电池荷电状态预测数据

电池荷电状态（SOC）预测数据通过对电动车电池在充放电过程中的电压、电流、温度等关键参数的实时监测，提供了对电池性能的深入分析。利用这些数据，可以实现以下应用：通过电压和电流数据，精确计算剩余电量，优化骑行里程预测。利用累计循环次数和温度数据，优化换电站的电池管理和调度。根据累计容量和电流积分，评估电池寿命，提供更优质的租赁方案。实时显示电池状态，包括电压、温度和SOC，提升用户体验。通过SOC的精确计算，提高电池管理的智能化水平。根据历史电流和温度数据，预测电池故障风险，进行预防性维护。这些数据的应用将提高用户满意度，并为平台的运营决策、安全管理和可持续发展提供重要支持。随着大数据分析和人工智能技术的进步，SOC预测数据的应用潜力将进一步释放，推动二轮电动车行业向更智能、高效和环保的方向发展。1.通过公司BAAS平台收集锂电池的运行数据，包括累计循环次数、时间、电流(A)、电池温度(℃)、初始SOC等数据。2.数据处理：1)时刻荷电状态（SOC_t），计算公式为SOC_t = SOC_0 + (∫I dt) / C_n，SOC_0 是初始 SOC，∫I dt)是电流积分，C_n 是额定容量；2）补偿荷电状态（SOC_comp），计算公式为SOC_comp = SOC_0 * (1 + k * (T - T_ref))，其中，k 是温度系数，T 是当前温度，T_ref 是参考温度；3）最终荷电状态（SOC_final），计算公式为SOC_final= w1 * SOC_t + w2 * SOC_comp，其中，w1, w2是权重系数，通过支持向量回归算法调整。

Battery State of Charge (SOC) prediction data enables in-depth analysis of battery performance by real-time monitoring of key parameters including voltage, current, and temperature during the charging and discharging cycles of electric vehicle batteries.

Leveraging this data, multiple applications can be implemented:
1. Accurately calculate the remaining battery capacity and optimize riding range prediction based on voltage and current data;
2. Optimize battery management and scheduling of battery swap stations using cumulative cycle counts and temperature data;
3. Evaluate battery lifespan based on cumulative capacity and current integration, and deliver higher-quality rental solutions;
4. Display real-time battery status including voltage, temperature and SOC to enhance user experience;
5. Improve the intelligence level of battery management through accurate SOC calculation;
6. Predict battery fault risks and conduct preventive maintenance based on historical current and temperature data.

The application of this data will boost user satisfaction, and provide critical support for the platform's operational decision-making, safety management and sustainable development. With the advancement of big data analysis and artificial intelligence technologies, the application potential of SOC prediction data will be further unleashed, driving the two-wheeled electric vehicle industry towards a more intelligent, efficient and environmentally friendly development path.

1. Lithium battery operating data is collected via the company's BAAS platform, including cumulative cycle times, timestamps, current (A), battery temperature (℃), initial SOC and other related parameters;
2. Data processing steps:
1) Instantaneous State of Charge (SOC_t): The calculation formula is $SOC_t = SOC_0 + frac{int I dt}{C_n}$, where $SOC_0$ is the initial SOC, $int I dt$ is the current integral, and $C_n$ is the rated capacity;
2) Compensated State of Charge (SOC_comp): The calculation formula is $SOC_{comp} = SOC_0 imes (1 + k imes (T - T_{ref}))$, where $k$ is the temperature coefficient, $T$ is the current battery temperature, and $T_{ref}$ is the reference temperature;
3) Final State of Charge (SOC_final): The calculation formula is $SOC_{final} = w_1 imes SOC_t + w_2 imes SOC_{comp}$, where $w_1$ and $w_2$ are weight coefficients adjusted via the support vector regression algorithm.

搜集汇总

数据集介绍

特点

该数据集包含68429条电池荷电状态预测数据，涵盖电压、电流、温度等关键参数，用于电动车电池性能分析和SOC预测。数据通过特定算法计算SOC，支持电池管理、寿命评估和故障预测等应用场景。