Dynamic Battery Research Articles

Improving Rechargeable Battery Dynamics

A series of experiments was undertaken to study the effects of additive changes on the plating of zinc metal from zinc chloride solutions. From these simple examples it was hoped to offer explanations for several different effects seen in battery performance.The avenue chosen was to observe the effect of adding a leveling agent and then a pH adjustment with a particular chemical based on previous experience to see if an outstanding result would be observed. Accidentally it was more important to find that the specific characteristics of the current supply were more important than the additives. Nonetheless the best result was achieved with a combination of these factors.It was hoped at the same time to better understand the effects of diffusion on the chemical process and its response to changes in the diffusion, especially locally. It is necessary to recognize that knowing the chemical reactions on paper does not accurately predict what happens in an active rechargeable cell. The effects of diffusion limit the concentration of chemicals and often the actual reactions in both the separator structure and in the electrolyte and in the surface regions contacted by the electrolyte. This is true for both the discharging and charging cell environment. Photos and current analysis will be shown.From these or similar experiments it may be possible to predict the parallel performance in lithium or lithium ion cells and how to extend cycle life. Discussion will be invited.

Development and Initial Evaluation of a Dynamic Marksmanship Battery: Sensitivity to Vestibular Disturbances Relevant to Return-to-Duty

Mild traumatic brain injuries (mTBI) have been diagnosed in 261,480 service-members in the past decade. This study aimed to evaluate the initial reliability and validity of a military-specific task battery to assess gross vestibular function and functional balance in service-members to aid in return-to-duty decision making. Four dynamic marksmanship tasks was developed to mimic the types of balance and movement challenges well-documented among patients with mTBI, including head turns (pivot and shoot task), picking up an object from the floor (pick-up and shoot task), rapidly walking a straight line (traverse beam and shoot task), and balancing with a reduced base of support (kneel and shoot task). Test-retest reliability, sensitivity to vestibular disturbance created by chair rotation (construct validity), and ability to distinguish soldiers with and without mTBI (known-groups validity) were evaluated. Results showed acceptable test-retest reliability for two of the four tasks and found the other two tasks to be sensitive to temporary vestibular insult in healthy controls. Additionally, the pick-up and shoot task distinguished mTBI soldiers with vestibular symptoms from a control group. The pick up and shoot task showed acceptable test-retest reliability and support for known-groups validity, thus likely the best task from the battery for future research.

A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter

Because of the extensive applications of lithium-ion batteries (LIBs) in electric vehicles (EVs), the battery management system (BMS) used to monitor the state and guarantee the operating safety of LIBs has been widely researched. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, accurate SOC estimation is challenging because of erratic battery dynamics and SOC variation with current, temperature, operating conditions, etc. In this paper, a method combining a long short-term memory (LSTM) network with an adaptive cubature Kalman filter (ACKF) is proposed. The LSTM network is first utilized to learn the nonlinear relationship between the SOC and measurements, including current, voltage and temperature, and then, the ACKF is applied to smooth the outputs of the LSTM network, thus achieving accurate and stable SOC estimation. The proposed method can simplify the tedious procedure of tuning the parameters of the LSTM network, and it does not need to establish a battery model. Data collected from dynamic stress tests are used as training datasets, while data collected from US06 tests and federal urban driving schedules serve as test datasets to verify the generalization ability of the proposed method. Experimental results reveal that the proposed method can dramatically improve estimation accuracy compared with the solo LSTM method and the combined LSTM-CKF method, and it exhibits excellent generalization ability for different datasets and convergence ability to address initial errors. In particular, the root-mean-square error is less than 2.2%, and the maximum error is less than 4%.

