that the cooling efficiency factor is prominently affected by the transverse spacing and the temperature uniformity by the longitudinal spacing between the batteries. The current study helps in the selection of the battery module spacing for an optimized value of evaluation parameters.

Heat-pipe based spray-cooling thermal management system for lithium-ion battery: Experimental study and optimization

Today, performance of lithium-ion battery is still limited by its operating temperature. The upper bound is capped by 50 ∘C. However, this limit is easily surpassed if batteries work at a high room temperature. In this article, sintered copper-powder heat pipe combining with water spray at its condensation section is designed to attack this issue−The former is sandwiched among batteries, removing battery heat by air convection under normal thermal conditions. When battery operation deteriorates at a room temperature beyond 40 ∘C, water spray functions for rapid heat dissipation by droplet evaporation. To assess effectiveness of this battery thermal management (BTM) design, discharge of lithium iron phosphate batteries at two large currents, Id=12.5A and 24 A, are performed at 40 ∘C. The cooling performance of the proposed BTM system is examined at different air speeds, air relative humidity, spraying frequencies and duty cycles. It is also compared to other heat-pipe based BTM approaches, and tested in transient cycles. The results demonstrate the proposed BTM system is highly effective. In the case of Id=24A, the maximum temperature and maximum temperature difference of the battery surface are dropped by 29.2 ∘C and 8.0 ∘C in comparison to those without BTM aids. It well protects lithium-ion battery operating at a large discharging current in an adverse thermal environment.

A Fast Charging–Cooling Coupled Scheduling Method for a Liquid Cooling-Based Thermal Management System for Lithium-Ion Batteries

Efficient fast-charging technology is necessary for the extension of the driving range of electric vehicles. However, lithium-ion cells generate immense heat at high-current charging rates. In order to address this problem, an efficient fast charging–cooling scheduling method is urgently needed. In this study, a liquid cooling-based thermal management system equipped with mini-channels was designed for the fast-charging process of a lithium-ion battery module. A neural network-based regression model was proposed based on 81 sets of experimental data, which consisted of three sub-models and considered three outputs: maximum temperature, temperature standard deviation, and energy consumption. Each sub-model had a desirable testing accuracy (99.353%, 97.332%, and 98.381%) after training. The regression model was employed to predict all three outputs among a full dataset, which combined different charging current rates (0.5C, 1C, 1.5C, 2C, and 2.5C (1C = 5 A)) at three different charging stages, and a range of coolant rates (0.0006, 0.0012, and 0.0018 kg·s −1 ). An optimal charging–cooling schedule was selected from the predicted dataset and was validated by the experiments. The results indicated that the battery module’s state of charge value increased by 0.5 after 15 min, with an energy consumption lower than 0.02 J. The maximum temperature and temperature standard deviation could be controlled within 33.35 and 0.8 °C, respectively. The approach described herein can be used by the electric vehicles industry in real fast-charging conditions. Moreover, optimal fast charging–cooling schedule can be predicted based on the experimental data obtained, that in turn, can significantly improve the efficiency of the charging process design as well as control energy consumption during cooling.

Evaluating the accuracy of electro-thermal coupling model in lithium-ion battery via altering internal resistance acquisition methods

Predicting the heat generation and dissipation characteristics accurately is vital for the lithium-ion battery management system applied in electric vehicles. To achieve such goal, an electro-thermal coupling model is established by measuring the key parameters including the open circuit voltage, entropy coefficient and internal resistance. The internal resistance is further measured by three different methods, namely the constant-current discharge (V–I) method, the difference between the OCV and the cell voltage (OCV–V) method, and the voltage drop during the 30 s intermittent discharge (HPPC) method, which is used as the input of electro-thermal coupling model. The comparatively analysis between the output results from modeling and the testing results of lithium-ion battery, such as discharge curve, temperature distribution diagram and discharged temperature rise curves, shows that HPPC method is the best internal resistance acquisition method as compared with the others.

