新能源车辆补能站智能充电策略

（一）需求背景为促进新能源汽车产业发展，为新能源汽车用户提供更高效便捷的换电补能方式，解决用户出行问题，提升换电站运营效率，提供满足司机用户体验、换电站运营方运营利益需求及电池持有方等电池保值需求的数据服务方案。蓝谷智慧（北京）能源科技有限公司搭建了智能化换电运营决策系统，通过车辆的换电意愿预测模型与换电推荐策略、站端的服务需求预测模型与充电管理策略两种数据服务方式一定程度上满足了司机用户高效补能、消除里程焦虑，提高换电站运营收入，加强电池保值的需求 （二）解决方案换电站运营通过提供换电服务，峰平谷电价差价获取利润，因此换电站运营方通常通过调整充电峰平谷时段降低成本，但如只在谷时段充电成本虽可以下降，但无法满足车辆换电需求。此外还需考虑充电对电池的健康衰减，因此就形成了一个多方博弈的问题，需要一个系统解决方案。 针对现有业务痛点，蓝谷智慧（北京）能源科技有限公司（简称”蓝谷能源“）构建了智能化换电运营决策系统，通过大数据、AI技术及优化算法等数智化手段解决换电运营中面临的关键问题，提升运营效率与用户体验，满足业务中的多方诉求。智能化换电运营决策系统的核心思想为基于数据与AI算法构建多种预测模型与算法策略，主要包括车辆的换电意愿预测模型与换电推荐策略、站端的服务需求预测模型与充电管理策略。（1）车辆的换电意愿预测与换电推荐策略可实时掌握并AI分析车辆行为与车况、车辆实时位置等数据，进行换电服务需求预测，通过预测结果对司机进行车辆换电提示并推荐合理的换电服务地点，有效缩短服务半径，缓解司机里程焦虑。 （2）站端的服务需求预测与充电管理策略通过分析站端现有的电池数量、电池状态、电池充电Map图、电池健康状态、整站功率配额、当前电价等数据，实时获得未来1-2小时的换电需求量，调整电池的充电策略，通过对电价的削峰填谷与功率调控实现在可满足换电需求的前提下，最大化降低电池的充电成本，延缓电池衰减。智能化换电运营决策系统智能决策最直接的服务对象是包含出租车、网约车司机在内的车辆驾驶人、换电站运营商与电池资产持有方等，蓝谷能源技术团队完成了整套方案的设计与开发，实现从后端数据与算法模型开发到用户交互的全链路打通。在业务功能上司机可以通过手机APP实时掌握周边换电站分布与电池信息，解决里程焦虑问题；换电站运营方则可提前获取未来一段时间的服务需求量，结合电价情况与电池状态计算最优充电策略，节约成本。蓝谷能源算法平台以数据接口的方式为站端提供每块电池的充电策略数据，策略数据实时更新，在保证精细化的同时也兼具时效性。策略模型构建需要足够的数据支撑，其中主要包含换电站历史运营数据与基本信息，基于GBT32960的车辆数据，电池信息，电价信息，以及天气、路况等环境数据。对数据的处理与建模面临数据多样化、数据动态性高、数据关系复杂等诸多挑战。蓝谷能源以自建大数据平台为底座，融合多种数据源，依托于数据平台的数据处理能力完成对数据的集成与重构，构建了多维一体的数据架构模型，确保数据的完整性与可靠性，为算法模型发开提供了充分的数据与技术支撑。 （三）技术亮点在业务层面，智能决策的创新点主要体现在将车辆、场站、电池以及其他关联数据进行有效打通，并通过对业务流程全链路的拆解构建多个模型与策略最终形成一套让多方收益的决策方案。在技术层面，突破性的实现了大数据与AI技术在换电行业领域中的应用。在算法设计中使用了多模型融合与依赖的混合架构，比如换电意愿预测模型作为站端需求预测模型的输入特征，而站端需求预测模型同时使用了将LSTM与Prophet等多种时序模型融合的方案，最大限度满足结果数据的准确性。此外，基于蓝谷能源大数据平台的智能化决策系统具有很强的推广应用价值。首先，智能决策可应用于全国所有的乘用车及商用车换电站，甚至储能等能源场站。其次，智能决策的车辆数据可用于出租车公司或交通管理部门对车辆的实时监控与管理，换电站数据则可用于运营商对站端的运营管理，比如根据换电需求数据合理配置站内电池数量，有效降低固定资产的投放成本。 （四）应用成效1）经济效益方面：最直接的体现便是有效降低充电成本5%以上，在站端控制中减少对人工的依赖，提升换电站运营效率。2）可通过提升用户满意度增强用户粘性与忠诚度，从而达成订单量的提升，增加运营营收。3）对于出租车司机，可以更合理的规划车辆补能计划并以最小成本完成补能，有效提升了补能的合理性与经济型。4）对于电池资产持有者，可通过智能化决策系统选择最优充电管理方式，可延缓电池衰减，延长电池寿命，有效提升了资产利用率和保值率。 换电运营智能决策系统从以下多个方面体现了社会效益。1）首先，智能决策促进了行业生态圈中各方的利益共赢，对行业的良性发展起到了积极的推动作用。2）其次，出租车在公共交通领域扮演了重要角色，对出租车辆的服务提升可保证车辆本身的服务能力，这在一定程度上对城市的公共交通设施建设起到了积极作用。3）最后，智能决策中对电池充电安全保护的功能，极大的降低了电池安全隐患，对生命财产安全起到了保护，同时还避免了因安全问题而引起的社会影响

