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Ran L.,Harbin University of Science and Technology | Junfeng W.,Harbin University of Science and Technology | Gechen L.,Harbin University of Science and Technology | Gechen L.,CENS Energy Technology Co.
Lecture Notes in Electrical Engineering | Year: 2012

It has a strong request to real-time communication and sharing the information between each subsystem of hybrid electric vehicle (HEV). The mainstream of constructing network of hybrid electric vehicle applies the CAN Bus system. It follows the standard SAE J1939 protocol, uses the characteristics of the motor and inverter. In order to achieving superior braking, the total braking moment is divided into energy regeneration braking, mechanical breaking and ABS breaking, according to the actual situation, which are divided into different proportion. Finally, in the road test on the actual data collection, verification of the hybrid electric vehicle CAN bus control network hardware and software modules designed with the academic value and practical application. © 2012 Springer-Verlag GmbH.


Yu Z.,Northeast Forestry University | Yu Z.,Harbin University of Science and Technology | Guo Y.,Northeast Forestry University | Hao Z.,Harbin University of Science and Technology | Li G.,CENS Energy Technology Co.
ICIC Express Letters, Part B: Applications | Year: 2013

The equivalent circuit of battery on the foundation of electronic movement theory is established, as well as the temperature revision on the model, in order to conduct real-time monitoring of state of charge (SOC) and the relationship between SOC and temperature of battery. Extended Kalman filter (EKF) algorithm is included in the method to conduct prediction on SOC of battery, and the state observer on SOC is established to conduct simulated condition experiment on lithium-iron-phosphate power battery under environment of 20±2°C and -20±2°C. The results have indicated that the observer of dynamic system on battery constructed by the revised model can conduct effective compensation to SOC, and that the max error of SOC under different temperature after revised is 1.36% and 2.7% respectively. © 2013 ISSN 2185-2766.


Wang H.,Harbin University of Science and Technology | Liu P.,Harbin University of Science and Technology | Zhao Y.,Harbin University of Science and Technology | Du L.,Harbin University of Science and Technology | Li G.,CENS Energy Technology Co.
International Journal of Control and Automation | Year: 2016

In the actual application process, power battery may not behave properly, such as insufficient charging, internal resistance increment, capacity reduction and etc, the paper establishes a fuzzy comprehensive evaluation model of power battery to diagnose judge and analyze. The diagnostic results are given in the form of battery health status DOH and maintenance information. The paper tests the model through Actual working conditions of electric vehicle, and the results show that the actual fault information is consistent with the diagnostic results of model. The model is reasonable, and it can be used to diagnose the faults of power battery. © 2016 SERSC.


Li R.,Harbin University of Science and Technology | Wu J.-F.,Harbin University of Science and Technology | Wang H.-Y.,Harbin University of Science and Technology | Guo J.-Y.,Harbin University of Science and Technology | And 2 more authors.
Information Sciences | Year: 2014

In this paper, we present experimental data on the resistance, capacity, and life cycle of lithium iron phosphate batteries collected by conducting full life cycle testing on one type of lithium iron phosphate battery, and we analyse that data using the data mining method of pattern recognition. We also predict battery reliability using cluster analysis. A strategy for enhancing the reliability of lithium iron phosphate batteries is proposed based on a statistical analysis and study of the macromechanism of product failures. We show in practice that the average life cycle of a battery is increased by 45.5% after adopting a new strategy that we suggest. The strategy is effective for mass-producing reliable lithium iron phosphate batteries and instructive for improving the industry of lithium iron phosphate battery production, as well as the quality of its products. © 2013 Elsevier Inc. All rights reserved.


Wang H.,Harbin University of Science and Technology | Liu S.,Harbin University of Science and Technology | Sun T.,Harbin University of Science and Technology | Li G.,Harbin University of Science and Technology | Li G.,CENS Energy Technology Co.
International Journal of Smart Home | Year: 2013

In dealing with the increment in environment pollution and source consumption, research has focused on the application of renewable energy source. Batteries, especially power batteries, which has great prospects in the fields, are among the attention. Rechargeable batteries are widely used in many electrical systems to store and deliver energy. However, there is a wide variety of Power Batteries and they have different weak Points. In order to develop and apply battery in a more efficient and appropriate method, their response to various operating conditions must be understood. Knowing the battery temperature variation in electric vehicles (EVs) is very important issue. Temperature depends on ambient temperature, charging current and charging time. Recently neural networks have been successful used for power system applications. In the literature, there are many neural networks for power system applications. However, Back Propagation (BP) has demonstrated better capabilities. This paper presents neural network for temperature estimation of power batteries. The main contribution of this paper is consideration of non-uniform temperature field and the temperature effect in batteries. In addition, the results of estimation and actual measured values are compared, proving the feasibility and accuracy of the method.

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