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Global environmental concerns over increased emissions from the transportation vehicles have driven research instincts towards development of clean energy vehicles. Pure and hybrid electric vehicles coupled with lithium-ion batteries for energy storage are environmental friendly options to realize the future dream of low carbon emissions. However, an efficient battery thermal management system (BTMS) is a prerequisite for safe operation of such high voltage batteries to avoid thermal runaway and degradation, thereby maintaining the temperature uniformity in the battery pack. Thus, the present work numerically investigates the performance of an air cooled battery module for efficient removal of heat generated during the discharge process at different values of air-flow velocity, cell configuration and the number of cells in the battery module. A coupled electrochemical-thermal model is employed to model the discharge behaviour of the battery in conjunction with the two-dimensional flow and heat transfer model to monitor the temperature in and out of each battery cell. A correlation is developed for the maximum temperature in the module as a function of the number of cells in the system. The flow configuration (inline or staggered arrangement) of cells in the stack is observed to have no influence over the velocity and thermal field beyond a critical value of number of cells in the module.

Battery pack performance is the main concern for electric vehicles and energy storage systems. However, charge imbalance is inevitable due to inconsistent manufacturing techniques and environmental conditions. Charge imbalance reduces the power performance and available energy of battery packs. Hence, it is necessary to perform battery equalization. This article proposes an active equalization circuit and a novel equalization strategy to achieve energy redistribution. A bidirectional equalization topology consisting of a forward transformer and switch matrix is proposed first. Then, an innovative equalization strategy based on clustering analysis and genetic algorithm (GA) is developed. Clustering analysis is introduced to identify the target cells to be balanced. To further increase the speed of the equalization process, GA is employed to optimize the classification results. Finally, a series of experiments were conducted to confirm the effectiveness of the proposed topology and the strategy. Both the simulation and experimental results validate that the proposed equalization strategy not only improves the inconsistency but also increases the equalization speed. In practical battery pack experiments, the pack capacity is improved by 16.84% after equalization, and the equalization time is decreased by 23.8% using the proposed method.

This article presents an energy management strategy (EMS) design and optimization approach for a plug-in hybrid electric vehicle (PHEV) with a hybrid energy storage system (HESS) which contains a Li–Ti–O battery pack and a Ni–Co–Mn battery pack. The EMS shares power flows within the hybrid powertrain, and it employs a dual fuzzy logical controller whose inputs are predictions for PHEV powertrain states. An elitist nondominant genetic algorithm using a model in loop simulation approach as fitness functions is implemented to multiobjective optimization for the EMS under worldwide light-duty test cycles. The optimal objectives are improving PHEV mileage, minimizing battery packs capacity fades, reducing HESS degradation inconsistency, and minimizing driving cost unit distance. A hardware in loop test bench has been established to verify EMS performances in embedded systems. The test results under new European driving cycles demonstrate that optimized EMSs remain appropriate for different driving cycles and their performances are close to dynamic programming based offline optimal solutions. Due to the contributions of both the HESS and the optimized EMS, the PHEV energy efficiency has been improved by 1.6%–2.5% and the PHEV energy storage system cycle life can be improved by 159%–203%.

Battery thermal management system (BTMS) is a key to control battery temperature and promote the development of electric vehicles. In this paper, the heat dissipation model is used to calculate the battery temperature, saving a lot of calculation time compared with the CFD method. Afterward, sensitivity analysis is carried out based on the heat dissipation model, and four structural parameters and inlet airflow rate are selected as design variables. Based on not increasing the volume of the battery pack, BTMS is optimized with the objective of reducing power consumption, maximal temperature difference, and maximal temperature. In terms of multiobjective optimization algorithms, the overall performance of the NSGA-III_DE (Non-dominated Sorting Genetic Algorithm-III_Differential Evolution) algorithm is much better. Therefore, the NSGA-III_DE algorithm is used to perform multiobjective optimization on the heat dissipation model to obtain the optimal design variables. The optimization result shows that compared with the original BTMS, the optimized BTMS has a decrease of 16.7% in power consumption, 60.7% in maximal temperature difference, and 3.4 K in maximal temperature.