A noise-tolerant model parameterization method for lithium-ion battery management system

A well-parameterized battery model is prerequisite of the model-based estimation and control of lithium-ion battery (LIB). However, the unexpected yet inevitable noises may markedly discount the identification of model parameters in real applications. This paper focuses on the noise-immune and unbiased model parameter identification for LIB. The signal-disturbance interface in LIB model identification is firstly analyzed by reformulating an overdetermined nonlinear system, on the premise of a cautiously-designed instrumental vector estimator. The multi-variable identification is then solved in the framework of a separable nonlinear least squares (SNLS) problem via a novel two-step method combining least squares (LS) and variable projection algorithm (VPA), to co-estimate the noise variances and unbiased model parameters. A numerical solver is further exploited for the proposed LSVPA, giving rise to a recursive and computational efficient algorithmic architecture which is favorable for online applications. The proposed method is validated with both simulations and experiments in terms of the noise tolerance and the parameterization accuracy.

A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

Comparison of one and two time constant models for lithium ion battery

The fast and accurate modeling topologies are very much essential for power train electrification. The importance of thermal effect is very important in any electrochemical systems and must be considered in battery models because temperature factor has highest importance in transport phenomena and chemical kinetics. The dynamic performance of the lithium ion battery is discussed here and a suitable electrical equivalent circuit is developed to study its response for sudden changes in the output. An effective lithium cell simulation model with thermal dependence is presented in this paper. One series resistor, one voltage source and a single RC block form the proposed equivalent circuit model. The 1 RC and 2 RC Lithium ion battery models are commonly used in the literature are studied and compared. The simulation of Lithium-ion battery 1RC and 2 RC Models are performed by using Matlab/Simulink Software. The simulation results in his paper shows that Lithium-ion battery 1 RC model has more maximum output error of 0.42% than 2 RC Lithium-ion battery model in constant current condition and the maximum output error of 1 RC Lithium-ion battery model is 0.18% more than 2 RC Lithium-ion battery model in UDDS Cycle condition. The simulation results also show that in both simple and complex discharging modes, the error in output is much improved in 2 RC lithium ion battery model when compared to 1 RC Lithium-ion battery model. Thus the paper shows for general applications like in portable electronic design like laptops, Lithium-ion battery 1 RC model is the preferred choice and for automotive and space design applications, Lithium-ion 2 RC model is the preferred choice. In this paper, these simulation results for 1 RC and 2 RC Lithium-ion battery models will be very much useful in the application of practical Lithium-ion battery management systems for electric vehicle applications.

Estimation of the SOC of Energy-Storage Lithium Batteries Based on the Voltage Increment

State of charge (SOC) estimations are an important part of lithium-ion battery management systems. Aiming at existing SOC estimation algorithms based on neural networks, the voltage increment is proposed in this paper as a new input feature for estimation of the SOC of lithium-ion batteries. In this method, the port voltage, current and voltage increment are taken as inputs and the current SOC is used as output to train a neural network. Different from the adaptive filtering algorithm, which requires complex equivalent circuit parameter identification, this algorithm uses the voltage increment instead of the open circuit voltage (OCV); hence, the complexity of the SOC estimation algorithm is reduced, and the problem of inaccurate estimation caused by neural network algorithms without considering the internal structure of the battery is avoided. The experimental results show that compared with the traditional neural network algorithm, the neural network SOC estimation algorithm based on the voltage increment could improve the accuracy of SOC estimation.

