Battery SOC Research Articles

Establishment of energy management strategy of 50 kW PEMFC hybrid system

A 50 kW class PEMFC and battery model were evaluated to find appropriate battery capacity for 50 kW class vehicles. Afterwards, fuel cell and battery EMS strategies were established in response to rapid load changes. First, a 50 kW class PEMFC model and a battery model were designed and verified using the experimental values. After that, the amount of SOC change according to the battery capacity was compared at the same fuel cell output: it was found that the use of a battery with a capacity of “40 kWh” or more is effective. By comparing stack and battery power, hydrogen consumption, and battery SOC according to load changes, it was confirmed that high efficiency of stack net power is not always good thing. In addition, it was confirmed that the most effective strategy is to set the fuel cell-to-battery ratio at 6:4 initially, at 8:2 when the load increases rapidly, and at 4:6 when the load decreases rapidly.

Parameter Optimization and Tuning Methodology for a Scalable E-Bus Fleet Simulation Framework: Verification Using Real-World Data from Case Studies

This study presents the optimization and tuning of a simulation framework to improve its simulation accuracy while evaluating the energy utilization of electric buses under various mission scenarios. The simulation framework was developed using the low fidelity (Lo-Fi) model of the forward-facing electric bus (e-bus) powertrain to achieve the fast simulation speeds necessary for real-time fleet simulations. The measurement data required to verify the proper tuning of the simulation framework is provided by the bus original equipment manufacturers (OEMs) and taken from the various demonstrations of 12 m and 18 m buses in the cities of Barcelona, Gothenburg, and Osnabruck. We investigate the different methodologies applied for the tuning process, including empirical and optimization. In the empirical methodology, the standard driving cycles that have been used in previous studies to simulate various use case (UC) scenarios are replaced with actual driving cycles derived from measurement data from buses traversing their respective routes. The key outputs, including the energy requirements, total cost of ownership (TCO), and impact on the grid are statistically compared. In the optimization scenario, the assumptions for the various vehicle and mission parameters are tuned to increase the correlation between the simulation and measurement outputs (the battery SoC profile), for the given scenario input (the velocity profile). Improved simple optimization (iSOPT) was used to provide a superfast optimization process to tune the passenger load in the bus, cabin setpoint temperature, battery’s age as relative capacity degradation (RCD), SoC cutoff point between constant current (CC) and constant voltage charging (CV), charge decay factor used in CV charging, charging power, and cutoff in initial velocity during braking for which regenerative braking is activated.

Optimal sizing and assessment of grid-tied hybrid renewable energy system for electrification of rural site

In the developing countries, rural and remote sites are rarely connected with the national grid due to the lack of transmission and distribution infrastructure. The development of hybrid renewable energy model is an alternate solution to provide electricity accessibility in such areas. In the present research work, a grid-connected hybrid system model has been developed based on the available renewable resources. The proposed model consists of local renewable sources i.e., solar and biomass along with batteries for storage purposes. Major objectives of the study are to optimize the sizing of PV panels and batteries with power sales or purchases from the grid. The net present cost of the system has been minimized which is subjected to power reliability, SOC of batteries, upper and lower bound of SPV and batteries. Discrete Harmony Search (DHS) Algorithm has been adopted to optimize the system. Among the various considered scenarios, optimal configuration includes 30 kW biomass, 83.50 kWp solar PV, 78.70 kW converter and 38.40 kWh battery bank. Further, the optimization result yields the minimum cost of energy and net present cost as 4.66 INR/kWh and 13.07 million INR respectively.