Lithium-ion cells are the most commonly used portable energy storage devices due to their high specific energy and energy density but have certain thermal issues that raise safety concerns. The cynosure of this work is to prepare an accurate electrochemical-thermal model of commercially available 18650 Li-ion cells of various chemistries, for tropical countries (like India). The developed model can be used for thermal analysis of diverse desired battery pack configuration and various battery cooling strategies. Newman Pseudo Two Dimensional (P2D) model is employed as it can accurately capture Li-ion migration within the cell. Nyquist plots are generated for the selected cells and Warburg constants used in the determination of Li-ion diffusion coefficients from Electrochemical Impedance Spectroscopy. This battery model was used to simulate commercial cells at discharge rates of 1C, 2C and C/2 for 3600 s, 1800 s and 7200 s, respectively. Different parameters like heat generation, temperature distribution, current densities, voltage, DOD are estimated in the model discussed here.

Lithium-ion batteries are widely used in energy storage nowadays. However, differences caused by aging among in-pack cells are inevitable, which makes accurate state-of-charge (SOC) estimation for packs still challenging. In this article, a novel discharge mode identification (DMI) method for series-connected battery pack online SOC estimation is proposed. The DMI method simplifies the process of searching for “poor SOC cell.” The discharge process is defined into two different modes on the basis of inconsistency analysis. The pack SOC is estimated based on the average cell or all cells during different discharge phases, respectively. Furthermore, considering lower computational load is critical in a battery management system (BMS), a novel segmented coulomb counting (SCC) method based on partial adaptive forgetting factors recursive least square (PAFFRLS) is proposed as a part of the DMI method, which provides a balanced solution to the cell SOC estimation. Eventually, numerous simulations and experiments for LiNCM and LiFePO4 packs are employed to verify the validity of the proposed DMI over a wide life scale. The average estimation errors of a series-connected battery pack under different working conditions at different temperatures are within 2.5%, which shows a good performance and provides a better guidance to the design of BMS.

As the energy storage device of an electric vehicle (EV), in order to meet the mileage requirements, a battery pack always has large volume and mass and is responsible for a considerable portion of...

In recent years, Lithium-ION (LI-ION) battery packs have been the dominant energy storage system (ESS) in electrified transportation applications such as material handling, robotics, and electric vertical takeoff and landing aircraft. These applications prefer a high-energy-density and lightweight ESS owing to size and weight limitations. However, they are often equipped with an oversized battery pack to mitigate the long downtime due to the low charging rates (1C) of high-energydensity Li-ion batteries. Fast charging is a potential solution to create a "filling a tank" experience that has recently drawn tremendous interest from researchers and the industry.

Efficient and effective thermal management of Li-ion battery pack for electric vehicle application is vital for the safety and extended-life of this energy storage system. In this paper, the thermal management system of a battery module is presented as an integral part of the electric vehicle air conditioning system. The refrigerant is bifurcated from the main system through a pipe and expansion valve and guided to the battery module compartment consisting of 64 18650-type Li-ion battery arranged in the form of 8 × 8 array. Several experimental strategies such as expansion valve throttle level, thermostat sensitivity and proportional-integral-derivative (PID) control algorithm are investigated. The results indicate that a larger openness of the throttling valve avoids superheating of the refrigerant and maintains the maximum temperature difference in the battery module around 2 °C. Additionally, thermostat sensitivity and PID control algorithm is found to be able to offer effective thermal management for an electric vehicle battery pack.

