Battery Charging Time Estimation Using Iterative SOC and Temperature Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for estimating remaining charging time in power batteries are inaccurate, especially at low temperatures or low State of Charge (SOC), leading to significant errors due to the lack of consideration for temperature changes during the charging process.
Innovation Solution
A method that acquires initial state of charge and temperature values, determines charging current and temperature change rates, and iteratively updates estimation sections to accurately calculate remaining charging time by considering both state of charge and temperature changes, using lookup tables for correspondence between charging currents, temperatures, and temperature change rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional ampere-hour integration method is used for estimating remaining charging time, then the estimation process is simple, but the accuracy deteriorates significantly at low temperatures or low SOC
Solution Approach 1:
The patent segments the charging process into multiple stages based on SOC intervals and temperature ranges. Different estimation models are applied to different segments, with each segment having optimized parameters. This allows the system to handle the complexity of temperature and SOC variations without requiring a single overly complex model, thereby maintaining reasonable system complexity while significantly improving estimation accuracy across different operating conditions.
Solution Approach 2:
The patent introduces dynamic adjustment of estimation parameters based on real-time temperature and SOC measurements. The estimation model dynamically adapts to changing battery conditions by adjusting key parameters such as charging efficiency coefficients and temperature compensation factors. This dynamic approach allows the system to maintain high accuracy throughout the charging process without requiring constant model restructuring.
2Device complexity
If temperature influence is not considered in charging time estimation, then the estimation model is simple, but large errors occur at low temperatures
Solution Approach 1:
The patent changes the parameters of the estimation model based on temperature conditions. Different temperature ranges correspond to different sets of model parameters, including charging efficiency coefficients, internal resistance values, and thermal compensation factors. This parameter adaptation allows the model to accurately reflect battery behavior at various temperatures without requiring a completely different estimation approach for each temperature condition.
Solution Approach 2:
The patent introduces temperature compensation factors as intermediary variables that mediate between the basic ampere-hour integration method and the final accurate estimation. These compensation factors, derived from temperature measurements and pre-characterized battery data, adjust the raw integration results to account for temperature effects. This intermediary approach adds minimal complexity while effectively correcting temperature-induced estimation errors.
3Measurement precision
If iterative updates of state of charge and temperature estimation sections are performed, then the estimation accuracy is improved, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary characterization of battery behavior across the full range of operating conditions before actual charging estimation. Pre-computed lookup tables and pre-determined parameter sets for different temperature and SOC ranges are prepared in advance. During real-time estimation, the system only needs to query these pre-prepared data structures and perform simple interpolations, rather than performing complex calculations from scratch. This preliminary action significantly reduces online computational complexity while maintaining high accuracy.
Solution Approach 2:
The patent implements iterative updates of estimation sections at strategically selected intervals rather than continuously. By performing updates only when crossing predefined SOC or temperature thresholds, the system achieves sufficient accuracy without the excessive computational burden of continuous iterative refinement. This partial action approach balances accuracy requirements with computational efficiency.
Data Source
Figure 1~2
Figure 3
AI summary
The present disclosure discloses a method, a device, a system, and a storage medium for estimating remaining charging time. The method includes: determining a charging requested current value and a battery temperature change rate; estimating a first estimated time required for the battery to be charged from the state of charge estimation initial value to an upper limit of the state of charge estimation section; estimating a second estimated time required for the battery to be changed in temperature from the temperature estimation initial value to an upper limit of the temperature estimation section; determining a new state of charge estimation initial value, a new state of charge estimation section, and a new temperature estimation initial value and a new temperature estimation section based on a smaller estimated time between the first estimated time and the second estimated time, until an upper limit of an state of charge estimation section reaches a target state of charge and a smaller estimated time is a first estimated time; and accumulating every determined smaller estimated times to obtain an estimated remaining charging time for the battery to be charged to the target state of charge. According to the method in embodiments of the present disclosure, accuracy of estimation of remaining charging time can be improved.