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(a) battery pack of Mitsubishi i-MiEV; (b) battery pack of VW e-Up; (c) battery pack of smart fortwo electric drive. Note: scaled differently.
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This paper focuses on the hardware aspects of battery management systems (BMS) for electric vehicle and stationary applications. The purpose is giving an overview on existing concepts in state-of-the-art systems and enabling the reader to estimate what has to be considered when designing a BMS for a given application. After a short analysis of gene...
Contexts in source publication
Context 1
... first example is the traction battery of a Mitsubishi i-MiEV (initial registration: February 2014), shown in Figure 4a. It contains 10 Modules of eight cells and two modules with four cells, which leads to a total amount of 88 prismatic cells, all connected serially using screwed contacts. On top of each of the modules, a PCB is mounted, which-among other things-contains an LTC6802G-2. This IC is designed to monitor up to 12 lithium-ion cells, which are connected in series. The same PCB design is used for the module versions with four and eight cells. When used with four cells, the PCB is not fully populated, as four of eight available channels are not needed. The eight-cell modules use a second PCB to connect the second half of the module to the four remaining channels. The PCB on top of the modules is called the Cell Management Unit (CMU) in the official service manual for the car [30]. In addition to voltage measurement, each PCB contains three temperature sensors, which are connected to a controller located next to the Linear Technology BMS ...
Context 2
... second example is a battery pack taken from a smart fortwo electric drive (third generation, initial registration: September 2014), built by Deutsche Accumotive, which is shown in Figure 4c). It consists of 90 pouchbag cells, connected in series by welded connections. The cells are mounted in plastic frames, organized in three rows positioned side by side with cooling plates for a liquid cooling system mounted on ...
Context 3
... e-Up battery does not have a cooling system or a service disconnect, dividing the battery into two halves. The BMS modules are centralized, and the white box on the left side of Figure 4b) contains the measurement ICs (or BMS slave) for the whole battery pack. To the right of it, below a black cover, the contactors, fuse and current measurement can be found. The other white box contains some kind of BMS master. A large amount of voltage measurement wires connects the individual cells to the slave module, where Maxim's MAX11068 [3] is used for the measurement and balancing tasks with MAX11081 [8] as a secondary protection device. A closer look at the PCB shows that nine MAX11068 (12 voltage measurement channels each) are daisy-chained via I2C, which is also used to connect the last IC to the rest of the system. There is no microcontroller converting to a more robust field bus, like e.g., CAN. Apart from that, the slave's PCB is filled with a large amount of balancing resistors taking up most of the ...
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... The rapid expansion of industries in modern civilization and excessive use of non-conventional energy sources lead to the energy crisis that suspiciously affects rural communities and also impacts the environment [1,2]. So, the developing countries are deciding to generate the power through renewable energy sources, in addition to creating the new imbalance in the state of charge (SOC), resulting in an unstable DC bus voltage [12][13][14][15]. To tackle the mentioned concerns, a proposed intelligent energy management system aims to enhance the performance of small-scale microgrid systems. ...
... This iterative process continues until the output value at the output layer accurately predicts the desired outputs, thereby minimizing the training error. Fundamentally, the mean-squared error (MSE) acts as the cost function, which can be mathematically represented by Eq. (14). ...
This research paper focuses on an intelligent energy management system (EMS) designed and deployed for small-scale microgrid systems. Due to the scarcity of fossil fuels and the occurrence of economic crises, this system is the predominant solution for remote communities. Such systems tend to employ renewable energy sources, particularly in hybrid models, to minimize fuel costs and promote environmental sustainability. However, in small-scale microgrids, a significant challenge lies in maximizing power utilization amidst rapid variations in ecological conditions in renewable energy resources, ensuring energy balance during peak demand, and preventing wastage during low demand condition. To address these issues, this research focuses on two main areas. Firstly, the implementation of the GWO-tuned feed-forward neural network MPPT algorithm in both solar and wind energy conversion systems. This control algorithm demonstrates superior performance compared to existing controllers by efficiently tracking the maximum power point (MPP) value and rapidly utilizing the available power. Secondly, IoT-based energy monitoring system is implemented in small-scale microgrid systems to track the real time of data from sources like wind, solar, and batteries. Furthermore, intelligent rule-based strategies are employed to enhance the control function of EMS and ensure stability within the microgrid. This system effectively manages microgrid demand and prevents power wastage. In this specific EMS setup, the battery storage unit is a key component, but challenges arise when there are sudden load and power generation fluctuations, leading to disruptions in control mechanisms. To address this, a GWO-tuned ANN controller is integrated into the voltage control loop of the battery controller unit, effectively correcting DC bus voltage fluctuations and maintaining stability. The entire work has been designed, and system performances are analyzed in the MATLAB Simulink environment and compared with existing work. The simulation results have been validated by means of experimental setup.