Li-ion batteries are widely used for battery electric vehicles (BEV) and hybrid electric vehicles (HEV) due to their high energy and power density. A battery thermal management system is crucial to improve the performance, lifetime, and safety of Li-ion batteries. The research on the heat dissipation performance of the battery pack is the current research hotspot in the electric vehicle industry. In this paper, battery modules and battery pack are simplified to heat source and semi-closed chamber, respectively. The field synergy principle and CFD technology were used to make a synergy analysis on its heat dissipation performance. Thermal flow fields of different air outlet modes were considered in this paper, and the results show that the heat dissipation performance of air-cooled battery pack increases with the improvement of the synergy degree between velocity field and temperature gradient field. Compared with other air outlet modes, the upper air outlet mode has the best cooling effect, when the inlet air flow rate is 0.444 m/s, the maximum temperature rise and the maximum temperature difference of heat source can be controlled at 7.01 °C and 3.08 °C.

Emissions and pollution from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been negatively affecting the global climate and public health. Electric vehicles (EVs) are a cleaner solution to reduce the emission and pollution caused by transportation. Lithium-ion (Li-ion) batteries are the main type of energy storage system used in EVs. The Li-ion battery pack must be considerably large to satisfy the requirement for the vehicle&rsquo, s range, which also increases the cost of the vehicle. However, considering that most people use their vehicles for short-distance travel during daily commutes, the large pack is expensive, inefficient and unnecessary. In a previous paper, we proposed a novel EV powertrain design that incorporated the use of a zinc&ndash, air (Zn&ndash, air) battery pack as a range-extender, so that a smaller Li-ion pack could be used to save costs. The design and performance aspects of the powertrain were analyzed. In this study, the environmental and economic benefits of the proposed dual-battery powertrain are investigated. The results from the new powertrain were compared with values from a standard EV powertrain with one large Li-ion pack and a conventional internal combustion engine vehicle (ICEV) powertrain. In addition, an air pollution model is developed to determine the total amount of pollution released by the transportation sector on Highway 401 in Ontario, Canada. The model was then used to determine the effects of mass passenger EV rollout on pollution reduction.

This review article describes the basic concepts of electric vehicles (EVs) and explains the developments made from ancient times to till date leading to performance improvement of the electric vehicles. It also presents the thorough review of various components and energy storage system (ESS) used in electric vehicles. The main focus of the paper is on batteries as it is the key component in making electric vehicles more environment-friendly, cost-effective and drives the EVs into use in day to day life. Various ESS topologies including hybrid combination technologies such as hybrid electric vehicle (HEV), plug-in HEV (PHEV) and many more have been discussed. These technologies are based on different combinations of energy storage systems such as batteries, ultracapacitors and fuel cells. The hybrid combination may be the perspective technologies to support the growth of EVs in modern transportation. The advanced charging systems may also play a major role in the roll-out of electric vehicles in the future. The general strategies of advanced charging systems are explained to highlight the importance of fast charging time with high amount of power and its cost-effectiveness for electric vehicles. Furthermore, the battery pack designing calculation is briefly explained along with all mechanical, electrical and environmental battery tests, which helps in the evaluation of batteries. Moreover, this paper also has a brief summarizing with the help of a flow chart, which clearly demonstrates all the parts of electric vehicles in a much simpler way.

The mechanism of hybrid (PV+gasoline) for fishing vessel propulsion was aimed to reduce fuel consumption. The background of this research is lots of vessels not going for fishing because of a lack of fuel. With the hybrid mechanism, the fishing vessels could less fuel consumption without decreases its performance (torsion, power durability, and velocity) when cruising. In this paper, a parallel hybrid (PV/gasoline) is proposed. The gasoline generator will deliver power to DC motor if PV energy that stored to the battery not enough as the requirement of propeller propulsion. Hybrid design for a 3-ton overall weight of fishing vessel was designed with eight-unit solar panels @200 WP arranged as four string-2 series. Li-Ion battery pack with 4.5 KWH of capacity was used as energy storage. The simulation shows that the hybrid scheme can preserve the vessel speed at 16 Km per hour with time duration in 12 hours 46 minutes and needs 4 hours 10 minutes for full battery charging by a gasoline generator.

