Battery Life and Electric Vehicle Range Prediction

2022 ◽  
pp. 249-268
Author(s):  
S. Ravikrishna ◽  
Kumar C. S. Subash ◽  
M. Sundaram
Keyword(s):  
Electric Vehicle ◽  
Battery Life

Design and Implementation of Electric Vehicle Power Lithium Battery Protection Board

2010 ◽  
Vol 152-153 ◽  
pp. 192-196
Author(s):  
Ju Hua Huang ◽  
Ming Cao ◽  
Hang Guo
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Lithium Batteries ◽  
Lithium Battery ◽  
Short Circuit ◽  
Battery Life ◽  
Test Results ◽  
Charge Balance ◽  
Good Test ◽  
Li Ion

The performance of power lithium batteries directly affects the performance of electric vehicles. To ensure the safety of power lithium batteries and improve battery life, this paper uses Ricoh R5408 Series Li-ion battery protection IC to design the high-current protection board for electric vehicle, to achieve the power lithium battery group overcharge protection, over-discharge protection, over current, short circuit protection, temperature protection and charge balance protection, and has run on the pure electric vehicles with good test results.

scholarly journals Comparison of Plug-In Hybrid Electric Vehicle Battery Life Across Geographies and Drive Cycles

2012 ◽  
Author(s):  
Kandler Smith ◽  
Matthew Earleywine ◽  
Eric Wood ◽  
Jeremy Neubauer ◽  
Ahmad Pesaran
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Electric Vehicle Battery ◽  
Hybrid Electric

scholarly journals A Health Indicator for the Online Lifetime Estimation of an Electric Vehicle Power Li-Ion Battery

2020 ◽  
Vol 11 (3) ◽  
pp. 59
Author(s):  
Bin Yu ◽  
Haifeng Qiu ◽  
Liguo Weng ◽  
Kailong Huo ◽  
Shiqi Liu ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Health Management ◽  
Estimation Error ◽  
Health Indicators ◽  
Training Data ◽  
Battery Life ◽  
Li Ion Battery ◽  
Load Curve ◽  
Response Level ◽  
Li Ion

With the further development of the electric vehicle (EV) industry, the reliability of prediction and health management (PHM) systems has received great attention. The original Li-ion battery life prediction technology developed by offline training data can no longer meet the needs of use under complex working conditions. The existing methods pay insufficient attention to the dispersive information of health indicators (HIs) under EV driving conditions, and can only calculate through standard configuration files. To solve the problem that it is difficult to directly measure the capacity loss in real time, this paper proposes a battery HI called excitation response level (ERL) to describe the voltage variation at different lifetimes, which could be easily calculated according to the current and voltage under the actual load curve. In addition, in order to further optimize the proposed HI, Box–Cox transformation was used to enhance the linear correlation between the initially extracted HI and the capacity. Several Li-ion batteries were discharged to the 50% state of health (SOH) through profiles with different depths of discharge (DODs) and mean states of charge (SOCs) to verify the accuracy and robustness of the proposed method. The average estimation error of the tested batteries was less than 3%, which shows a good performance for accuracy and robustness.

scholarly journals A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3918 ◽  
Author(s):  
Yung Po Tsang ◽  
Wai Chi Wong ◽  
G. Q. Huang ◽  
Chun Ho Wu ◽  
Y. H. Kuo ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Life Cycle ◽  
Electric Vehicle ◽  
Product Life Cycle ◽  
Sustainable Transportation ◽  
Battery Pack ◽  
Transport Systems ◽  
Battery Life ◽  
Inference System ◽  
Product Life

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

scholarly journals Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

2020 ◽  
Vol 12 (24) ◽  
pp. 10537
Author(s):  
Jin Li ◽  
Feng Wang ◽  
Yu He
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Carbon Emissions ◽  
Vehicle Routing Problem ◽  
Hill Climbing ◽  
Neighborhood Search ◽  
Battery Life ◽  
Routing Problem ◽  
Crossover And Mutation

In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

Optimizing for Efficiency or Battery Life in a Battery/Supercapacitor Electric Vehicle

2012 ◽  
Vol 61 (4) ◽  
pp. 1526-1533 ◽  
Author(s):  
Rebecca Carter ◽  
Andrew Cruden ◽  
Peter J. Hall
Keyword(s):  
Electric Vehicle ◽  
Battery Life
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