... To better understand these degradation phenomena and make the battery work under optimal conditions, which can increase its efficiency and mitigate aging, it is very important to develop battery models that are accurate yet easy to implement in battery management systems (BMSs). In this way, BMSs can forecast the battery behavior by estimating its parameters and adequately controlling the operating conditions [21][22][23]. ...
Currently, the urgent needs of sustainable mobility and green energy generation are driving governments and researchers to explore innovative energy storage systems. Concurrently, lithium-ion batteries are one of the most extensively employed technologies. The challenges of battery modeling and parameter estimation are crucial for building reliable battery management systems that ensure optimal battery performance. State of charge (SOC) estimation is particularly critical for predicting the available capacity in the battery. Many methods for SOC estimation rely on the knowledge of the open-circuit voltage (OCV) curve. Another significant consideration is understanding how these curves evolve with battery degradation. In the literature, the effect of cycle aging on the OCV is primarily addressed through the look-up tables and correction factors applied to the OCV curve for fresh cells. However, the variation law of the OCV curve as a function of the battery cycling is not well-characterized. Building upon a simple analytical function with five parameters proposed in the prior research to model the OCV as a function of the absolute state of discharge, this study investigates the dependency of these parameters on the moved charge, serving as an indicator of the cycling level. Specifically, the analysis focuses on the impact of cycle aging in the low-, medium-, and high-SOC regions. Three different cycle aging tests were conducted in these SOC intervals, followed by the extensive experimental verification of the proposed model. The results were promising, with mean relative errors lower than 0.2% for the low- and high-SOC cycling regions and 0.34% for the medium-SOC cycling region. Finally, capacity estimation was enabled by the model, achieving relative error values lower than 1% for all the tests.
... The SoC FC of the state observer shown in Fig. 7c was directly determined from the positive electrode average concentration. At 3 ⃝the observer is activated at the end of the relaxation phase. The accurate estimation results observed confirm the hypothesis that initialization at zero current facilitates fast recovery of all states. ...
Optimal utilization of the safe operating area of lithium-ion battery cells is fundamental to achieving peak performance. Pushing the currents close to the intrinsic electrochemical cell limits during fast charging of battery electric vehicles reduces charging times noticeably and improves customer satisfaction. With electrochemical model-based state observers embedded in the battery management system, predicting the critical anode potential is feasible. This allows the charging process to be adjusted accordingly for effective prevention of lithium plating. However, validating the model, especially the internal potentials, is particularly challenging, and the real-time capability is often doubted. This work addresses the parameterization and experimental validation of a full-order electrochemical model utilizing cells instrumented with micro-reference electrodes. Special emphasis is put on validating the estimate of the anode surface potential, which enables the model to act as a virtual reference electrode. For the first time, it is shown how this can be efficiently implemented on a next-generation automotive microcontroller, in combination with a charging current controller, for online adaptive anode potential-controlled fast charging. Execution times well below 4 ms prove real-time capability, and reference electrodes validate good agreement between model estimate and measurements in closed-loop charging current control.
... This system is responsible for the measurement and monitoring of battery parameters, as well as the regulation of battery operation. The BMS is a compact electronic device that facilitates the regulation and supervision of the battery system, therefore guaranteeing both the safety and efficiency of battery operations [12,13]. Nevertheless, it is worth noting that the power and energy attributes of LIBs experience a decline over time due to ageing [14]. ...
Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system’s modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery’s efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation.
... However, this issue may be ameliorated by utilizing an on-board energy generator that operates simultaneously with the car. An example of this is a solar power plant embedded in the exposed body of the car [26]- [28]. With electric cars considered to be a solution to reduce gas emissions [29], the addition of the solar power plant on electric cars further supports the movement for a healthier environment and helps promote investments in the renewable energy sector [30], [31]. ...
This study aims to determine the reliability of applying a thermal management system in conjunction with Internet of Things in solar electric cars. In conventional electric cars or those whose driving energy source comes from gasoline fuel; the applied thermal management system is mainly used as a coolant for the internal combustion engine. However, for electric cars the thermal management system may be used for the main components such as controllers that convert solar module energy into electricity and batteries. Results from tests utilizing six DC fans for air cooling of the thermal management system yield two variations of battery charging conditions from the solar modules, namely variations of 25 and 400 turns of the trimmer constant current step-up charger. Test results from the proposed thermal management system show that the highest step-up charger temperature is 35.75 °C with voltage of 57.64 V for the variation of 25 laps. The test results on the battery voltage and temperature show that the highest battery temperature reaches 31.75 °C with voltage of 57.3 V at the variation of 25 rounds.
... Apart from other features, it provides battery state estimation, contactors control, and the measurement of physical cell states such as voltage, current, and temperature. Integral to a BMS is its ability to balance battery cells, which aims to achieve uniform state-of-charges (SoCs) across cells 1 . Presently, there are two main approaches to balancing: dissipative and non-dissipative systems. ...