In this article, the attention is focused on a specific battery testing methodology for high-power hybrid electric working vehicle applications. Due to the high power demand, a deep knowledge of the battery behaviour is necessary and requires specific testing procedures, different from the standard activities available in the literature for the automotive field. In this article, the attention focused on the power demand that an energy storage system must satisfy to achieve a certain performance, considering this testing approach closer to the real application. A numerical model of a full electric telescopic handler developed in previous works was used to test a LiFePO4 cell in a hardware-in-the-loop bench test configuration. The output voltage–current characteristic was compared with the same output generated by the numerical model of the considered battery. The same model was then tested according to a handling working cycle proposed by the authors for a telescopic handler derived from the experience of the research group in the field of this type of vehicles. This mission profile was then used to evaluate the performance of the proposed battery pack configuration in a real working scenario and at different SoC levels.

A novel cell equilibrium algorithm which is used for battery state of charge equilibrium of battery pack is proposed in this paper. Cell equilibrium algorithm is a key technology for lithium-ion battery pack in the energy storage systems. In order to eliminate the imbalance of battery state of charge, it’s generally controlled by adjusting the charging current. The artificial potential field can construct a virtual force function, which provides the mapping between state of charge deviation and charging current. Therefore, an artificial potential field-based equilibrium method to balance battery state of charge was proposed in this paper. By reasonably distributing the charging current of the battery, the equilibrium of the battery state of charge can be achieved. A laboratory testbed has been built to verify the effectiveness of the proposed method compared with conventional method. Experiment results analyzed the energy consumption and the convergence speed of state of charge deviation under artificial potential field control with different parameters.

The lifetime of shipboard energy storage systems (ESSs) has great impacts on the operating cost of all-electric ships (AESs) since their high investment costs. Additionally, those ESSs are designed to have multiple battery packs with high capacity redundancy to cope with various navigation scenarios and distributed in different electric zones onboard to avoid operating risks. To extend the battery lifetime while fully addressing the redundant capacity and distributed locations, an optimal hierarchical management model for shipboard multibattery ESS is proposed, which consists of three levels. In the first level, a practical quadratic degradation cost model is reformulated from a nonlinear data-driven model, which considers both the depth of discharge (DoD) and mean state of charge (MSOC). Then, in the second level, the obtained quadratic model is implemented into the shipboard generation scheduling that views the multibattery ESS as a “single battery pack. ”In the third level, a multibattery management strategy is implemented to split the “single battery pack’s” power to each battery group by iteratively limiting their MSOCs. To bring the proposed energy management method into practical usage, a real-time simulation experiment is designed to test its validity. The case study shows that the proposed method is suitable for real-time usage and reveals that iteratively regulating the MSOC of batteries will greatly facilitate their lifetime. As a result, the operating cost of AESs can be reduced due to battery lifetime extension.

The reconfigurable equilibrium circuit can take account of the number of components and the conversion efficiency and can easily balance every single cell in the battery pack, but the voltage of the battery pack will fluctuate when the battery pack is balanced. To solve this problem, a reconfigurable equalization circuit with an additional power supply is proposed. By replacing the cell with the additional power supply, the purpose of stabilizing the voltage of the battery pack is achieved. Aiming at the additional power supply, this paper puts forward a method that the additional power supply participates in the charging (discharging) process when the SOC (state of charge) of the battery pack is not lower (higher) than the additional power supply while the battery pack is charging (discharging), to make the charge and discharge capacity of the additional power supply always better than the battery pack. The experimental results show that the proposed equalization circuit can not only retain the advantages of the traditional reconfigurable equilibrium circuit but also maintain the voltage of the battery pack in the equalization process.

