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Electric vehicle technology
Published in V.S.K.V. Harish, Amit Vilas Sant, Arun Kumar, Renewable Energy Integration with Building Energy Systems, 2022
Arpit J. Patel, Chaitali Mehta, Ojaswini A. Sharma, Amit V. Sant, V.S.K.V. Harish
Depending on the physical location of the components of the charger utilized to charge the electric vehicle battery from the grid, chargers can be categorized as on-board or off-board. On-board chargers are located within the vehicle, and the available space within the vehicle determines the size and power rating of the charger. Off-board chargers are located outside the vehicle. More flexibility is available in off-board chargers in terms of deliverable power. Both on-board and off-board chargers must contain control circuits and need to communicate with the vehicle battery to ensure optimum charging of the battery. In this way, damage sustained by the battery due to overcharging can be completely avoided. On-board charging uses a slow ac charger, and off-board charging uses a dc fast charger. An inductive charger uses a combination of on-board and off-board chargers. Figure 6.3 depicts the concept of on-board and off-board chargers. The ac chargers are connected to the on-board charger. The on-board charger supplies power to the battery via a BMS. The task of the BMS is to protect the battery from overcharging.
Batteries Charge Controller and Its Technological Challenges for Plug-in Electric and Hybrid Electric Vehicles
Published in Thandavarayan Maiyalagan, Perumal Elumalai, Rechargeable Lithium-ion Batteries: Trends and Progress in Electric Vehicles, 2020
BMS stands for Battery Management System, and is an electronic device utilized wherever Li-ion chemistry cells are in operation – appliances, electric vehicles or general energy storage. The purpose of a BMS is to: Provide battery safety and longevity, a prerequisite for Li-ion.Determine state-of-function in the form of state-of-charge and state-of-health.Caution against any imminent errors. This could be high temperature, cell imbalance or calibration.Indicate end-of-life when the capacity falls below the user-set target threshold.
Batteries
Published in Larry E. Erickson, Gary Brase, Reducing Greenhouse Gas Emissions and Improving Air Quality, 2019
Batteries are equipped with a battery management system (BMS) that ensures the battery is running safely and helps to protect it. Each charge and discharge has an impact on battery life span. When a battery cell is fully charged, it is at 100% state of charge (SoC). When a battery is fully discharged, it is said to be at 100% depth of discharge (DoD) (Arcus, 2016). Consistently taking a battery to 100% SoC or DoD is not good for the health of the battery, so a BMS prevents the battery from reaching those points. The BMS also “ensures that the energy of the battery is optimized to power the product” and “that the risk of damaging the battery is minimal” (Hu, 2012).
Effect of Noise Covariance Matrices on State of Charge Estimation Using Extended Kalman Filter
Published in IETE Journal of Research, 2022
The BMS can monitor the battery terminal voltage, current, and temperature to prevent overcharging and over-discharging conditions. State estimation can be performed with these measured parameters. The state of charge, indicating the percentage of charge currently available, allows safe charging and discharging of a battery. Thus, SOC helps in battery management especially cell balancing, fault diagnosis, and ensures safe use. Direct state estimation approaches include the Ampere hour (Ah) integral method and the open circuit voltage (OCV) method are commonly employed to estimate SOC. To estimate SOC, an Ah approach integrates charging and discharging current across time [15]. Since it is simple to implement, inaccurate unknown initial SOC and sensor error makes this method insufficient for EV. OCV method uses one to one relationship between OCV and SOC. It is simple and quick to install, but it requires a long rest period, making it unsuitable for EV [16]. Model-based SOC estimation is more powerful than the aforementioned methods [6]. Model-based SOC estimation methods comprise battery modeling, parameter correction, and state estimation.
An Adaptive Algorithm for Battery Charge Monitoring based on Frequency Domain Analysis
Published in IETE Journal of Research, 2021
Poulomi Ganguly, Surajit Chattopadhyay, B.N Biswas
Current needs to be monitored to ensure that everything is being operated within a safe range. Battery capacity can be degraded if overcharging occurs, as well as if discharging rate is high. The current amount during charging can be monitored to ensure that it does not cross the permissible limit. If the battery charge level gets too low, a properly designed battery management system can communicate the information to a controller to avoid such low values of cell voltage. From the earlier figures, it is evident that the parameters considered here for monitoring undergo variation with degradation of charge level. The charge level monitoring process can be outlined by the following algorithm as shown in Figure 11. Performance of the algorithm using automotive data has been found satisfactory in up to 42%, then accuracy decreases gradually. The scale is adaptive and requires prior learning for different automotive applications. A comparison with some previous methods has been provided in Table 2, which shows monitoring is possible at starting without the use of any filter.
High Fidelity Equivalent Circuit Model Parameter Extraction for Characterization and Simulation of Li-Ion Cells in Battery Electric Vehicles
Published in Electric Power Components and Systems, 2018
Venu Sangwan, Avinash Sharma, Rajesh Kumar, Akshay K. Rathore
The emission of greenhouse gases from fossil-fuel powered vehicles has increased global warming and degraded the air quality. Therefore, the battery electric vehicles (BEVs) have been encouraged in the transportation sector in recent years [1]. The battery has been extensively utilized in BEVs as electric energy storage. Lithium-ion (Li-ion) technology is regarded as the most promising energy storage solution owing to its inherent merits of high energy density, light weight, long cycle life, and extremely low self-discharge rate [2, 3]. For the real-time application such as BEVs, high power and high voltage battery packs (hundreds of battery cells connected in series/parallel configuration) are required. An accurate and efficient battery management system (BMS) is necessary for the safe and reliable functioning of BEVs and operating battery within safety margins [4]. The primary function of BMS is to monitor and maintain voltage, current, and temperature within limits and estimate the online status of the battery such as State of Charge (SOC) and State of Health (SOH) to enhance the battery life. Since the states of the battery cannot be measured directly by physical sensors, an accurate dynamic model of the battery is required for estimation of these states [5].