(1) Background of Requirements
To promote the development of the new energy vehicle (NEV) industry, provide more efficient and convenient battery swap energy replenishment solutions for NEV users, solve users' travel problems, improve the operational efficiency of battery swap stations, and deliver data service solutions that meet the experience demands of driver users, the operational benefit demands of battery swap station operators, and the battery value preservation demands of battery holders. Bluepark Wisdom (Beijing) Energy Technology Co., Ltd. has developed an intelligent battery swap operation decision-making system. Through two data service modes—vehicle battery swap willingness prediction model and swap recommendation strategy, and station-side service demand prediction model and charging management strategy—it has to a certain extent met the demands of driver users for efficient energy replenishment, elimination of range anxiety, improvement of battery swap station operating revenue, and enhancement of battery value preservation.

(2) Solutions
Battery swap stations generate profits by providing swap services and exploiting price differences between peak, flat and off-peak electricity tariffs. Therefore, station operators usually adjust charging periods during peak, flat and off-peak tariff windows to reduce costs. However, only charging during off-peak periods can lower costs but fails to meet vehicle swap demands. Additionally, the impact of charging on battery health degradation must be considered, forming a multi-party game problem that requires a systematic solution.
To address existing business pain points, Bluepark Wisdom (Beijing) Energy Technology Co., Ltd. (hereinafter referred to as "Bluepark Energy") has built an intelligent battery swap operation decision-making system. Using intelligent and digital means such as big data, AI technology and optimization algorithms, it solves key issues in battery swap operations, improves operational efficiency and user experience, and meets the multi-stakeholder demands of the business. The core idea of the intelligent battery swap operation decision-making system is to construct multiple prediction models and algorithm strategies based on data and AI algorithms, mainly including the vehicle battery swap willingness prediction model and swap recommendation strategy, as well as the station-side service demand prediction model and charging management strategy.
(1) Vehicle battery swap willingness prediction and swap recommendation strategy: This solution can collect and AI-analyze data such as vehicle behavior, vehicle condition and real-time location in real time, predict battery swap service demands, remind drivers to swap batteries based on prediction results and recommend reasonable swap service locations, effectively shortening service radius and alleviating drivers' range anxiety.
(2) Station-side service demand prediction and charging management strategy: By analyzing data such as the current number of batteries, battery status, battery charging map, battery health status, station-wide power quota and current electricity price at the station, it can obtain the battery swap demand in the next 1-2 hours in real time, adjust battery charging strategies, and maximize the reduction of battery charging costs while meeting swap demands through peak shaving and valley filling of electricity prices and power regulation, thereby delaying battery degradation.
The direct service objects of the intelligent decision-making of the battery swap operation decision-making system include vehicle drivers (such as taxi and ride-hailing drivers), battery swap station operators and battery asset holders. The Bluepark Energy technical team has completed the design and development of the entire solution, realizing full-link connectivity from back-end data and algorithm model development to user interaction. In terms of business functions, drivers can master the distribution of nearby battery swap stations and battery information in real time through mobile APPs, solving range anxiety problems; station operators can obtain service demands in advance for a period of time, calculate the optimal charging strategy combined with electricity price conditions and battery status, and save costs. Bluepark Energy's algorithm platform provides charging strategy data for each battery at the station via data interfaces, with real-time updated strategy data, ensuring both refinement and timeliness.
The construction of strategy models requires sufficient data support, mainly including historical operation data and basic information of battery swap stations, vehicle data based on GBT32960, battery information, electricity price information, and environmental data such as weather and road conditions. Data processing and modeling face many challenges such as data diversity, high data dynamics and complex data relationships. Bluepark Energy takes its self-built big data platform as the foundation, integrates multiple data sources, completes data integration and reconstruction relying on the data processing capabilities of the data platform, and constructs a multi-dimensional integrated data architecture model to ensure data integrity and reliability, providing sufficient data and technical support for algorithm model development.