Summary In the accident scenarios of electric vehicles, the battery pack can be damaged catastrophically, resulting in the electric short circuit, thermal runaway, and possible fire and explosion. Therefore, it is important to investigate the range of conditions under which the safe operation of each individual cell is adequately controlled, known as the “safety envelope”. The biggest challenge of developing such a safety envelope lies in the acquisition of a large data bank of battery failure tests. In this study, we overcome the challenge by establishing a high-accuracy detailed computational model of lithium-ion pouch cells, in which all the component materials are characterized by well-calibrated constitutive models. A large matrix of extreme mechanical loading conditions is simulated, and a data-driven safety envelope is obtained using the machine learning algorithm. This work is a demonstration of combining numerical data generation with data-driven modeling to predict the safety of energy storage systems.

In order to solve the problem of individual voltage unevenness of energy storage battery for electric power in series operation, a multi-level uniform conditioning circuit structure based on flyback transformation is proposed, which can effectively realize the individual voltage regulation of energy storage batteries for power grid. Individual battery current through the conditioning circuit, in the multi-level multi-directional flow process, at the same time without mutual influence, this scheme through the special high-precision multi-channel acquisition circuit module, in the charging process of equalization and floating charge stage, real-time acquisition of each cell accurate voltage value, according to the voltage value monitored, the single battery outside the preset voltage range is taken as the conditioning object. Select a group of three-year running battery pack, do the comparative experiment of adding conditioning circuit and no conditioning circuit, and carry out three-stage charging experiment of constant current and voltage changing, constant voltage changing current and floating charge. The experimental results show that the scheme can realize the rapid voltage correction of battery pack, improve the overall efficiency of battery pack, slow down the performance degradation speed, and extend the service life of the whole battery.

Aiming at the problems that the application of conventional energy storage batteries in DC distribution networks, such as high cost, complicated control, and post-maintenance, this paper proposes an adaptive control strategy for charging and discharging DC distribution network energy storage systems on the basis of retired batteries, and its port output voltage can change with the grid voltage fluctuations of the DC distribution network adaptively by building a set of direct-mounted bus-type energy storage system based on retired batteries, so as to realize the adaptive charging and discharging of energy storage batteries. Finally, when the network voltage of the DC distribution network system changes suddenly, the energy storage system can realize the adaptive charging and discharging function of the energy storage system by switching on and off the retired battery pack module, which is verified by the SIMULINK simulation and experiment platform.

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a reliable health assessment strategy for battery pack is important but challenging when the joint requirements of computational burden, modelling cost, estimation accuracy, and battery equalisation are considered. This paper proposes a balancing current ratio (BCR)-based solution to realise satisfactory state-of-health (SoH) estimation performance of all series-connected cells within a pack, while the overall reliance on cell-level battery models is also reduced. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy for effectively estimating SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance (OCV-R) model is adopted, the typical estimation error of our proposed solution can still be bounded within only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also improved.

Hybrid vehicles are now more common in response to increasing global warming. The hybridization of energy sources and energy storage units enables improving the sustainability, reliability, and robustness of power systems. To reach the objective of zero emissions, a proton exchange membrane hydrogen fuel-cell was utilized as an energy source. The aim of this research was to create an accurate optimal sizing procedure for determining the nominal rating of the necessary sources. We modeled the fuel cell and the battery pack using data from real experimental results to create the generic database. Then, we added data on the mission profile, system constraints, and the minimization target function. The mission profile was then analyzed by the sizing algorithm to determine optional minimum and maximum fuel cell ratings. Analyzing the optional solutions using the vehicle real time energy management system controller resulted in a set of solutions for each available rated fuel cell, and the optimal compatible battery in the revealed band successfully accomplished the route of the driving cycle within the system limitations. Finally, the Pareto curve represented the optimal finding of the sizing procedure. Ultimately, in contrast to previous works that utilize gross manufacturer data in the sizing procedure, the main research contribution and novelty of this research is the very accurate sizing results, which draw on real experimental-based fuel-cell and battery sizing models. Moreover, the actual vehicle real time energy management system controllers were used in the sizing procedure.