State of Charge Estimation of LiFePO4 in Various Temperature Scenarios

The state estimation of a battery is a significant component of a BMS. Due to the poor temperature performance and voltage plateau phase in LiFePO4 batteries, the difficulty of state estimation is greatly increased. At the same time, the ambient temperature in which the battery operates is changeable, and its parameters will vary with the temperature. Therefore, it is extremely challenging to estimate the state of LiFePO4 batteries under variable temperatures. In an effort to accurately estimate the SOC of LiFePO4 batteries at different and variable temperatures, as well as its capacity at low temperature, the characteristics of LiFePO4 batteries at different temperatures are first tested. In addition, a variable temperature OCV experiment is designed to obtain the OCV of the full SOC range. Then, the ECM considering temperature is established and all parameters are identified by PSO. Finally, an improved EKF algorithm is presented to accurately estimate the SOC of LiFePO4 batteries at different and variable temperatures. Meanwhile, the battery capacity at low temperature is further estimated based on the estimated SOC result. The results show that SOC estimation errors at variable temperature are all within 3%, and the capacity estimation errors at low temperature are all within 1%.

Research on HEV energy management strategy based on improved deep reinforcement learning

A parallel hybrid vehicle was studied to establish the demand power and power system model of the whole vehicle and proposed an energy management strategy based on improved deep reinforcement learning (DRL). The DRB-TD3 algorithm was proposed to improve the sampling efficiency of the original algorithm by improving the twin delayed deep deterministic policy gradient (TD3) algorithm in DRL and introduced dual replay buffers. A rule-based constraint controller was designed and embedded into the algorithm structure to eliminate unreasonable torque allocation. The performance of the dynamic planning (DP)-based energy management strategy was used as a benchmark for simulation experiment under UDDS driving conditions. The experimental results show that the DRB-TD3 algorithm has the best convergence performance compared with the deep deterministic policy gradient (DDPG) algorithm and the conventional TD3 algorithm, with 61.2$ \% $ and 31.6$ \% $ improvement in convergence efficiency, respectively, the proposed energy management strategy reduces the average fuel consumption by 3.3$ \% $ and 2.3$ \% $ compared with the DDPG- and TD3-based energy management strategies, respectively, the fuel performance reaches 95.2$ \% $ of DP-based, which with the best fuel economy, and the battery SOC can be maintained at a better level, which helps to extend the battery life.

Reducing NOx Emissions and Optimizing Fuel Economy by Controlling Torque and Catalyst Warm-up in Mild HEVs

In this paper, hierarchical model predictive control is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between the temperature control (high-level) and the torque control (low-level), which are connected via the battery. In high-level of the hierarchical control, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At the low-level of the hierarchical control, the optimal torque distribution for the engine and motor is determined. The controller at both levels then applies model predictive control to consider the future behavior of the HEV's components. Fuel consumption, battery SoC and NOx emissions from the engine are taken into account. Finally, the effectiveness of our proposed method is demonstrated by simulations.

EV Parking Lots for Flexible Energy Sourcing

Energy storage is inherently a flexible asset that can be used to reduce renewable energy curtailment and the congestion at its host network, enhance system resilience, and provide ancillary services at peak times. But the cost of technology still hampers the large-scale adoption of storage in power distribution networks. With EV parking lots included in its asset portfolio, a city can take advantage of the power stored in the parked EVs without major capital investments. In this article, we formulate the operation of an EV parking lot from the viewpoint of its owner (i.e., a city or a private entity). The lot works as a market aggregator with operational uncertainties stemming from: (i) random arrival and departure of vehicles, (ii) the SoC of EV batteries at the times of arrival and departure, and (iii) willingness of EV owners to participate. The risks from these uncertainties and market prices of ancillary services impact the bottom line of the lot owner’s revenue. For EV owners the excessive up and down cycles of battery is offset by discount offered by the lot owner. We provide an illustrative example and a roadmap to extend this model to take the holistic view of a power distribution network.

MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.

High-precision Measurement Method for Entropy Heat Coefficient of Lithium-ion Batteries

An improved method is proposed to reflect the relationship between entropy heat coefficient and battery SOC continuity. The SOC of the battery is continuously adjusted at 20°C and 30°C with very small charge/discharge multipliers. Meanwhile, the entropy heat coefficient, charge state and terminal voltage are recorded under dynamically changes of SOC. The entropy heat coefficient dU/dT could be obtained based on calculation of the open circuit voltage. In addition, different charge/discharge rates and battery materials are examined for their effects on the entropy heat coefficient. The results show that this method can be used to calculate the entropy-thermal coefficient of the battery, especially in small charge/discharge multipliers.