(3) Technical Highlights
From the business perspective, the innovation of intelligent decision-making is mainly reflected in the effective connectivity of vehicle, station, battery and other related data, and the disassembly of the entire business process to construct multiple models and strategies, ultimately forming a decision-making solution that benefits multiple parties. From the technical perspective, it has achieved a breakthrough application of big data and AI technology in the battery swap industry. The algorithm design adopts a hybrid architecture of multi-model fusion and interdependence. For example, the battery swap willingness prediction model is used as an input feature for the station-side demand prediction model, and the station-side demand prediction model simultaneously uses a solution that fuses multiple time-series models such as LSTM and Prophet, maximizing the accuracy of the result data. In addition, the intelligent decision-making system based on Bluepark Energy's big data platform has strong popularization and application value. First, the intelligent decision-making can be applied to all passenger vehicle and commercial vehicle battery swap stations across the country, and even energy stations such as energy storage. Second, the vehicle data from the intelligent decision-making can be used by taxi companies or traffic management departments for real-time vehicle monitoring and management, while battery swap station data can be used by operators for station-side operation management, such as reasonably configuring the number of on-site batteries based on swap demand data, effectively reducing fixed asset investment costs.

(4) Application Effects
1) Economic Benefits: The most direct manifestation is effectively reducing charging costs by more than 5%, reducing reliance on manual labor in station-side control and improving the operational efficiency of battery swap stations.
2) Improving user satisfaction can enhance user stickiness and loyalty, thereby increasing order volume and operating revenue.
3) For taxi drivers, they can more reasonably plan vehicle energy replenishment plans and complete replenishment at the minimum cost, effectively improving the rationality and economy of energy replenishment.
4) For battery asset holders, the intelligent decision-making system can be used to select the optimal charging management method, delaying battery degradation and extending battery life, effectively improving asset utilization rate and value retention rate.
The intelligent battery swap operation decision-making system reflects social benefits in the following aspects:
1) First, the intelligent decision-making promotes win-win interests of all parties in the industry ecosystem, playing a positive role in promoting the sound development of the industry.
2) Second, taxis play an important role in the public transportation field. Improving the service quality of taxis can ensure their service capabilities, which to a certain extent contributes to the construction of urban public transportation facilities.
3) Finally, the battery charging safety protection function in the intelligent decision-making greatly reduces battery safety hazards, protecting life and property safety, and also avoiding social impacts caused by safety issues.

搜集汇总

背景与挑战

背景概述

该数据集由蓝谷能源开发，通过车辆换电意愿预测和站端需求管理模型，利用大数据和AI技术优化充电策略，降低5%以上充电成本并延长电池寿命。方案覆盖司机、运营商和电池持有方需求，兼具经济效益（提升运营收入）和社会效益（促进行业生态共赢）。