Modeling and control of dynamic battery storage system used in hybrid grid

AbstractIn renewable energy‐based grids, the most challenging tasks are to achieve uninterrupted, reliable, and continuous power supply from these grids. From literature, the problems in these grids and their associated solutions are (a) discrepancy between generation and demand: this is resolved by hybrid solar‐wind battery storage system (BSS), (b) requirement of improved efficiency and reliability: this is achieved by the use of state‐of‐the‐art controllers and innovative switching systems, (c) analysis of dynamic systems, optimization, and control: for these, mathematical modeling of systems is prepared, which is a tough task as solar, wind, and battery system contain nonlinear parameters. This article focuses on modeling and simulation of the wind turbine, photovoltaic (PV) arrays, and dynamic BSS. Moreover, control of variable speed wind turbines with pitch angle control, output power improvement of PV systems, and State‐of‐Charge (SoC) estimation of BSS with its characteristics parameters, temperature effects are analyzed in this work. The dynamic controller is provided which controls the charging/discharging of battery with the change in load, the power output of wind and solar system and makes the system reliable and efficient. In the result section, comparison of battery voltage and SoC is made by considering and neglecting the temperature effect.

ICE/HPM generator range extender for a series hybrid EV powertrain

This study discusses an optimised auxiliary power unit for applications in series hybrid electric vehicles (SHEVs). The auxiliary power source consists of an internal combustion engine (ICE) and a hybrid permanent magnet (HPM) generator. The ICE acts as a prime mover to the HPM generator the electrical output of which is connected to the vehicle DC-link via a passive rectifier. The vehicle primary energy is supplied by a ZEBRA battery system while the ICE/HPM generator provides a range extension. The SHEV performance is evaluated over some driving cycles using performance indicators such as driving range, fuel consumption, emissions and, battery utilisation. A dynamic model of the ICE/HPM generator system is developed and a vehicle range, with consideration of the battery dynamics, is numerically evaluated via a simulation platform. The predicted results are compared with measurements from a laboratory prototype HPM generator system, which shows a good agreement hence validating the models and simulation platform.

Remaining energy estimation for lithium-ion batteries via Gaussian mixture and Markov models for future load prediction

Other than upgrading the energy storage technology employed within electric vehicles (EVs), improving the driving range estimation methods will help to reduce the phenomena, known as range anxiety. The remaining discharge energy (RDE) of the battery affects the remaining driving range of the vehicle directly and its accurate calculation is crucial. In this paper a novel approach for the RDE calculation of the battery is proposed. First a stochastic load prediction algorithm is prepared via a Markov model and Gaussian mixture data clustering. Then, the load prediction algorithm is connected to the battery second order equivalent circuit model (ECM) coupled with a bulk parameter thermal model. Based on the extrapolated load and the battery dynamics, the battery future temperature conditions, future parameter variations and its internal states are predicted. Finally, the battery end of discharge time is prognosed and its RDE is calculated iteratively. In order to prove the proposed concept, lithium-ion battery cells are selected and the performance of the method is validated experimentally under real-world dynamic current charge/discharge profiles.

A novel energy management strategy for the ternary lithium batteries based on the dynamic equivalent circuit modeling and differential Kalman filtering under time-varying conditions

The dynamic model of the ternary lithium battery is a time-varying nonlinear system due to the polarization and diffusion effects inside the battery in its charge-discharge process. Based on the comprehensive analysis of the energy management methods, the state of charge is estimated by introducing the differential Kalman filtering method combined with the dynamic equivalent circuit model considering the nonlinear temperature coefficient. The model simulates the transient response with high precision which is suitable for its high current and complicated charging and discharging conditions. In order to better reflect the dynamic characteristics of the power ternary lithium battery in the step-type charging and discharging conditions, the polarization circuit of the model is differential and the improved iterate calculation model is obtained. As can be known from the experimental verifications, the maximize state of charge estimation error is only 0.022 under the time-varying complex working conditions and the output voltage is monitored simultaneously with the maximum error of 0.08 V and the average error of 0.04 V. The established model can describe the dynamic battery behavior effectively, which can estimate its state of charge value with considerably high precision, providing an effective energy management strategy for the ternary lithium batteries.