Lithium battery (LiB) is widely used in electric vehicles (EVs), energy storage power stations and other fields. because of its high voltage, light weight, high energy density, good cycle performance, low self discharge rate and no memory effect, less environmental pollution and other significant advantages. However, due to the limited voltage and capacity of battery cells, battery is usually used as a battery pack composed of hundreds of single cells in parallel or series, The difference between batteries not only affect the charging and discharging capacity of batteries, but also lead to battery aging and potential safety problems. It is of great significance to study the influence of the differences between cells on the battery pack for battery grouping and battery balancing. This paper takes the coupling relationship among the initial state of charge (SOC), initial capacity and initial internal resistance into consideration, and establishes the relationship between the initial parameters. A model consists six cells connected in series is established based on the Rint equivalent circuit model, and simulations under different working conditions are conducted. The simulation results show that the consistency of the capacity of battery pack is mainly affected by the initial SOC and initial capacity. The influence of the two factors on capacity of battery pack consistency under different charging current rates is different. The initial internal resistance has little effect on capacity consistency under different charging current rates.

An active balancing method based on two flyback converters is proposed for series-connected battery pack. Balanced energy can be transferred between the whole battery and any single cell. The proposed topology reduces the number of energy storage components, the volume and the cost of the balancing system. And it has the characteristics of fast balancing speed and high balancing efficiency. Based on the topology, a dual-objective hybrid control strategy is proposed, which can reduce the highest voltage and boost the lowest voltage in the charging or discharging process of the cells in one sampling period, so as to improve the balancing speed. Simulation and experimental results show that the proposed method has a good balancing effect and can significantly improve the consistency of series battery pack. This work is potentially significant in terms of improved reliability of battery packs and savings of costs and lives in safety-critical applications.

Battery applications, such as electric vehicles, electric propulsion ships, and energy storage systems, are developing rapidly, and battery management issues are gaining attention. In this application field, a battery system with a high capacity and high power in which numerous battery cells are connected in series and parallel is used. Therefore, research on a battery management system (BMS) to which various algorithms are applied for efficient use and safe operation of batteries is being conducted. In general, maintenance/replacement of multi-series/multiple parallel battery systems is only possible when there is no load current, or the entire system is shut down. However, if the circulating current generated by the voltage difference between the newly added battery and the existing battery pack is less than the allowable current of the system, the new battery can be connected while the system is running, which is called hot swapping. The circulating current generated during the hot-swap operation is determined by the battery’s state of charge (SOC), the parallel configuration of the battery system, temperature, aging, operating point, and differences in the load current. Therefore, since there is a limit to formulating a circulating current that changes in size according to these various conditions, this paper presents a circulating current estimation method, using an artificial neural network (ANN). The ANN model for estimating the hot-swap circulating current is designed for a 1S4P lithium battery pack system, consisting of one series and four parallel cells. The circulating current of the ANN model proposed in this paper is experimentally verified to be able to estimate the actual value within a 6% error range.

The energy consumption of electric vehicles (EVs)depends on traffic environment, terrain, resistive forces acting on vehicle, vehicle characteristics and driving habits of driver. The battery pack in EV is the main energy storage element and the energy capacity determines the range of vehicle. This paper discusses the behavior of battery when EV is subjected to different driving environments such as urban and highway. The battery rating is selected based on requirement of driving cycle. The MATLAB/Simulink model of battery energy storage system (BESS) consisting of battery, bidirectional DC/DCconverter and electric propulsion system is built. The simulation is carried out and the performance of BESS is tested for standard driving cycles which emulate actual driving situations. It has been shown that, the amount of the energy recovered by battery during deceleration depends on the amount of regenerative energy available in the driving cycle. If the battery recovers more energy during deceleration, the effective energy consumed by it reduces and the range of the vehicle increases.

