幼女乱伦-lepetitsejour.com

google seo -> telegram: @ehseo6

">Newsnet 2022-10-07 01:13
  • home  >   /ϲϴý  >   幼女乱伦
  • ʹô0886.tvb3c1v2I-20221007.   L   W

    24СʱĶ

    ű裺ľححححjavaحaspحеحеحоحƬححɿ󹤳
    ǰλãĶ > ҵ >
    ٵ
    ҵĶ
    һרҵṩҵƵվ2002ΪڶͻṩҵơĶƵȷӮڶͻΪרערҵдʦ󲿷ȫ211/958ȸУIJʿ˶ʿƣִʣĿǰΪ5000λͻд⡣ ûΪģΪûֵվӵ޷Խӵۺϵҵɺרҵʦһһ޸ƣдͬѧһһĸ,Ϊ˳ҵݻ
    ҵ

    ʹô0886.tvb3c1v2I-20221007.   Q   A

    ʱ䣺2019/12/25 Դѧ ߣ췼
    Աȷ Ah ˲ڴϵͳ BP ȵ㷨ƣѡסм书ܣ BP 㷨ŵֲ䲻 PSO-BP 㷨 SOC
    Ϊƪģ

    ժҪ

    ȫIJ࣬ȾԴȱսͻͳʯԴҲԴͷչѾʼʶԴҪԡҺҲ̨һϵߣƶԴҵչֵ֧綯ȼϵչƶƷƽԴͬȽˮƽӹ졣

    ΪԼ綯չĹؼغ͵綯еĶԴصܺûֱӹϵ̵ij̡ʹȫԡһ׺ĵعϵͳ(Battery Management System, BMS)ܹӵ״̬м⡢ȫԽ˻ӿͨţȷ綯йõĺȫЧ˽һ׺ɿķֲʽ BMS ΪҪѡ㷨ӵغɵ״̬SOC(State Of Charge, SOC)йҲdzֵо

    SOC 㷽оͨͳ㷽ȱ㣬ֹ㷽ĿԺ;ȷԣѡȺ PSO(Particle Swarm Optimization, PSO)ŻBP(Back Propagation, BP)㷨ӵ SOC 㡣ͨƹʵ飬 MATLAB ж㷨 SOC ķ֤ PSO-BP 㷨 BP㷨иõԺ;ȷԡ C ֲɹ DSP ȶеĸ㷨롣

    ֮⣬⻹˷ֲʽ BMS ذƣ TMS320F28335 Ϊƺģ IO ·AD ɼ·SPI ͨѶ·CAN ͨѶ·ȵȡ

    ʵذ塢ɼλ֮һ׺ķֲʽ BMS

    ؼʣ綯 عϵͳ ɵ״̬(SOC) ȺŻBP(PSO-BP)

    Abstract

    With the increasing ownership of global cars , environmental pollution and shortage of resources are becoming increasingly prominent. The dependence of traditional automobile on oil resources is increasing year by year. The rise and development of new energy vehicles reveals that people from starting realize to develop and use the importance of clean energy.The state and the government also introduced a series of policies to promote energy-saving and new energy automotive industry development, puting forward to support the development of electric vehicles, fuel cell vehicles, promoting self-owned brand of energy-saving and new energy vehicles to achieve international standards.

    As a key factor to restrict the development of electric vehicles and the power source of the motor vehicle, the battery performance is directly related to the length of the vehicle mileage, service life and safety. A reasonable set of battery management system (Battery Management System, BMS) can monitor the status of lithium-ion battery, evaluate the whole vehicle’s safety, complete man-machine interface communication and ensure reasonable, safe and efficient of energy using when electric vehicles in the operation process.

    So it is very important to establish a set of reasonable and reliable distributed battery management system, and it is worth to estimate the Charge of State (SOC) of the lithium-ion battery by using a suitable algorithm.

    For the research of the SOC estimation, this paper finally chooses particle swarm PSO (Particle Swarm Optimization) (Back Propagation) to optimize the BP neural network algorithm to estimate the SOC lithium ion battery through the analysis of advantages and disadvantages of the traditional estimation method and measure the feasibility and accuracy of various estimation methods. By designing working conditions, which conducting emulation proof of SOC by using two algorithms in MATLAB. The results show that the PSO-BP algorithm is more reliable and accurate than the BP algorithm. At the end of the paper, it succeed to obtain the algorithm code that can stable operate in DSP by using C code transplant technology.