Controlling a combined polymer fuel cell and directional battery system maintaining efficiency at the optimum point

In this paper, a hybrid polymer fuel cell system with a lead-acid battery has been explored, so that the battery is designed to support the fuel cell system in providing the power needed for the load as well as storing additional energy. The battery is used to maintain the efficiency of the SOC battery. From a constant power strategy for a fuel cell and managing the fuel at the optimum operating point, to reducing the voltage load on the battery and reducing the instantaneous current load on the fuel cell in use. The general configuration of the hybrid system includes a polymer fuel cell followed by a boost converter, as well as a lead-acid battery pack, after which a bidirectional converter to control the power of the battery in charge and discharge mode. It's in the system. According to the simulation, it has been observed that the power contribution between the fuel cell and the battery is well suited to load changes, and the fuel cell always produces a constant power to charge the battery and power the load. It is protected from any kind of load fluctuation and interference.

Numerical study on the heat generation and thermal control of lithium-ion battery

Thermal management of lithium-ion battery plays a very important role in battery life and safety. Accurate heat generation calculation and low battery heat generation can reduce the difficulty of battery thermal management. However, the contribution of polarization heat, ohmic heat and reversible heat to the total heat generation and their individual sensibilities to battery material parameters have not been well studied, which brings difficulties in the battery thermal management. In this study, the effects of battery parameters and SOC variation ranges of electrodes on the polarization heat, ohmic heat and reversible heat production are analyzed numerically by using an electrochemical-thermal model, and the most sensible parameters influencing the three parts of heat generation are obtained. It is found that the reaction rate coefficient, maximum concentration of lithium ion in active materials and radius of solid active particles are the most sensible parameters influencing the polarization heat. The electrolyte conductivity and liquid volume fraction of the separator are the most sensible parameters influencing the ohmic heat. Overall, decreasing the electrode particle radius and increasing the electrolyte conductivity are the best methods to control heat generation. The electrode SOC influences the exchange current density which affects the battery polarization heat, and its effect is more obvious when the maximum lithium ion concentration of electrodes is higher. The heat generation of the charging process is reduced by increasing the endothermic time of the reversible heat of the negative electrode by changing negative electrode capacity or initial negative electrode SOC.

State-of-charge estimation for lithium-ion batteries based on dual extended Kalman filter

The strategy of estimating SOC by model is highly dependent on the accuracy of model. An SOC estimation method based on a dual extended Kalman fliter (DEKF) is proposed. One of the dual filters is employed to estimate the battery SOC, and the other is used to online identify the model parameters. The SOC estimation results by DEKF are compared with those by single EKF under the US06 Highway Driving Schedule test and Dynamic Stress Test (DST). The comparison results show that DEKF has higher SOC estimation and voltage prediction accuracy. Under the US06 and DST tests, the SOC mean absolute error (MAE) decreases from 2.57% and 3.00% to 1.18% and 1.56%, and the MAE of voltage prediction decreases from 54.6 mV and 38.3 mV to 43.9 mV and 29.1 mV, respectively. The SOC estimation and voltage prediction results demonstrate the effectiveness and accuracy of the proposed method.