A Karhunen-Loeve Galerkin Online Modeling Approach for the Thermal Dynamics of Li-Ion Batteries

The thermal dynamics of Li-ion batteries are very complicated, and the battery temperature is spatially distributed imbalanced from the battery interior to the surface. The thermal dynamics are commonly modeled by partial differential equations (PDEs); however, parameter identification for PDEs is difficult and time consuming. In this work, a Karhunen-Loeve Galerkin method is proposed to obtain a simple but effective low-order model for the distributed thermal dynamics of Li-ion batteries. The Karhunen-Loeve decomposition method is applied to capture the most representative spatial modes of the dynamics, while the Galerkin method is used to obtain the corresponding temporal modes. All the uncertain physical parameters in the temporal modes are identified by the Levenberg-Marquardt algorithm. The updated temporal modes synthesized with spatial modes can offer a fast estimation of the temperature distribution on the battery surface and thus has the potential to provide distributed temperature prediction for the battery management system. The proposed modeling scheme is tested on a 60Ah Li-ion battery cell, and the simulation result shows an excellent match in the temperature distribution and a faster computing speed than the rigorous physical model.

A Sparse Learning Machine for Real-Time SOC Estimation of Li-ion Batteries

The state of charge (SOC) estimation of Li-ion batteries has attracted substantial interests in recent years. Kalman Filter has been widely used in real-time battery SOC estimation, however, to build a suitable dynamic battery state-space model is a key challenge, and most existing methods still use the off-line modelling approach. This paper tackles the challenge by proposing a novel sparse learning machine for real-time SOC estimation. This is achieved first by developing a new learning machine based on the traditional least squares support vector machine (LS-SVM) to capture the process dynamics of Li-ion batteries in real-time. The least squares support vector machine is the least squares version of the conventional support vector machines (SVMs) which suffers from low model sparseness. The proposed learning machine reduces the dimension of the projected high dimensional feature space with no loss of input information, leading to improved model sparsity and accuracy. To accelerate computation, mapping functions in the high feature space are selected using a fast recursive method. To further improve the model accuracy, a weighted regularization scheme and the differential evolution (DE) method are used to optimize the parameters. Then, an unscented Kalman filter (UKF) is used for real-time SOC estimation based on the proposed sparse learning machine model. Experimental results on the Federal Urban Drive Schedule (FUDS) test data reveal that the performance of the proposed algorithm is significantly enhanced, where the maximum absolute error is only one sixth of that obtained by the conventional LS-SVMs and the mean square error of the SOC estimations reaches to 10−7, while the proposed method is executed nearly 10 times faster than the conventional LS-SVMs.

Robust Interval Observer Design for Fractional-Order Models with Applications to State Estimation of Batteries

Abstract Interval observers have been investigated by many researchers during the last decade, especially for those classes of systems that can be described by finite-dimensional continuous-time ordinary differential equations, discrete-time difference equations, and sets of partial differential equations in which both, system parameters and external disturbances, may be subject to bounded uncertainty. In contrast to this, only preliminary investigations were performed for fractional-order models. Due to the fact that many electro-chemical processes such as the charging and discharging dynamics of batteries can be described in good accuracy by using fractional-order models, this paper focuses on the design and numerical validation of interval observers for such systems. Here, we present a cooperativity-enforcing observer structure leading directly to decoupled lower and upper bounding systems for the sets of reachable states. This is visualized by a battery model with interval uncertainty in the output equation.

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively.

Energy Arbitrage Optimization With Battery Storage: 3D-MILP for Electro-Thermal Performance and Semi-Empirical Aging Models

Dispatch of battery storage systems for stationary grid applications is a topic of increasing interest: due to the volatility of power system's energy supply relying on variable renewable energy sources, one foresees a rising demand and market potential for both short- and long-term fluctuation smoothing via energy storage. While the potential revenue attainable via arbitrage trading may yet surpass the steadily declining cost of lithium-ion battery storage systems, profitability will be constrained directly by the limited lifetime of the battery system and lowered by dissipation losses of both battery and power electronic components. In this study, we present a novel three-dimensional mixed-integer program formulation allowing to model power, state of charge (SOC), and temperature dependence of battery dynamics simultaneously in a three dimensional space leveraging binary counting and union-jack triangulation. The inclusion of a state-of-the-art electro-thermal degradation model with its dependence on most influential physical parameters to the arbitrage revenue optimization allows to extend the battery lifetime by 2.2 years (or 40%) over a base scenario. By doing a profitability estimation over the battery's lifetime and using 2018 historical intraday market trading prices, we have shown that profitability of the system increases by 11.14% via introducing SOC awareness and another significant 12.64% via introducing thermal sensitivity, resulting in a total 25.19% increase over the base case optimization formulation. Lastly, through the open source publication of the optimization routines described herein, an adaption and development of the code to individual needs is facilitated.

