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Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance under each POM. Potential...
—The peak power capability of lithium-ion batteries (LIBs), or so-called state of power (SOP), plays a decisive role for electric vehicles to fulfill a specific power-intensive task. Generally, battery SOP can be achieved based on different peak operation modes (POMs), including constant current, constant voltage, constant current and
In this paper, with 2.75Ah ternary Li-ion battery as the research object, the test efficiency and accuracy of the current peak power test methods (HPPC, JEVS and constant current charge and discharge ) are compared at different temperatures, through optimizing the JEVS, the test results meet the requirements at different time scales on the basis...
To address the issue, this paper mainly investigates four different peak current solution algorithms, including bisection method, genetic algorithm method, particle swarm
As a crucial indicator of lithium-ion battery performance, state of power (SOP) characterizes the peak power capability that can be delivered or absorbed within a short period of time. Accurate SOP estimation is therefore essential for electric vehicles to ensure their safe
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Abstract—In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is pre-sented for use with battery state estimation (BSE) algorithm development; particular focus is given to state-of-power (SOP) or peak power availability reporting.
Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance under each POM. Potential factors, such as load profile, length of the prediction window and battery chemistry, are considered in the comparisons. The results offer valuable
Based on the ECM, this paper proposes a battery peak power prediction method based on online parameter identification and state estimation. The power that a battery can continuously provide is related to its terminal voltage, SOC, and its own charging and discharging capacity. Therefore, the power prediction method proposed in this paper mainly
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As a crucial indicator of lithium-ion battery performance, state of power (SOP) characterizes the peak power capability that can be delivered or absorbed within a short period of time. Accurate SOP estimation is therefore essential for electric vehicles to ensure their safe and efficient operations during power-intensive driving tasks.
To address the issue, this paper mainly investigates four different peak current solution algorithms, including bisection method, genetic algorithm method, particle swarm optimization method, and grey wolf optimizer (GWO) method for
Abstract—In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is pre-sented for use with battery state estimation (BSE) algorithm
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Online estimation of peak power capability of Li-Ion batteries in electric vehicles by a hardware-in-loop approach
Battery type. Nearly every rechargeable power bank you can buy (and most portable devices) contain a lithium-ion battery. These beat other current battery types in terms of size-to-charge capacity
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You travel a lot and need power: We never take flight without the Anker 733 in our carry on luggage replaces multiple wall chargers and gives us a large battery on the go. You carry a small
Based on the ECM, this paper proposes a battery peak power prediction method based on online parameter identification and state estimation. The power that a battery can continuously provide is related to its terminal
In this paper, with 2.75Ah ternary Li-ion battery as the research object, the test efficiency and accuracy of the current peak power test methods (HPPC, JEVS and constant
—The peak power capability of lithium-ion batteries (LIBs), or so-called state of power (SOP), plays a decisive role for electric vehicles to fulfill a specific power-intensive task. Generally,
为解决复合脉冲和日本电动汽车协会标准 (JEVS)峰值功率测试方法在放电低端和充电高端易达到截止电压的问题,提出了一种混合脉冲功率特性 (HPPC)测试和反向传播 (BP)神经网络相结合的峰值功率测试方法,在保证准确率的条件下快速获得放电低端和充电高端的峰值功率值。 在此基础上,建立了一种以比能量和比功率为核心的电池性能评价体系,提出了基于德尔菲法确定权重系数的电
Online estimation of peak power capability of Li-Ion batteries in electric vehicles by a hardware-in-loop approach
为解决复合脉冲和日本电动汽车协会标准 (JEVS)峰值功率测试方法在放电低端和充电高端易达到截止电压的问题,提出了一种混合脉冲功率特性 (HPPC)测试和反向传播 (BP)神经网络相结合的
Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance under each POM. Potential...
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Four key indices, including maximum and minimum instant magnitudes, time-averaged magnitude and falling/rising rate, are adopted to evaluate battery peak performance
The peak power of the battery (SOP) is an important parameter index for electric vehicle to improve the efficiency of battery utilization and ensure the safety of the system in the maximum limit. The estimation and prediction of SOP is based on a large number of test data at different temperature, different SOC and different time scales.
When the SOC of the battery is 70%, the predicted peak current is 117.4 A, with a relative error of 4.5%; When the SOC of the battery is 50%, the predicted peak current is 101.6 A, with a relative error of 8.1%; When the SOC of the battery is 20%, the predicted peak current is 40.34 A, with a relative error of 5.0%.
The applicability of the optimized JEVS test method in the study of the peak power test of lithium ion batteries is analyzed based on the experimental results of different test methods. 2. Test methods for peak power 2.1. HPPC test According to the Freedom CAR Battery Test Manual , 1C charge for 10s, reset 40s, 4C/3 discharge 10s.
In this paper, the research object is 2.75Ah lithium ion battery. Peak current can be directly characterized by the peak power, so we use HPPC, optimized JEVS and constant current charge/discharge to test the battery peak current between 5%SOC and 95%SOC at different duration in 10℃, 25℃ and 45℃.
The reference value of the battery peak power is obtained by multiplying the peak discharge current by the battery terminal voltage at the end of discharge. The experimental results of reference values at 70%, 50%, and 20% SOC are shown in Table 3.
By fitting the curve, the peak discharge current reference value of the battery during the predicted time can be obtained. The reference value of the battery peak power is obtained by multiplying the peak discharge current by the battery terminal voltage at the end of discharge.
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