Nonlinear State and Parameter Estimation for Li-ion Batteries with Thermal Coupling

Advanced Lithium-ion battery management systems rely on accurate cell-level state of charge (SOC) and parameter estimation for safe and efficient real-time monitoring. However, the design of combined state and parameter estimators that are provably convergent is notoriously difficult. A robust observer framework based on a coupled equivalent circuit-thermal model for a cylindrical battery is proposed. The coupled model also takes into account SOC and temperature-dependent electrical parameters for higher accuracy. In the literature, the model parameters are often treated as constants to simplify model structure and observer analysis. The problem considered in this work is particularly challenging due to (i) nonlinear two-way coupling between electrical and thermal sub-models, and (ii) nonlinear dependence of model parameters on the system states. A single aggregated observer for both SOC and thermal estimation becomes intractable due to lack of convergence certification caused by complex model coupling. We tackle this problem by proposing a sequential estimation scheme such that every sub-estimator converges separately, which is mathematically verified by Lyapunov stability analysis. Simulation results demonstrate the performance of the proposed state and parameters estimation framework.

Research on High Precision Lithium Battery Management System

Lithium battery has been used more and more in recent years due to its excellent performance such as high energy, high voltage, long service life, low self-discharge rate, environmental protection and so on. However, lithium battery also has the disadvantages of poor safety performance, complex protection circuit, high cost, etc. The high-precision lithium ion battery management system (BMS) is proposed to solve the charging and discharging protection problem of multiple lithium batteries. In order to solve this problem, we have designed a set of high-precision lithium battery management system by pulse width modulation based on previous studies.

Highly Effective Cell Equalization in a Lithium-Ion Battery Management System

This paper proposes a highly effective voltage cell equalization method for lithium-ion (Li-ion) battery management systems (BMSs) for several applications, such as nearly zero energy buildings, microgrids, and electric vehicles. The concept of the suggested method is the proper control of the charging current of each cell of a battery stack through the suitable regulation of the gate source of each cell's parallel-connected mosfet . Thus, the mosfet can act as a controllable current source for providing the proper equalization current according to the specific needs of each battery cell. The correct voltage level of the gate-source of each mosfet is determined by a cell equalization controller through the comparison of the performance of each cell of a battery stack with the healthier cell, which is detected by an aging estimation algorithm based on the evaluation of the internal resistor and the capacity fade of each cell. Therefore, smoother and faster cell equalization is achieved compared to the conventional dissipative method. The practicality and the high effectiveness of the proposed cell equalization technique have been experimentally validated. Several experimental results are presented to demonstrate the operational improvements of the proposed cell equalization technique against the conventional one.

Experimental Investigation of Water-Cooled Heat Pipes in the Thermal Management of Lithium-Ion EV Batteries

In this work, an experimental study was conducted to investigate the thermal performance of a heat pipe thermal management system for electric vehicle lithium-ion batteries. The battery cells were represented by two proxy cells with a heat source ranging from 10 to 35 W/cell. The evaporator of the heat pipes was in close contact with the battery cell surface, and the condenser was subjected to the forced convection of circulating water. The performance was characterized by the maximum surface temperature, temperature difference, total thermal resistance and Nusselt number at the condenser side. The effects of heat inputs, length of the condenser and water flowrate were also investigated. A condenser length in the range of 100–150 mm and water flowrate showed insignificant effects on the battery surface temperature and the total thermal resistance. Heat pipes were also observed to be able to reduce the battery surface temperature by 39.1% on average. They are also capable of maintaining the surface temperature below 50 °C and temperature differential below 5 °C if the heat generation of the battery cell is less than 20 W.

High-dimensional model representation-based global sensitivity analysis and the design of a novel thermal management system for lithium-ion batteries

Abstract Thermal management is indispensable to the lithium-ion battery packs utilized in electrical vehicles. In this study, a novel air-cooling-based battery thermal management system with the heat fins-assisted phase change material/expanded graphite structure is proposed. After verification of the accuracy of the thermal model based on experimental measurements, numerical studies are performed on a battery submodule. All the results are obtained at an ambient temperature of 25 °C. Considering the complexity of the multi-physics modeling, a Kriging-based high-dimensional model representation method is employed to perform the global sensitivity analysis with relatively low computational effort. The results reveal the influence of the design variables on the thermal performance of the proposed cooling system. Moreover, this method reveals the correlations between the design variables, which reduce the dimensions of the given problem and improve the computational efficiency during optimization. Finally, a series of simulation-based tests is performed to validate the superiority of the optimal cooling system under realistic and extreme operation conditions. The comparison results show that this system can effectively improve the thermal behavior of the battery, and this cooling system after optimization provides much better cooling efficiency in alleviating the temperature increase and reducing the temperature difference of the batteries within the battery packs compared to the original one.

