Extended range electric vehicle energy management method on basis of fuzzy control

A technology of fuzzy control and energy management, applied in electric vehicles, control drives, vehicle components, etc., can solve the problems of difficult control effect, inability to dynamically change the control effect, and inability to achieve the best vehicle economy, and achieve economical efficiency. Enhancement, strong adaptability and good robustness

Inactive Publication Date: 2013-03-06
TONGJI UNIV
2 Cites 30 Cited by

AI-Extracted Technical Summary

Problems solved by technology

Although this type of strategy is easy to implement, its control effect cannot be dynamically changed with the working conditions and vehicle status. The working power of the ...
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Abstract

The invention relates to an extended range electric vehicle energy management method on the basis of fuzzy control, which particularly comprises the following steps that: 1, a vehicle control unit acquires a storage battery SOC (State of Charge) and bus demand power data; 2, the vehicle control unit judges whether the storage battery SOC is less than or equal to 90 percent, the step 3 is executed if yes, and if no, a range extender is controlled to be switched off by a CAN (Controller Area Network) bus; 3, a fuzzy control module carries out fuzzification on the storage battery SOC and the bus demand power data according to a membership function; 4, fuzzy reasoning is carried out on the fuzzified data according to a set fuzzy rule and the membership function; 5, defuzzification is carried out on a reasoning result by utilizing a centroid method and output power distribution values of the range extender and a storage battery are output; and 6, the vehicle control unit sends the output power distribution values of the range extender and the storage battery to the range extender and the storage battery by the CAN bus. Compared with the prior art, the extended range electric vehicle energy management method has the advantages of strong adaptability, capability of promoting the whole vehicle performance and the like.

Application Domain

Speed controller

Technology Topic

CAN busArea network +11

Image

  • Extended range electric vehicle energy management method on basis of fuzzy control
  • Extended range electric vehicle energy management method on basis of fuzzy control

Examples

  • Experimental program(1)

Example Embodiment

[0032] Example
[0033] like figure 1 As shown, a fuzzy control-based energy management method for an extended-range electric vehicle, the electric vehicle includes a vehicle controller (VMS), a CAN bus, a range extender and a storage battery, and the vehicle controller passes through the CAN bus Connected with the range extender and the battery, the vehicle controller is provided with a fuzzy control module, and the method uses the fuzzy control module to control the power output distribution of the range extender and the battery in real time according to the SOC of the battery and the required power of the bus. The method specifically includes the following steps:
[0034] In step 401, various power system data (bus required power and battery SOC) required for energy management strategy calculation are sent to the VMS through the CAN bus.
[0035] In step 402, judge according to the SOC, if SOC
[0036] In step 403, keep the range extender off.
[0037] In step 404, the SOC of the battery and the required power of the bus are fuzzified according to the membership function in the fuzzy control module.
[0038] In step 405, fuzzy reasoning is performed according to fuzzy rules and membership functions.
[0039] The fuzzy rules and membership functions in the fuzzy control module are obtained through genetic algorithm optimization, and the goal of the optimization is to maximize the cruising range.
[0040] like figure 2 As shown, the optimization specifically includes the following steps;
[0041] a) Encode the fuzzy rules and membership functions, and generate an initial population according to the encoding length;
[0042] b) Transform the coding into fuzzy parameters, and substitute them into the established vehicle power system model to simulate the cruising range;
[0043] c) Set the fitness function to convert the optimization goal into fitness;
[0044] d) Calculate the fitness of each individual in the population;
[0045] e) judging whether the maximum mileage value converges, if so, then output the result, if not, then perform step f);
[0046] f) Perform selection, crossover, and mutation operations on the population to generate a new population, and return to step b).
[0047] The concrete steps of described fuzzy reasoning are:
[0048] a) According to the set fuzzy rules, the fuzzy expression of the corresponding output variable Pout is obtained from the fuzzy expression of the input variable battery SOC and the bus demand power Pr;
[0049] b) Corresponding to each fuzzy rule in step a), first get the value that is less than the set value ε in the degree of membership of SOC and Pr, and then carry out the small operation with the fuzzy function of the corresponding Pout;
[0050] c) Obtain multiple functions corresponding to multiple fuzzy rules from steps a) and b), and then take the maximum value of these functions as the function expression of the output variable.
[0051] The specific steps of using the center of gravity method to defuzzify the reasoning results are: according to the output function obtained by fuzzy reasoning, the area center of gravity of the output function is obtained, and the abscissa corresponding to the center of gravity is the fuzzy control module after defuzzification Output.
[0052] In step 406, defuzzification is performed using the center of gravity method;
[0053] In step 407, after the output value of the fuzzy control module is transformed, the power value that the range extender should output is obtained;
[0054]In step 408, the VMS sends the power distribution results to the controllers of the various components of the power system through the CAN bus, and completes the distribution of the output power of the power system by the VMS at the control end to each energy source of the power system.
[0055] like figure 2 As shown, the genetic algorithm is used to optimize the parameters of the fuzzy control module. The optimization objective is to maximize the cruising range, and the optimization variables are fuzzy functions and fuzzy rules.
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Description & Claims & Application Information

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Classification and recommendation of technical efficacy words

  • Adaptable
  • Good robustness

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