The feasibility of regional electric aviation to reduce environmental impact highly depends on technological advancements of energy storage techniques, available battery energy density, and high-power electric motor technologies. However, novel airframe technologies also strongly affect the feasibility of a regional electric aircraft. In this paper, the influence of novel technologies on the feasibility of regional electric aviation was investigated. Three game-changing technologies were applied to a novel all-electric regional aircraft: active flow control, active load alleviation, and novel materials and structure concepts. Initial conceptual design and mission analysis of the aircraft was performed using the aircraft design framework SUAVE, and the sensitivity of the most important technologies on the aircraft characteristics and performance were studied. Obtained results were compared against a reference ATR-72 aircraft. Results showed that an all-electric aircraft with airframe technologies might be designed with the maximum take-off weight increase of 50% starting from the battery pack energy density of 700 Wh/kg. The overall emission level of an all-electric aircraft with novel technologies is reduced by 81% compared to the ATR-72. On the other hand, novel technologies do not contribute to the reduction in Direct Operating Costs (DOC) starting from 700 Wh/kg if compared to an all-electric aircraft without technologies. An increase in DOC ranges from 43% to 30% depending on the battery energy density which creates a significant market obstacle for such type of airplanes. In addition, the aircraft shows high levels of energy consumption which concerns its energy efficiency. Finally, the sensitivity of DOC to novel technologies and sensitivities of aircraft characteristics to each technology were assessed.

Electric vehicles are gradually replacing some of the traditional fuel vehicles because of their characteristics in low pollution, energy-saving and environmental protection. In recent years, concerns over the explosion and combustion of batteries in electric vehicles are rising, and effective battery thermal management has become key point research. Phase change materials (PCM) can absorb or release a large amount of latent heat during the phase change process while maintaining a constant temperature (phase change temperature). In this paper, STAR-CCM+ software is used to carry out three-dimensional simulation of single cell and battery packs with PCM to investigate changing characteristics of battery temperature rise and temperature difference during the cooling and heat preservation process. At the same time, temperature rise and temperature difference of battery are analyzed under different ambient temperatures, convection heat transfer coefficients and phase change latent heats of PCMs. In summer, at an ambient temperature of 30 °C, when using PCM, the battery cell temperature can be reduced by 4 °C in 1800 s, which is about 8.6% lower than that without PCM. In winter, at an ambient temperature of −5 °C, the PCM with a melting point about 20 °C can keep the battery cell temperature drop of no more than 28% within 6700 s at a higher convection coefficient of 5 W/m2·K. Comparing the temperature of the battery pack with that of the battery cell, in the summer with an ambient temperature of 30 °C, the temperature of the battery pack decreased by 13.3 °C in 3600 s, while in the winter with an ambient temperature of −30 °C, the temperature of the battery pack increased by 14.3 °C in 5700 s. The use of phase change materials is conducive for batteries in electric vehicles to dissipate heat in summer and preserve heat in winter.

Lithium-ion batteries have been widely used in the field of energy storage, due to the high energy density, wide temperature range and long service life. However, in application, the parameters such as the capacity and voltage of each cell in the battery pack are inconsistent due to unreasonable use, poor operating environment and other factors. In this paper, the qualitative relationship between the voltage variation of lithium-ion battery and the cell capacity and SOC is firstly analyzed, and then an inconsistent cell identification method is proposed based on three feature parameters of voltage outliers, voltage range and voltage difference. The static component is obtained from the voltage in the unstable discharge data by empirical mode decomposition and the inconsistency of capacity and SOC of static components is analyzed. The online identification of inconsistency of lithium-ion battery pack is realized based on the threshold and $K$ -means algorithm.