A temporal–spatial charging coordination scheme incorporating probability of EV charging availability

The charging coordination of electric vehicle (EV) fleets in both temporal domain and spatial domain has attracted growing attention in recent years. Meanwhile, the uncertainties in EV arrival time and total available charging power from charging stations make the coordination problem highly dynamic and challenging. This paper develops a new temporal–spatial EV charging coordination scheme that jointly considers the above two major uncertainties. Firstly, EV charging scheduling (i.e., temporal coordination) is treated as a generalized Nash equilibrium game, in which each EV (including an upcoming EV) prefers to meet its own charging demand with minimized charging cost. The probability of EV charging availability is especially proposed to incorporate the charging demands of the upcoming EVs into the coordination scheme. In order to provide flexibility and private information protection, a distributed receding horizon optimization-based solution is developed, through which the Lagrange multipliers to reach the social equilibrium are determined via an iterative manner. The charging station selection is then recommended that minimizes the objective function over the entire optimization horizon. Finally, simulations under both small-scale and large-scale scenarios effectively demonstrate improved service quality of the EV charging, both in temporal and spatial domains, and avoidance of overload in charging stations. Results in a 150-EV scenario show that, averagely, the proposed method reduces battery SoC mismatch by 43% and increases degree of consistency by 5.9%.

The Characteristics of Thermal Runaway and Its Propagation in Large-Format LiFePO4 Batteries under Overcharging and Overheating Conditions

Abstract LFP batteries are widely used in energy storage stations and electric vehicles, but their related fire incidents have not been remediated. In this paper, the TR/TRP characteristics of LFP batteries/modules under different heat dissipation conditions are investigated through experiments and simulations. In addition, the TRP behaviour of large-capacity prismatic LFP modules under different SOCs and TR trigger modes is investigated in detail. It is found that the heat dissipation significantly affects the maximum TR temperature of the LFP single battery under overheating and whether TR occurs under overcharging. Besides, whether improving the convection coefficient can prolong the TRP time of the LFP module under overheating is influenced by the thermal resistance value between adjacent batteries. Furthermore, the TRP behaviour of the LFP module is significantly influenced by the SOC and TR trigger mode. 100% SOC LFP modules have the most intense TRP behaviour under overheating, with the TRP time increasing from 140 s to 644 s as the battery SOC drops from 100% to 80%, and TRP being blocked as the battery SOC drops to 50%. The LFP module however does not undergo TRP under overcharging even in an open environment at 80 °C.

Study on the Effect of Hydraulic Energy Storage on the Performance of Electro-Mechanical-Hydraulic Power-Coupled Electric Vehicles

In order to address the problems of low energy storage capacity and short battery life in electric vehicles, in this paper, a new electromechanical-hydraulic power coupling drive system is proposed, and an electromechanical-hydraulic power coupling electric vehicle is proposed based on this system. The system realizes the mutual conversion between mechanical energy, hydraulic energy, and electric energy through the electromechanical–hydraulic coupler. This paper describes the structural characteristics and working principles of the system and analyzes the different working modes during the driving of the vehicle. We established a mathematical model of the hydraulic accumulator and the hydraulic pump and motor. Based on the vehicle dynamics model, an AME Sim vehicle model was built and the vehicle, and the relevant hydraulic parameters were set in combination with the actual situation. The braking energy recovery and release process was jointly simulated by AME Sim and Simulink. The simulation results show that the hydraulic accumulator size of the accumulator volume can influence the maximum working pressure of the accumulator and the SOC of the vehicle battery, and it is verified that 35 L is the best capacity. This study has an important reference value for matching electromechanical–hydraulic coupling parameters of electric vehicles.

GA-ELM-based adaptive Kalman estimator for SOC of lithium-ion batteries

GA-ELM-based adaptive Kalman estimator for SOC of lithium-ion batteries

Renewable Energy Based Smart Grid Construction Using Hybrid Design in Control System with Enhancing of Energy Efficiency of Electronic Converters for Power Electronic in Electric Vehicles

