随着冷链物流的快速发展，燃油冷藏车碳排放日益加剧，但电动冷藏车由于续航里程限制，导致冷链配送时效性差从而影响企业发展。为了在节能减排的同时降低冷链物流企业配送成本，通过构建基于多能源车辆及充电、换电模式选择的冷链路径优化模型，研究多能源车的冷链配送问题和电动冷藏车的充电、换电优化问题，并提出一种改进的自适应大邻域搜索（modified adaptive large neighborhood search, MALNS）算法，设计相关破坏、修复算子来进行有效求解。实验结果表明：该模型不仅可以减少碳排放，还可节约配送成本，实现企业可持续发展；MALNS算法可以更高效率地搜索最优成本。研究成果对冷链物流企业配送具有重要指导意义。

With the rapid development of cold chain logistics, the carbon emissions from fuel-powered refrigerated vehicles are increasingly exacerbated. However, the poor timeliness of cold chain distribution is resulted from the range limit of electric refrigerated vehicles, thereby enterprise development is impacted. In order to reduce distribution costs for cold chain logistics enterprises while saving energy and reducing emissions, the problem of multi-energy vehicle-based cold chain distribution and optimal charging and changing modes for electric refrigerated vehicles were investigated by constructing a cold chain path optimization model which focused on selecting multi-energy vehicles and optimizing charging and changing methods. To effectively solve the model, a modified adaptive large neighborhood search(MALNS) algorithm was proposed along with relevant damage and repair operators. The experimental results demonstrate that the model not only effectively reduces carbon emissions, but also optimizes distribution costs, thereby facilitating the sustainable development of enterprises, and optimal costs can be searched efficiently by the MALNS algorithm. The research results hold significant guiding implications for cold chain logistics enterprise distribution.