Cooling Performance Characteristics of 20 Ah Lithium-Ion Pouch Cell with Cold Plates along Both Surfaces

Temperature control of the lithium-ion pouch cells is crucial for smooth operation, longevity and enhanced safety in the battery-operated electric vehicles. Investigating the thermal behavior of lithium-ion pouch cells and optimizing the cooling performance are required to accomplish better performance, long life, and enhanced safety. In the present study, the cooling performance characteristics of 20 Ah lithium-ion pouch cell with cold plates along both surfaces are investigated by varying the inlet coolant mass flow rates and the inlet coolant temperatures. The inlet coolant mass flow rate is varied from 0.000833 kg/s to 0.003333 kg/s, and the inlet coolant temperature is varied from 5 °C to 35 °C. In addition, the effects of the cold plate geometry parameter on cooling performance of 20 Ah lithium-ion pouch cell are studied by varying the number of the channels from 4 to 10. The maximum temperature and difference between the maximum and the minimum temperatures are considered as important criteria for cooling performance evaluation of the 20 Ah lithium-ion pouch cell with cold plates along both surfaces. The cooling energy efficiency parameter (β) and the pressure drop for 20 Ah lithium-ion pouch cell with cold plates along both surfaces are also reported. The study shows that enhanced cooling energy efficiency is accompanied with low inlet coolant temperature, low inlet coolant mass flow rate, and a high number of the cooling channels. As a result, the temperature distribution, the pressure drop, and the cooling energy efficiency parameter (β) of the 20 Ah lithium-ion pouch cell with cold plates along both surfaces are provided, and could be applied for optimizing the cooling performances of the thermal management system for lithium-ion batteries in electric vehicles.

Review on Health Management System for Lithium-Ion Batteries of Electric Vehicles

The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

Experimental investigation of thermal and strain management for lithium-ion battery pack in heat pipe cooling

Thermal and strain management is required for a considerate lithium-ion battery management system (BMS) to depress the operating temperature and strain. In this paper, non-destructive temperature equipment and strain gauges are used simultaneously to monitor the temperature and strain of 18650 lithium-ion battery pack with a heat pipe cooling device (HPCD) which is used to depress temperature. At the steady-state and dynamic-state discharge processes, the temperature of pack is dramatically different with cooling device or not, and the optimal operating temperature is kept when HPCD is embedded in the center of pack with forced convection. The strain decreases in discharge progress after the pack is equipped with HPCD and has the same trend of temperature change in charge-discharge cycle process. The battery management system of temperature and strain is also effective during multiple charge-discharge processes. Furthermore, this system has the superiority of low power consumption.

A New Method for State of Charge Estimation of Lithium-Ion Batteries Using Square Root Cubature Kalman Filter

State of charge (SOC) is a key parameter for lithium-ion battery management systems. The square root cubature Kalman filter (SRCKF) algorithm has been developed to estimate the SOC of batteries. SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables. After these points are propagated by nonlinear functions, the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions. SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition, guarantees the nonnegative quality of the covariance matrix, and avoids the divergence of the filter. Simulink models and the test bench of extended Kalman filter (EKF), Unscented Kalman filter (UKF), cubature Kalman filter (CKF) and SRCKF are built. Three experiments have been carried out to evaluate the performances of the proposed methods. The results of the comparison of accuracy, robustness, and convergence rate with EKF, UKF, CKF and SRCKF are presented. Compared with the traditional EKF, UKF and CKF algorithms, the SRCKF algorithm is found to yield better SOC estimation accuracy, higher robustness and better convergence rate.