环保锂离子动力电车将在香港面世

新一代锂离子动力电他的发明者钟馨稼日前在香港表示,他在香港成立的国家新能源有限公司计划与美、日投资者合作,在香港投资设厂,改装环保电动车。

SOC estimation of lithium-ion power battery for HEV based on advanced wavelet neural network

In order to improve the estimation accuracy of the battery＇s state of charge（SOC） for the hybrid electric vehicle（HEV）,the SOC estimation algorithm based on advanced wavelet neural network（WNN） is presented.Based on advanced WNN,the SOC estimation model of a lithium-ion power battery for the HEV is first established.Then,the convergence of the advanced WNN algorithm is proved by mathematical deduction.Finally,using an adequate data sample of various charging and discharging of HEV batteries,the neural network is trained.The simulation results indicate that the proposed algorithm can effectively decrease the estimation errors of the lithium-ion power battery SOC from the range of ±8% to ±1.5%,compared with the traditional SOC estimation methods.

Experimental study on heat generation and dissipation performance of PEV Lithium-ion battery

Based on the lithium-ion battery pure electric vehicle （PEV） application, two capacity types of batteries are applied in thermal characteristic experiments. With the experimental comparison method, battery thermal characteristics and heat generation mechanism are studied. Experiments of batteries in cases of different dimensions, batteries with different air cooling velocity and two capacity types of batteries in free convection environment are put forward. Battery heat generation performance, heat dissipation performance and comparison of different capacity types＇ batteries are researched and summarized. Conclusions of battery heat generation and dissipation in PEV applications, important battery thermal management factors and suggestions are put forward.

Online model identification of lithium-ion battery for electric vehicles

In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications,a battery model with a moderate complexity was established.The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation.An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery.A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model.The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration.The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery.The maximum and mean relative errors are 1.666% and 0.01%,respectively,in a hybrid pulse test,while 1.933% and 0.062%,respectively,in a transient power test.The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.

Analysis of Adverse Effects on Performance due to Battery Deterioration Installed in BEV and HEV

This paper reports the results of investigating the permissible amount of battery deterioration. An investigation was carried out using the following two types of vehicles： a BEV （battery electric vehicle） and a HEV （hybrid electric vehicle）. First, a detailed evaluation was carried out to identify how the vehicle performance was adversely affected as the lithium-ion batteries installed in the vehicles deteriorated. Next, an attempt was made to determine the permissible amount of deterioration for the vehicle-mounted lithium-ion batteries. In the case of the BEV, the driving distance declined by 20% when the capacity maintenance rate was approximately 80%. Therefore, this was specified as the permissible amount of battery deterioration for the BEV. In the case of the HEV, the fuel consumption increased by 20% when the maximum battery output maintenance rate was approximately 40%. Therefore, this was specified as the permissible amount of battery deterioration for the HEV.

Recursive modeling and online identification of lithium-ion batteries for electric vehicle applications

For safe and reliable operation of lithium-ion batteries in electric vehicles,the real-time monitoring of their internal states is important.The purpose of our study is to find an easily implementable,online identification method for lithium-ion batteries in electric vehicles.In this article,we propose an equivalent circuit model structure.Based on the model structure we derive the recursive mathematical description.The recursive extended least square algorithm is introduced to estimate the model parameters online.The accuracy and robustness are validated through experiments and simulations.Real-road driving cycle experiment shows that the proposed online identification method can achieve acceptable accuracy with the maximum error of less than 5.52%.In addition,it is proved that the proposed method can also be used to estimate the real-time SOH and SOC of the batteries.