    In addition, this thesis also completed the design of the distributed BMS master control board with TMS320F28335 as the control core, integrated IO isolated output circuit, AD acquisition circuit, SPI communication circuit, CAN communication circuit and so on, which realized the combination of the main control board, the acquisition board and the upper computer, as well as designed a set of reasonable distributed BMS.

    Key wordsElectric Vehicles, Battery Management System, State Of Charge(SOC), Particle Swarm Optimization BP Neural Network(PSO-BP)

    ȫIJӣԴ漱Ҳ֮ԴΣͻȾѳΪһȫ⡣ʯԴģȾҲվǴȾȫʳ 25%ȼռʯԴ 60%ȼռͼ 1-1 ʾҹΪʯ2016 ԭͽ 2.89 ڶ֣Ϊ 59.9%ʯԴսݽǴʹԴչһҪԭ򡣼ڽͨԴļȾŷţٽͨԴתڱС

    Դ߱ȾŷŵƣԴоԼչԴЧĽԴΣͬʱҲܴٽصIJϹҵеҵͨҵķչʱԴٷչʱر 2015 9 29 գǿսѯίԱʽй 2025 ص·ͼжԴ¹滮[1,2]ǿƶҹƷԴͬȽˮƽӹ죬һӿ綯ȼϵĽʡԴʱ٣֮صļзҲΪҵȵ㡣

    УԴļ̽ܵʿĸ߶ȹעΪ綯ĶԴԼ綯չļ֮һӵӡ⡢ǦȵиȾѭʹôߡܶȸߵŵ[3]˳Ϊ綯Ԫѡһ׺ԡȫԾѵ BMS ǵ綯оҪ򡣱Ҫ˶Ե綯 BMS ļķооΪ˶Ե綯еӵؽпѧ綯ЧȶʹͨԽļĸ㷨зԱȣѡ񲢳һµ㷨Ƴһ׺ķֲʽ BMSںϲɼͨѶSOC ȹΪһ壬ʻ̵ȶԺͰȫԡ

    ڵ綯ϷչĽ죬ԶصоѾΪǹעص㣬Ϊ֤ܵĺԣɱߡĵ⣬оԱ˴ŬͨĸĸĿǰƳһӵأ෽涼ȡشչ༼ҲȡҪͻơǰΪǦأVRLAӵأNi-CdأNi-MHӣLi-ionءǦɷ Plante 1859 귢ģ 150 ꣬ӦΪ㷺Ķ[4]ϵ͵ʹڱأҳʱϳֻʹڸ䳵ɼ͵綯гϡӵŵᡢҲȾȱԭϽΪ۸ӵظߡӵصܽӡߺֽܶ࣬гռĶء

    гԶƽԴߵĺ۵Ч͹ԡ綯ԭϼ۸ķǡԼǸҵӿչгҵѽΪչĿʱͶ룬ɹdz[5]A123ҵƵӶأ2.3Ahѭʹ 1000 Σŵ70A ʹùзdzȶValenceҵ U -charge﮵أǦȣ 36%ʹʱԶԶ 2 ܶȡȶԵȷԡڽЩٰijչУڶԴҲѡӵΪṩܡ

    зƳĶؾ⣬ڶܶȡˮƽȫϵȷѾȫﵽȽˮƽ»У⶯ط 4 ܣ䴢Ȼ 95%ȥȫһӶعܶȿɴ2000W/kg Ҳȫǰ[6]ȫΧԴΣأҺҵڳöصоӴʽͶ룬ڶгDZǿȣŶʱڵܶȱؽõЧҲ᲻ӳһ걸 BMS Ҳ

    綯ʻУҪܲذѹ¶ȡʣ̡ȫϵȵȷӳʻԱҪһ׺ȫЧܵ BMS ʵЩ[7]Եصʹ״ʵʱļءݶԲɼĸֵзBMS Եذijŵ硢ȫģӦơЧ䡢ŵ֣Ϊشһ׺ BMS ǵ綯ĺģǵ綯пġԱ֤ʻȫԡԭϳɱӳʹҪ塣

    ڵо𲽽磬ҵĵ綯չѾ¼׶ΣձĻ϶һָ 90 ˾ƳƻŷҲͶʽе綯оҵ BMS оȡһЩɹȫ۱Ƚŵǣŷ PHEV˹ MODEL SBMiϵеȡ˹Tesla˾Ƴĵ綯 MODEL S綥 BMS书ܰ״̬⡢ƽе״̬ԤȹԤ