Smart integration of the upcoming proliferation of electric forms of transport in our energy system is the key in order to make it more robust and ready for a greener future. As such, technologies as Vehicle to Home are key in order to optimize the house consumption on an individual level. This paper develops the key blocks of a modified Vehicle to Home system, where in addition to the EV, a stationary battery pack is added. The paper presents models for household consumption, bidirectional AC/DC converter, its control system, dc/dc controllers, and their controllers. The goal is to design a system capable of consuming a desired power over time from the AC grid. Any energy difference is used to charge or discharge the available energy sources. As such, optimal control strategies are developed for the control of both the charging/discharging process. The presented optimized models allow for system level simulation for several weeks on a typical computer. Using this model some allows bot sizing the dc energy storage needed, along with comparison of different control algorithms for the power converters.

In this paper, an active equalization method based on redundant battery is proposed. A redundant battery is added to a battery pack consisting of several series batteries. According to the capacity and SOC (state-of-charge) of series batteries in the battery pack, one of these batteries is in parallel with the redundant battery in a certain time period. On the one hand, the energy is transferred between the redundant battery and the equalized batteries. On the other hand, during the charging or discharging process, the redundant battery acts as a power source, which reduces the current of the equalized battery. By adjusting the length of time that redundant battery and series batteries are connected in parallel, the purpose of battery equalization will be achieved. The balance current is limited only by the maximum charge–discharge ratio of the batteries. As long as the sum of the difference of series batteries capacity is less than the redundant battery capacity, the balance of all batteries can be achieved. Compared with other active equalization methods, the energy storage element (redundant battery) is used to be the main component of the equalization circuit in this method. It not only has the advantages of simple equalization circuit and large equalization current, but also has higher energy density of battery pack. In this paper, the equalization circuit is given, and the equalization strategy with accurate battery parameters and uncertain battery parameters is proposed. The experimental results also verify the correctness of the theoretical analysis and modeling.

Cell-to-cell variations can drastically affect the performance and the reliability of battery packs. This study provides a model-based systematic analysis of the impact of intrinsic cell-to-cell variations induced by differences in initial state of charge, state of health, capacity ration, resistance and rate capability. The impact of these cell-to-cell variations was evaluated on the performance of battery packs of different topologies, from series to parallel, and chemistries. For each chemistry and topology, simulations were performed with different levels for each type of intrinsic variation as well as all variations together in order to investigate the combined effects. The most salient changes were observed for cells connected in series. The calculated incremental capacity response for the pack and the single cells was used to select different features of interest that changed depending on the type of variation. From this methodology, the automatic quantification of the variations was attempted at the pack and single cell level. Cell-to-cell variations make each battery pack unique; their quantitation is essential for accurate monitoring.

Battery storage systems are becoming more popular in the automotive industry as well as in stationary applications. To fulfill the requirements in terms of power and energy, the literature is increasingly discussing electrically reconfigurable interconnection topologies. However, these topologies use switching elements on the cell and module level that exhibit an electric resistance due to their design and hence generate undesirable dissipative losses. In this article, we propose a new analysis and optimization framework to examine and minimize the losses in such topologies. For this purpose, we develop a SystemC model to investigate static and dynamic load scenarios, e.g., from the automotive domain. The model uses SystemC TLM for the digital subsystem, SystemC-AMS for the mixed-signal subsystem, and host-compiled simulation for the microcontroller executing the embedded software. Here, we analyze the impact of the dissipative losses on the system efficiency that depend on the modularization level, implying the number of serial and parallel switching elements. Our analysis clearly shows that in reconfigurable topologies, the modularization level has a significant influence on the losses, which in our automotive example covers several orders of magnitude. For the topologies we have investigated, the highest efficiency can be reached when a parallel-only modularization is aspired and the number of serial switching elements is minimized. It is also shown that the losses of the state-of-the-art topology with one battery pack protection switch are almost as high as in a smart cell approach in which each energy storage cell has its own switching element. However, due to the high number of switching elements, this results in a reduction of energy density and increases the system costs, showing that this is a multi-criteria optimization problem.