Control Allocation for Hybrid Braking Considering Dynamic Battery Behaviour

This work deals with the problem of maximizing energy recovery while optimizing braking capability of high-performance electric vehicles under given actuator constraints. A scalable, real-time capable concept using a complementary filter with an additional daisy chain for the control allocation between hydraulic brakes and electric motors is proposed. Furthermore, a model inversion approach that derives necessary battery limits while trying to decouple the wheel speed control loop from the protection loop of a modern battery management system is described. The hybrid braking control architecture is studied in simulation and validated in a prototype electric vehicle by highly dynamical driving manoeuvres.

State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture

State-of-charge (SOC) estimation of lithium-ion batteries based on deep learning techniques has been receiving considerable attention. However, most deep-learning-based methods focus on SOC estimation at fixed ambient temperatures and cannot provide useful indications for battery state in real-world scenarios because batteries usually experience varying temperatures during operation. In this study, an encoder-decoder with bidirectional long short-term memory (LSTM) is proposed for estimating the SOC at different temperature conditions. This end-to-end model can learn sequential information from the measurement sequences to characterize battery dynamics for sequence estimation. Introducing the bidirectional LSTMs into the encoder-decoder enables the model to capture the long-term dependencies of the measurement sequences from both past and future directions to increase the estimation accuracy. The proposed method is evaluated on public battery datasets under dynamic loading profiles. Validation with an experimental dataset shows that this method of considering the sequential contexts and bidirectional dependencies of battery measurement data can accurately estimate the SOC at different ambient temperatures. In particular, the mean absolute errors are as low as 1.07% at varying temperatures. The proposed method can improve the reliability and availability of battery management systems for monitoring the battery state under varying ambient conditions.

Online and robust resource allocation for D2D communications assisted by Green relays

Device-to-device (D2D) communications in cellular networks allow proximity users to communicate directly without going through the base station. Concentrating on scenarios where the direct communication between potential D2D users may not be reliable due to poor proximity and link condition, and paying attention to energy harvesting wireless communications, this study focuses on the green relay-assisted D2D communications, where long-term evolution-advanced layer-3 relays powered by renewable energy are employed. To achieve the trade-off between system capacity maximisation and user fairness, the authors formulate an offline resource block (RB) and power allocation problem under imperfect channel information with the objective of maximising the weighted sum-rate of all users. Then, based on battery dynamics, the original problem is reformulated as an online one with energy outage avoidance. To obtain distributed solutions, the online problem is further transformed into a convex one, and the rate outage-probability requirement is also converted for tractability. Finally, applying Lagrangian duality, the distributed RB and power allocation is derived, which is globally optimal to the reformulated online problem. Comparing with a baseline scheme where all users communicate directly without assistance of relays, numerical results show the superiority of the proposed approach in terms of the average achievable data rate.

Electrode size influence on static and dynamic single cell lead-acid battery

Renewable energy such as solar energy, waves and wind require batteries as a storage of electrical energy which still has constraints related to voltage, capacity, and energy efficiency. This experiment aims to determine the effect of electrode size on lead-acid dynamic and static battery capacity and energy efficiency. Dynamic and static single cell lead-acid batteries consist of three different electrode sizes, 13.5x7.5 cm2 (A1); 22.5x7.5 cm2 (A2) and 32.5x7.5 cm2 (A3) have been developed. Continuous and simultaneous charge-discharge test using turnigy accucell-6 50 w and chargemaster 2.02 software as graphic programming. Based on experiments, dynamic batteries perform better than static batteries with a difference in capacity of up to 48% and differences in energy efficiency up to 17%. The best performance is obtained on A3 dynamic batteries with an average capacity capacity of 10357 mAh and an average energy efficiency of 81%.

Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

Accurate state of charge estimation and robust cell equalization are vital in optimizing the battery management system and improving energy management in electric vehicles. In this paper, the passive balance control based equalization scheme is proposed using a combined dynamic battery model and the unscented Kalman filter based state of charge estimation. The lithium-ion battery is modeled with a 2nd order Thevenin equivalent circuit. The combined dynamic model of the lithium-ion battery, where the model parameters are estimated depending on the state of charge, and the unscented Kalman filter based state of charge, are used to improve the performance of the passive balance control based equalization. The experimental results verified the superiority of the combined dynamic battery model and the unscented Kalman filter algorithm with very tight error bounds. Furthermore, these results showed that the presented passive balance control based equalization scheme is suitable for the equalization of series-connected lithium-ion batteries.

Sequential Monte Carlo Filter for State-of-Charge Estimation of Lithium-Ion Batteries Based on Auto Regressive Exogenous Model

The state of charge (SOC) of lithium-ion batteries (LIBs) is an important evaluation index in battery management system of energy storage systems. However, it is always a challenging task to accurately estimate SOC of LIBs, because of the existence of nonlinear characteristics and significant temperature effects. To improve the SOC estimation accuracy and robustness, a model-based estimation approach for SOC and impedance of LIBs is proposed against uncertain loading profiles and ambient temperatures. First an auto regressive exogenous model for online model order determining and parameters identification is established to monitor parameters variations based on numerical subspace state space system identification method. Second, a sequential Monte Carlo filter is employed to overcome the nonlinear and non-Gaussian error distribution state estimation problems caused by complicated open-circuit voltage characteristics. Finally, evaluation of the adaptability and generality of the proposed method are verified by different LIBs under different operating conditions. Experimental results indicate that the proposed method shows great performance, whose estimation value converges to real SOC within an error of $\pm \text{3}\%$ , and the battery model can simulate battery dynamics robustly with high accuracy.

Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles

The battery state of available power (SOAP) is crucial to improve the energy management of electric vehicles (EVs) and protect batteries from damage. This paper proposes a novel supervisory long-term prediction scheme of SOAP for lithium-ion batteries in electric vehicles. The supervisory long-term prediction denotes that the SOAP is online predicted under the supervision of the EV’s future long-term driving conditions, instead of the traditional approaches under the constant working limitations. Firstly, to accurately capture the battery dynamics, a battery model incorporated with multi-parameters dynamic open circuit voltage is established, and the least square approach with an adaptive forgetting factor is applied to online identify the battery parameters. A new battery state estimation algorithm based on an adaptive two step filter is then proposed to improve the accuracy of the state estimation. A battery’s long-term power demand (LTPD) prediction model is also established for EVs. Based on the improved battery model and predicted battery states, especially under the supervision of the predicted LTPD, the novel supervisory long-term battery SOAP prediction approach is finally put forward to make the prediction practical and accurate. The long-term state of charge (SOC) and SOAP of battery are online co-predicted by the derived algorithms. The robustness of the proposed approach against erroneous initial values, different battery aging levels and ambient temperatures is systematically evaluated by experiments. The experimental results verify the long-term battery SOAP prediction error reduced by 85.9% when compared with that by traditional approaches.

Combined State and Parameter Estimation of Lithium-Ion Battery With Active Current Injection

Estimating the State-of-Charge (SoC) and State-of-Health (SoH), together with the parameters used in representing the dynamics of a lithium-ion battery, is essential to ensure optimal and reliable operation. However, this simultaneous estimation can take a significant amount of time to converge, and the estimation accuracy is limited by measurement noise and model inaccuracy. Note that for overactuated systems (e.g., hybrid energy storage systems and hybrid electric vehicles), the overactuation feature can be exploited to optimize the battery current profile for the estimation purpose. This article shows the potential to improve estimation accuracy when the desired current is actively injected and the estimation algorithm is properly structured. Specifically, by incorporating a high-pass filter, battery parameters can be independently characterized by injecting high-frequency and medium-frequency currents, and battery SoC/SoH can then be estimated sequentially from the estimated parameters. A Cramer–Rao bound analysis shows that the accuracy of the proposed sequential estimation is much better than the case where all parameters and states are simultaneously estimated. The analysis is verified by simulation and experimental results. We point out that for battery-only applications (e.g., electric vehicles); the proposed method has limitations, as generally, the battery current profile cannot be changed.