The power electronic interface is critical in matching a distributed generation (DG) unit’s characteristics to grid requirements as most DG technologies rely on renewable energy. Increased adoption of electric vehicles (EV) is seen as a positive step toward minimizing air pollution as well as carbon emissions. Rapid proliferation of electric vehicles as well as charging stations has exacerbated voltage quality as well as harmonic distortion difficulties, which harm the efficiency of combined renewable energy. This research proposes novel hybrid design techniques in control systems that enhance the energy efficiency of electronic converters for power electronics. The control system enhancement has been carried out using a hybrid energy storage electric convertor, and energy efficiency is improved using a synergetic battery reference adaptive controller. A plug-in hybrid electric vehicle (PHEV)’s internal combustion engine with a small photovoltaic (PV) module is utilised to assess a proposed control method which effectively regulates electric power on-grid by draining electricity from batteries during peak hours as well as then charging them during off-peak times, lowering the load on the converter as well as allowing electric vehicles to charge faster. Experimental results show the constant acceleration case obtained battery current of 92 Amps, ultracapacitor current of 89 Amps, charging voltage of 88 V, DC load current of 85 Amps, battery SOC of 72%, and the time-varying acceleration proposed technique obtained current of 94 Amps, and ultracapacitor current of 90 Amps, charging voltage of 90 V, DC load current of 82 Amps, battery SOC of 79%.

Research on Estimation of State of Charge of Li-ion Battery based on Cubature Kalman Filter

Li-ion batteries are widely used in the new energy vehicle industry. To make the battery pack work safely and reliably, it is necessary to estimate the state of charge of battery (SOC) accurately. In this paper, the second-order Thevenin is used as the equivalent circuit model, and the parameters of the model are identified based on the experimental data of Hybrid Pulse Power Characterization (HPPC). In view of the shortcomings of the Extended Kalman Filter (EKF) algorithm and the Unscented Kalman Filter (UKF) algorithm, the Cubature Kalman Filter (CKF) algorithm is chosen to estimate the SOC in this paper. Three algorithms are estimated and verified under different operating conditions. This paper also compare the estimated results with the simulation data. The results showed that the accuracy of SOC-estimation is significantly improved by using the optimized CKF algorithm method. The maximum error of estimated SOC is kept within 4% and the average error is kept within 1%. Compared with other algorithms, it is greatly improved and satisfies the accuracy and real-time requirements of Li-ion battery SOC estimation, which provides a reference value for practical applications.

A novel method for determination of SOC of Lead-acid battery in E-Rickshaw application

Electric rickshaws are a popular mode of short-distance transportation in cities and an alternative to petrol- or diesel-powered auto-rickshaws because of their low capital and running cost and less human effort compared to pedal rickshaws. E Rickshaws can operate if there is energy or charge in the battery. State of Charge (SoC) indicates the amount of energy left in the batter. Accurate estimation of SoC is essential for estimating how long the vehicle can travel apart from overcharge and under-discharge protection resulting in life enhancement. Conventional methods of SoC prediction are based on the estimation of open-circuit voltage. These are inaccurate and time-consuming and become erroneous as the battery ages. A method for quick determination of SoC using a high current short-time discharge is proposed for both old and new batteries. Experimental investigations on 90Ah C10 batteries using the said methodology can determine the correct state of charge in any condition with a maximum error of ± 8 %.

Estimating the State of Charge of Lithium-Ion Batteries Based on the Transfer Function of the Voltage Response to the Current Pulse

There are several methods for estimating the SoC of lithium-ion batteries that use electrochemical battery models or artificial intelligence and intelligent algorithms. These methods have numerous advantages but are complex and computationally intensive. This paper presents a new method for estimating the SoC of lithium-ion batteries based on identifying the transfer function of the measured battery voltage response to the charging current pulse. It is assumed that the transfer function of the battery changes with the state of charge. In the learning phase, a reference table of known SoCs and associated transfer functions is created. The parameters of these transfer functions form the reference points in hyperspace. In the phase of determining the unknown SoC of the battery, the parameters of the measured transfer function form a point in hyperspace that is compared with the reference points of the transfer functions for known SoCs. The unknown SoC of the battery at the particular measurement time is obtained by finding the two reference points closest to the point of unknown SoC using the Euclidean distance and a linear interpolation based on this distance. The method is simple, computationally undemanding, insensitive to measurement noise, and has high accuracy in SoC estimation.