    ¹۵˾ƳԴ BMi3 Smart ϵСBMi ϵдص BMS ҲﵽˮƽͨһӵΪ乩磬״̬£̿Դﵽ 259kmʱΪ 160km/hͬʱҲɲÿ䷽ʽֻ 1h ܹɿյ״̬ 80%ϵͳصԪ͵ؼشԪɣΪṩȶijѹЧ[8]ƹ˾Ҳֿȣ¿Ϊ 135kW 𲽸Ѹ٣ǿԵؼ뿪ҲǰؿزΪ62.2 kW· hΪ400km[18]

    ҹ BMS о𲽱ȽϸߡǽõҷչսίԱĸ߶ӣҴԴҵչӴʹҹ綯ҵ٣ͬʱҲٽ˸̶ BMS ĿоȽϾдԵ 2008 걱˻רõ綯ʿص BMSɻܵӹ˾뱱ͨѧͬзģ÷ֲʽϵͳܣһϵͳаһصԪͶⵥԪҪӦڴ綯

    ǵϹ˾ͱзĴ綯춯ڹڵҲǵشص BMS ﮵ءܵѹΪ 500Vѹ 3.2Vʽ϶ϵͳܹͨԴΪṩڳʻУ90%Ķ綯ģʽ 10%״̬ҪȼͷʽһԼȼ͵󡣽ϵǰ״֪˾ӵBMSо˸߶ӣչ˴оĿҲȫƹ㹫˾µȽƷŵ綯гDZϱھ򣬶ӵ BMSоؽиΪķչռ䡣

    綯عϵͳƣ


    BMS Ӳṹͼ


    JTAG ӿڵ·


    ܵѹ·


    ܵ·


    ·ɼ·


    ɼоƬ——LTC6802


    SOC ͼ


    ֲʽ BMS ذ


    ɼ


    ӵ

    Ŀ ¼

    һ
    1.1 оĿļ
    1.2 о״
    1.3 оݼɫ֮
    1.3.1 о
    1.3.2 ⴴ֮
    ڶ ӵعϵͳ
    2.1 ӵعԭ
    2.2 ӵ
    2.2.1 ӵز
    2.2.2 ӵ
    2.3 ӵعϵͳṹ
    2.4 ӵعϵͳܷ
    2.4.1 ɼ
    2.4.2 SOC 㹦
    2.4.3
    2.4.4 ͨŹ
    2.4.5 ⹦
    2.5 С
    ֲʽعϵͳɵ״̬о
    3.1 ֲʽعϵͳ SOC 弰㷽
    3.1.1 SOC
    3.1.2 ʱ
    3.1.3 ·ѹ
    3.1.4 صѹ
    3.1.5 ˲
    3.1.6 編
    3.1.7 ȺŻ編
    3.2 PSO-BP 㷨 SOC
    3.2.1 BP 㷨ԭ
    3.2.2 PSO 㷨ԭ
    3.2.3 PSO-BP 㷨ԭ
    3.3 PSO-BP 㷨 SOC
    3.3.1 ƹʵ鼰ɼ
    3.3.2 ģ
    3.3.3
    3.3.4
    3.4 С
    ֲʽعϵͳӲ
    4.1 ֲʽعϵͳƷ
    4.2 ֲʽعϵͳӲ
    4.2.1 ư·
    4.2.2 ɼ·
    4.3 ֲʽعϵͳ
    4.3.1 ֲʽعϵͳ
    4.3.2 ϵͳʼӳ
    4.3.3 ܵѹ/ܵɼӳ
    4.3.4 ذصѹɼӳ
    4.3.5 SOC ӳ
    4.4 С
    ֲʽعϵͳӲ
    5.1 ɼݲɼ봫
    5.2 SOC ӳе
    5.3 С
    ܽչ
    6.1 ܽ
    6.2 չ
    ο
    ĺͿ˵
    л

    Ҫ鿴ƪҵȫģϵͷȡ

    رḵ̌ҵ
    ĶҪΪṩҵƼרҵҵд վͼ
    ġϾԴϵĹԴԼһЩڿ־ĽѹѼ໥ѧϰ֮ãرעǷ;
    ַİȨΪϵָĶĻиɾй!
    ô S12ӢͶעƼ ƽ̨ ͶעΧվ Ϸٷ
    Dzapp µ羺ƽ̨ ౦ٷַǶ ٷվǶ BGƽ̨עַ
    ٷַ BGƽ̨ٷվ ӢΧվ 羺Ѻע ƽ̨
    ֱĸվ lolͶעվ AGַǶ lolS12ע վ