... This leads to 10.4 s excitation time for one complete sweep of all frequencies. The overall excitation time could be reduced according to Küpfmüller's uncertainty principle to 1 2 Hz ¼ 0:5 s by using broadband excitation signals. As derived in Equation (9), broadband signals always lead to a lower SNR. ...
Active dissipative balancing systems are essential in battery systems, particularly for compensating the leakage current differences in battery cells. This study focuses on using balancing resistors to stimulate battery cells for impedance measurement. The value of impedance spectroscopy for in-depth battery cell diagnostics, such as temperature or aging, is currently being demonstrated and recognized by vehicle manufacturers, chip producers, and academia. Our research systematically explores the feasibility of using existing balancing resistors in battery management systems and identifies potential limitations. Here we propose a formula to minimize hardware requirements through signal processing techniques. A quadrupling of the sampling rate, number of averaging values, or the size of the fast Fourier transform is equivalent, concerning the signal-to-noise ratio, to increasing the analog resolution by one bit or reducing the input filter bandwidth by a quarter.
... During the charge and discharge process, the continuous release of chemical reaction heat and the production of resistance heat inside the battery can lead to elevated and uneven temperatures within the battery and its connections [6]. Typically, the loading density of batteries inside the storage tank is high, and temperature deviations can not only impact the charging and discharging processes but also result in unbalanced performance among individual batteries, thereby affecting the overall efficiency of the battery compartment [7]. ...
... individual batteries, thereby affecting the overall efficiency of the battery compartment [7]. This can potentially lead to uncontrolled temperature, internal leakage, combustion, and even explosions [8]. ...
This study investigates the airflow and thermal management of a compact electric energy storage system by using computational fluid dynamic (CFD) simulation. A porous medium model for predicting the flow resistance performance of the battery modules in a battery cabinet is developed. By studying the influence of rack shapes, the effects of heat exchanger arrangements and other parameters on the airflow and battery thermal distribution are analyzed. When applying a larger bottom air channel, the inlet flow uniformity of each battery cabin in the cabinet increases by 5%. Meanwhile, temperature standard deviation decreases by 0.18 while raising the flow rate from 3 m/s to 8 m/s, indicating better temperature uniformity in the battery cabin. When the charge–discharge ratio reaches 0.5 C, the temperature deviation of the entire cabinet significantly increases, reaching 8 K. Furthermore, a rack-level thermal management scheme is proposed to effectively reduce the thermal deviation of the container electric energy storage system and improve the overall temperature uniformity. Results reveal that the rack-level thermal management of the wavy cabinet in the electric storage container can effectively improve the thermal uniformity of the distributed battery cabin, and the overall thermal deviation is controlled within 1.0 K.
... Subsystems of the BMS, namely electrical, thermal, and safety management, govern these parameters. In order to function appropriately, the BMS necessitates certain measured and calculated inputs such as current, voltage, temperature, and battery state (State of Charge -SoC-, State of Health -SoH-, State of Power -SoP-, and etc.) [1][2][3][4]. Battery metrics like SoC, SoP, and SoH become increasingly critical, especially in electric vehicles. SoC and SoP indicate the short-term state of the battery, while SoH indicates the long-term state, and they affect each other [5,6]. ...
... Charging the battery can be done optimally when the engine rotation is optimal or proportional to the power that has been issued by the battery to carry out electrical functions. The problem that arises is that motorized vehicle users do not know the condition of the battery which has reached a voltage level below the average of 85% [21][9], [22][9], [23], [24]. If there is a problem with the regulator, then the charging current in the battery becomes excessive. ...
... Usability, consumption control, reliability and safety issues require a comprehensive understanding of battery performance and health, and the development of new measurement techniques becomes essential. The design of battery management systems (BMS) with a distributed architecture that can perform monitoring at single-cell level, requires the development of low-complexity and easily scalable instrumentation and diagnostic techniques [3,4]. ...
... The impedance of a resistor and of an inductor is respectively a constant and a linear function of frequency, as in (2) and (3). Of course, ...
In this study we define a comprehensive method for analyzing electrochemical impedance spectra of lithium batteries using equivalent circuit models, and for information extraction on state-of-charge and state-of-health from impedance data by means of machine learning methods. Estimation of circuit parameters typically implies a non-linear optimization problem. A detailed method for estimating initial values of the optimization algorithm is described, emphasizing short computation times and efficient convergence to global minimum. Parameters identifiability is investigated through an analysis of the injectivity of the model, Cramer–Rao lower bound, profile likelihood, and sensitivity analysis. An exploratory data analysis is presented to estimate the degree of correlation between impedance spectra (or circuit parameters) and battery state-of-charge or state-of-health, prior to the implementation of any machine learning algorithm. A publicly available dataset of impedance spectra of five lithium-polymer batteries is used to test the whole procedure. Estimation of state-of-charge and state-of-health is performed by implementing Gaussian process regression.
