Electric vehicle remaining mileage estimation algorithm and electric vehicle using same

Abstract

The invention provides an electric vehicle remaining mileage estimation algorithm and an electric vehicle using the algorithm, under the condition that hardware of the electric vehicle is not changed, the remaining mileage is estimated through weighted average real energy consumption per kilometer in the historical mileage of the vehicle and battery remaining energy, and the prediction value is adjusted in time according to the operation state so as to estimate the remaining mileage more accurately; meanwhile, when the battery is low in electric quantity, the power of the electric drive system is limited and optimized, the residual energy of the battery can be fully released, the situation that power output of the vehicle is cut off when the residual electric quantity is not used up and the instrument does not display the residual mileage of 0 kilometer is avoided, and the driving experience of a user is guaranteed.

Description

technical field [0001] The invention relates to the technical field of electric vehicles, in particular to an estimation algorithm for the remaining mileage of an electric vehicle and an electric vehicle using the same. Background technique [0002] The remaining mileage refers to the mileage that the electric vehicle can travel when the current power state of the power battery of the electric vehicle runs to the cut-off voltage of the battery. The remaining mileage can provide a reference for car owners to make travel plans, avoid energy shortages during vehicle driving, and play an important role in improving user experience and reducing mileage anxiety. [0003] Existing light electric vehicles generally do not provide remaining mileage information to users, but only provide battery SOC (State of Charge, state of charge) information; or directly estimate remaining mileage based on SOC, and the estimation expression is as follows: [0004] RM=K 1 *SOC, 0<SOC<SOC 拐...

AI Technical Summary

Problems solved by technology

[0009] (1) The method of only providing SOC information makes it impossible for users to judge the remaining mileage, especially for users who are new to the vehicle;

[0010] (2) Based on the SOC information, the remaining mileage is estimated piecewise linearly. No matter from the theoretical point of view or the experimental point of view, the relationship between the cruising range and SOC is not purely linear, and the energy usage is affected by road conditions, traffic conditions, environmental factors, system Efficiency, vehicle weight and load, tire pressure, etc., the energy supply end is affected by factors such as battery type, battery rate, battery internal resistance, battery temperature, battery SOH (State of Health, battery health status), the superposition of the above factors may affect Estimates of remaining range based on this approach yield very large biases;

[0011] (3) The use of algorithms such as Kalman filtering requires strong hardware computing power. Many current hardware configurations cannot support such a large computing power requirement and need to be upgraded, which will bring compatibility problems and increase costs;

[0012] (4) Regardless of the scheme, the impact of the vehicle's operating power on the remaining outputtable energy of the battery is not considered

When the battery power is low, if the user applies a large accelerator, the drive system of the vehicle will draw power from the battery at a large discharge rate, causing the battery voltage to drop to the threshold of low-voltage protection prematurely. Cut off the power output when there is a lot of power and there is still a lot of remaining mileage to drive, resulting in a bad driving experience

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